This week was completely overrun with two homework projects that refused to surrender even when groups of Sloans & MBAs spent hours sitting in the room and cranking through logistic regressions and ROI analysis for marketing channels of a pharma company, or trying to calculate the optimal inventory cost of the supply chain of HP injet printer factory. Also, a real dataset of 90,000 users of mobile phone users came in, which will be a basis for one of our final projects on understanding churn – once we chew ourselves through it.

And in the Town Hall with Madhav Rajan, Associate Dean of Academics we learned that the incoming MSx 2014 class will have quite a few improvements to their class schedule, with even more room for electives in Winter & Spring – congrats! Make sure you’ll fill those slots with quant analysis under the California sun this time next year.

Covered in this issue:

• Milgram’s Small world problem and its modern developments
• Recency/Frequency/Monetary Value & Churn analysis
• Measuring sales force performance and forecasting tricks (and a video sampler for a litmus test for if you should be in sales)
• Organizational blueprints for startups
• Guests from: Google, VMWare, Progreso, Ariat

OB322: Networks (Hasan)

Article: The Small-World Problem (Milgram’s original text from Psychology Today in 1967)

• the Small-world experiment article on wikipedia gives a nice quick summary (and animated maps!)
• If you had completely random connections between people, you would get to anyone else on the planet in much shorter path than “six degrees of separation”. In a continuum of “high clustering” (long paths) -> “completely random” (shortest paths), just 1% of connections have to be random to achieve a small world.
  • Same pattern: WWW, e.coli, US power grid…
• A lot of modern research on small world problems including Random Rewiring above: Duncan Watts (Microsoft Research). 20k+ citations on his papers.
• When choosing someone to pass off a message to someone outside your network, best strategies look for (based on CS research):
  • demographic proximity
  • high degrees (in/out)
  • high betweenness
• In real human networks, demographic beats others (in HP Labs, passing a message by hierarchy: 4 hops, by office location: 6 hops, via the most connected: 16 hops – started bouncing between biggest brokers)
• Preferential attachment process - in which some quantity, typically some form of wealth or credit, is distributed among a number of individuals or objects according to how much they already have, so that those who are already wealthy receive more than those who are not.
  • In case of social networks: if there are three interconnected nodes, an incoming node connects to this cluster thorough one of them by random – say to node C. But after this connection, it is just a little bit more likely that another incoming connection is done to C (as opposed to A or B), because .. it looks more connected.

Guest: Mark Rivera (People Analytics, Google)
Topic: Science of HR at Google

• notes remain private by request of the speaker
• Minimal group paradigm research (for Day 1 employee experience)

MKTG 365: Marketing Analytics (Narayanan)

• RFM analysis is the oldest database marketing technique, looks at Recency, Frequency and Monetary Value of individual past customers to make targeting decisions – and assigns new purchase / response probability to each bucket.
• Makes use of powerful metrics of customer behaviour that are contained in most house files (easy to access). Also easy to understand and communicate – does not require training in statistics (Excel and averaging enough)
• Recency (days since purchase) curve against new purchase probability can have different shapes (decreasing, hyperbolic, cyclical), but relationship is clear. The curve shape usually doesn’t matter, because the groups will be stack ranked based on their probability (not actual recency)
• In any case RFM analysis requires a lot of data. In case of independent analysis, without enough data, the size of groups will be very different. With sequential ordering (first R, then F inside that, etc)  the groups are always the same size, but without enough data a few outliers can notably mislead your results – and RFM 111 is not necessarily responding better than 211 (intuition breaks).

• RFM analysis difficult to implement
  • for finer segmentation than quintiles – even deciles problematic (30,000 observations: in quintiles, 125 cells, 250 observations per cell. Same data in deciles: 1000 cells, only 30 obs. per cell!)
  • to add additional explanatory variables (demographics, etc)
• As a response to limitations, use logistic regressions, decision trees or neural networks - but RFM still provides the intuition behind the more complicated statistical models

Topic: Churn Analysis

• Sun spot activity and stock market - a well known case of overfitting (just 100 years of data for both)
• Characteristics of logistic regressions
  • Pseudo R^2 is even in best cases significantly lower than the R^2 of linear regressions
  • the coefficients for independent variables don’t have a direct interpretation as with linear regressions (elasticity, %% impact, etc).
  • Coefficients show the change of abstract “utility units” (utils) – so the absolute value doesn’t say much, but the change in it (“how does the probability of churn change when revenue changes by 1%”) – you can calculate the elasticity.

. logit churndep revenue-retcall

Iteration 0:   log likelihood = -26991.574

Iteration 1:   log likelihood = -26166.913

Iteration 2:   log likelihood = -26150.176

Iteration 3:   log likelihood = -26150.169

Iteration 4:   log likelihood = -26150.169

Logistic regression                               Number of obs   =      38941

                                                  LR chi2(66)     =    1682.81

                                                  Prob > chi2     =     0.0000

Log likelihood = -26150.169                       Pseudo R2       =     0.0312

------------------------------------------------------------------------------

    churndep |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

     revenue |   .0019635   .0007981     2.46   0.014     .0003992    .0035278

         mou |  -.0002809   .0000496    -5.66   0.000    -.0003782   -.0001836

    recchrge |  -.0031225   .0008888    -3.51   0.000    -.0048645   -.0013806

…………….*snip* ……..

     setprcm |  -.0963204   .0405136    -2.38   0.017    -.1757257   -.0169152

      setprc |   .0006204   .0002827     2.19   0.028     .0000662    .0011745

     retcall |   .7936977   .1945818     4.08   0.000     .4123244    1.175071

       _cons |   .1497005   .0952738     1.57   0.116    -.0370327    .3364336

------------------------------------------------------------------------------

Note: 0 failures and 1 success completely determined.

. margins, dydx(revenue)

Average marginal effects                          Number of obs   =      38941

Model VCE    : OIM

Expression   : Pr(churndep), predict()

dy/dx w.r.t. : revenue

------------------------------------------------------------------------------

             |            Delta-method

             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

     revenue |   .0004702   .0001911     2.46   0.014     .0000957    .0008447

------------------------------------------------------------------------------

. margins, eyex(revenue)

Average marginal effects                          Number of obs   =      38941

Model VCE    : OIM

Expression   : Pr(churndep), predict()

ey/ex w.r.t. : revenue

------------------------------------------------------------------------------

             |            Delta-method

             |      ey/ex   Std. Err.      z    P>|z|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

     revenue |   .0582378   .0236117     2.47   0.014     .0119597    .1045159

------------------------------------------------------------------------------

• Interpretation
  • marginal effect (margins, dydx): for people whose revenue goes up by $100, their probability(churn) goes up by 100*.0004702=4.7% (4.7 of additional people of every next 100 churn)
  • elasticity (magins, eyex): for 1% change in revenue, the churn increases compared to current level by 0.05%
• Stepwise regression: runs a regression on all variables, orders by p-value, drops the highest (least significant) and reiterates until only significant variables remain.
  • Stata: stepwise logit churndep revenue-retcall, pr(0.05)
• Finding exact churn predictions is hard, more reasonable relative questions to ask: “which users are more than 75% more likely to churn than average”. Generate a lift variable = predicted_churn / average_churn. Then filter by lift>1.75

OIT 262: Operations (Plambeck)

Topic: Suppy Chain Inventory

• Point estimates on expected demand are of little use, you need estimation on their accuracy too (is the estimate of 2000 distributed with std deviation of 100 or 700?)
• Demand forecasting and calculating demand forecast errors

Topic: HP DeskJet

• Delay the customisation/localizations of products in the cycle as long as you can

STRAMGT 351: Building and Managing Professional Sales Orgs (Lattin/Levine)

Reading: What can we really manage? (Ch 2)
Book: Cracking the Sales Management Code: The Secrets to Measuring and Managing Sales Performance

• In the argument around if sales is more of art or science, superstar sellers seem to prove the tilt towards creative arts. In case of sale management, the high performers are for sure more scientific (analysis, not gut; rigorous goal setting, tracking; formal meetings, formal agendas). Consider if your top sales guys are really the best material for promotion to managerial roles?
• Risk of the “make your numbers” mantra: people can still only manage people and resources, not concepts or numbers. Can you really manage your kids’ grades, or can you manage their behaviour and create an environment where they are more likely to improve the grades?
• Beware of the death by reporting:
  • $ Profit, $ Revenue, % Share of Market, Customer Satisfaction, Unit Volume, % Sales calls advancing, % Customers contacted, Day Ramp-up Time, % New Product Sales, % Customer Retention, % Share of Wallet, # New Customers, % CRM usage, % Account Plan Completion, # Prospecting Calls, # Accounts per Rep, $ Spent on Training, # Reps per Manager…?
  • No real industry standard or best practices, even inside a single niche
  • A survey surfaced 306 different metrics used by sales organisations. A sanity check of each one with sales managers across companies:
    • 17% of those deemed highly manageable, all related to sales activities and activity metrics.
    • 80%+ of metrics used can only be indirectly influenced by managers.
    • The latter, indirect category is in practice split 1/3 business results (revenue, CSAT) and 2/3 sales objectives (share of wallet, customer retention…).
• Reverse-engineered chain of Sales Metrics: To achieve “3% increase of revenues” (business result) -> drive towards “4 new customers per quarter” (sales objective) -> by managing “16 more prospecting calls per quarter” (sales activity).

Blog post: Building a Culture That Works by Peter Levine

• In trusting people, engage them in your thought process:
  • Don’t just share a decision, but the pros/cons discussed and path why we came out on the side we did
  • put yourself in a teaching position for your org (behave like your favourite professor)

Topic: Forecasting
Guest: Bryan Cox (VP of Global Support Renewals, VMWare)

• Common issues with forecasting
  • Asymmetric incentives (quota always more important). Forecasting should be at least “marker for evaluation”
  • Asking for “blood commit” numbers first anchors the forecasts lower
  • Sandbagging is safer than shooting over
  • Anchoring to quota: it is some arbitrary number given from above
  • No explicit and regular feedback to sales reps on their forecasting performance
  • Easier to be a good forecaster when you’re crappy at sales (more time, lower numbers, less absolute variance)
  • Stops at “a $ number”, no actionable or helpful conclusions on what the managers and sales people should do knowing the number, where to allocate the resources
  • Data is sliced by reps and managers, not pooled together for comparative views (no sales operations, analytical people crunching numbers)
  • Running scientific experiments on employees to get reliable performance data can be unfair (and borderline creepy)
• Hermann Brain Dominance Instrument - for sales team evaluation
  • Great sales people: D. Imaginative thinking: Visual, holistic, intuitive, innovative, and conceptual.
  • Great forecasters: A. Analytical thinking: Auditive,logical, factual, critical, technical and quantitative.
• To driver commitment and accountability: do it in person & among peer group
• Try also to track “upside”: deals not forecastable yet, but potential to move up
• More risk in paying money for forecasting performance (tension with actual sales achievement), but a great basis for promotion. Sales performance given for all, but best forecasters understand the business, can teach others they methods…
• The more linearity you can impose on a hockey stick process (like sales in quarter) – the less surprises you get
• “Sale postponed is sale lost” – reason not to strongly incentivise forecasts (and exceeding them)

Movie: Glengarry Glenn Ross

• Litmus test if you should be in sales or not. Great sales people love this movie. If you get uncomfortable: wrong job.

Case: Progreso Financial
Guest: Sam Ulloa (VP Sales, Progreso)

• Legal requirement: if you have employees physically in some location every day and you tell them ahead what they need to do – you need to pay them a base salary (not just incentives on sales)
• Sanity check your incentive model: after setting all steppings and levels the intended 100% payout (base + incentives) require the unit sales you need? Can get out of sync: full payout at 40 contracts, if your business plan needs 50 per person…
• When building an early, “hunter” salesforce, watch out for when to augment it with customer support. Do not waste hunter’s energy and attention and get better service for existing clients from people specifically incentivised to provide that (not sell)
• When not ready to start a company, don’t go to banking or consulting to learn more – go to sales. As and entrepreneur you will always be selling your vision and product – to first employees, investors, clients…

STRAMGT353: Formation of New Ventures (Holloway/Morgridge/Chess)

Article: Organizational Blueprints for Success in High-Tech Startups by Baron & Hannan (Stanford 2002)

• From Stanford Project on Emerging Companies (SPEC) - also referenced from Noam Wasserman’s Founders Dilemmas (see the reading notes post here)
• Dimensions of employment blueprints across 200 startups viewed (thorough the dot com boom):
  • Basis of attachment & retention: compensation (“money”), qualities of work (“work”), work group as community (“love”)
  • Criterion for selection: skills, exceptional talent/potential, fit with team/organization
  • Means of Control & Coordination: direct monitoring, peer and/or cultural, reliance on professional standards, formal processes & procedures
• Across 3 dimensions, there are 36 possible combinations – in reality 5 patterns emerge. Founder quotes:
  • Star: “we recruit only top talent, pay top wages & give them resources and autonomy they need to do their job” (Work/Potential/Professional from above dimensions)
  • Commitment: “I wanted to build the kind of company where people would only leave when they retire” (Love/Fit/Cultural)
  • Bureaucracy: “We make sure things are documented, have job descriptions and pretty rigorous project management techniques” (Work/Skills/Formal)
  • Engineering: “We have a  skunk-works mentality and the binging energy is very high” (Work/Skills/Cultural)
  • Autocracy: “You work, you get paid” (Money/Skills/Direct)
• Replacing founder CEO changed blueprints in 3/4 cases, in 40% of cases – by two or more dimensions.
• Engineering & Bureaucracy models are the closest (only differ on means of control), and also there is the most notable shift towards latter when non-founder CEOs take over.
• Blueprints strongly correlated with actual admin overhead in organization, in 50, 500, 1500 people size alike: in Commtiment based models, share of managers and administrators of total staff was 11-17%, in Autocracy & Bureaucracy models, up to 37%!
• Commitment based models most likely to go public and Star-based least likely. Yet, Star-based organisations grow post-IPO market cap 80% faster than next best.
• Compared to Engineering model based company, Autocratic is 130% more likely to fail and all other less likely to fail (Commitmend-based -100%)
• Beware of the strong focus on “fitting in” in Commitment model. Notably limits diversity (gender, viewpoints..) becoming natural for the culture.

Case: Cisco
“Guest”: Prof Morgridge (as ex-CEO of Cisco)

• Why start a company at a university?
  • Basic research
  • Young minds
  • Free resources/facilities
  • Latest Tools & Equipment
  • Answers are one department away
  • Risk/Failure tolerant
  • Low cost (no mortgages yet)
  • Long runway
  • Beta test audience who cares
• What should a new CEO focus on in first 90 days when talking to people? Not just isolated view (their skills, their views, their obstacles), but a network view (who do they look up to, who do they think are top performers/potential)
• Non-founding CEO should remember that investors invested in the founder, not him/her
• “You can always change the culture. You just need three bad quarters.” (Cisco was built on Dr Pepper. Started charging for soft drinks after 2008)

BOOK: Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers

Case: Ariat
Guest: Beth Cross (Founder & CEO)

• #1 horse riding boot brand in a world (after entering the “old boys club” from zero in early 90s)
• In consumer space, great to work outside designers to get fresh perspectives
• With a new & differently positioned product, you have to differentiate with the margin you’re leaving to the retailer to get in the door (50%, when others do 20%). Presumes premium pricing & positioning.
• First year sold 1000 pairs and “seeded” (gave away to clerks for their own use) at least 500. The clerk stands between you and the sale, he/she needs to be a believer.
• Visualize your company as a path over time, not just today or not the end point. One way: map out products, channels, marketing, geographies, capabilities, customers, technology as concentric circles and show in which order you would like to expand coming from inside.

—-

For more posts on the Stanford GSB Sloan life – see the table of contents here.

Somehow the notes from this week are lighter – and not for the lack of activities, but rather a different kind. Somehow felt pointless to try to capture heated in-class ethics discussions or hours spent on preparing group work presentations in bullet point form. Instead, I took some time to dump a new load of photos of school life to the Flickr feed, where I had dropped the ball some time back in February.

The biggest project effort went into a “consulting gig” for the Networking class. Given a network relations dataset from a 200 person law firm (across their work, non-work and friendship social relations), their demographic metadata (tenure, education, roles, age…) and some qualitative survey results, we had to build an action plan that would solve for some of their identified personell issues, such as high churn of women. A very interesting mix of social network visualisations and multiple regression approaches, with potential to take far more attention and time than due for “1 homework of 4″.

As an amusing side product we predicted a single associate to become partner next, which the professor confirmed to have actually happened in the last 2 years since the data was gathered. As a teaser, find person #2910 on this picture to see why that is (partners are red, associates blue):

On BBLs front, GSB had a group of StartX companies drop by. Without going into the detail of the pitches, the overall quality of the batch of this accelerator program seemed quite impressive ranked agains many I’ve seen. Which is well done, given the affiliation to a single university and their somewhat less-commercial structure (for example they take no equity stake in companies they incubate and even fund).

EDIT: the most regretted lecture that I unfortunately missed this week: Building computers from bacteriophage data, communication, logic within biological cells in EE380 Computer Systems Colloquium.

Covered in this issue:

• A data scientist’s take on how social graph analysis fits into the uncertain world
• Calculating the effect of ads and promotions on sales
• Tough life of a district manager and ethics of sales people
• Booting up a biotech business
• Some design thinking references and videos from IDEO
• Guest speakers from: Jive, NetApps, VMWare, Connectics, IDEO

OB322: Networks (Hasan)

Topic: Societal Resilience in an Uncertain World
Guest: Chris Diehl (first Data Scientist, Jive Software)

• Moved to social network analysis from CIA/NSA – after 9/11 the work on video surveillance didn’t seem like the right tool any more, refocused on solutions that don’t start with technology, but human networks
• Inside the biggest surprises of last decade (Arab spring, financial crises, …) the most common theme is the surprise about how quickly surprise events cascade
• Cyberdefence is a real issue because of the persistent asymmetry that is there by design. Have to protect yourself against attacks from anywhere. And the attacker only needs to succeed once.
• Limits of prediction: cascading uncertainty, limited observability, cognitive bias. Even if you have all the data in the world (remove middle limit), the other things kick in.
• This makes dynamics of persuasion still potentially more interesting.
  • When did you last change your mind because of an infographic? Reason to be sceptical if and when visualisation really works
  • Terrorism is effective in creating terror because of the “cognitive bomb” in launches in media
• When iterating through experiments, you want to be so fast that your competition considers the situation unpredictable
• Tacit knowledge: the collective knowledge that exists in every information that is close to impossible to be captured in any knowledge management system. Today the only viable mechanism: social query (call a friend who might know)
• Your willingness to take social risk increases over time when you start using a new social platform

MKTG 365: Marketing Analytics (Narayanan)

• By including the past Sales (t-1) as an independent term in a regression, you are accounting for all of the advertising effects of all past periods. But beware of other things like prices and promotions fogging the result.
• A simple rule of thumb for a good period size: if you have many periods with zero sales — you are probably too granular. (Ex: daily Tesla sales per dealership, vs monthly). On the other end – everyday consumer goods advertising effects typically wear off in a quarter.
• Three regression approaches to try (seeking for highest R^2 and statistical significance) – can have very different results on a small dataset:
  • including current and all lagged advertising periods individually
  • consolidating all current & lagged advertising periods in geometric progression into G-term
  • including only current advertising level and a sales from t-1 period
• Common mistake to stop at elasticity calculation for promotional planning – ROI could be higher for lower elasticity

Topic: Trade/Retail Promotions

• Two main channel members with their distinct marketing levers:
  • Manufacturer: advertising, consumer promotions
  • Retail/Trade: Termporary Price Reductions (TPR), Displays (Front of store, end of aisle, in aisle), Feature advertising (inserts & mailers); often in combination

STRAMGT 351: Building and Managing Professional Sales Orgs (Lattin/Levine)

Case: NetApps
Topic: Life of a district manager
Guest: Jim Wilson (Sales District Manager in the case)

• Performance Improvement Plans
  • overall culture needs to support coaching, training for these having any chance of credibility for good
  • stigma, label quickly becomes “to be fired” or “find a job in 90 days”
  • creates a paper trail for complex departures, HR thing first & foremost
  • CA is an “at will” state (for hiring/firing), but once you do 1 PIP, you will always have to do them, because of precedent that can be used in unfair treatment lawsuits
• “Little bit of slope makes up for a lot of Alpha”
• Good backbone of a conversation with a micromanaging boss: what am I held accountable for? If it is the quota, as agreed, are there any other constraints I am not aware about? Who can I hire/fire to make it happen, etc.
• Instead of raising quotas in the crisis of a reduced team size: pitch the opportunity to take the most out of existing compensation plan. More leads per person, work hard, exceed quotas, hit accelerators…
• You only get the straight scoop from people if you add value when you get bad news.
• You can look at sales rep motivation on scale from “don’t care about exact amount of money, but want to be clear #1″ VS “never shine a spotlight on me, but  make me rich”. Most are somewhere in between, of course.
• “Best of best” principle. Best reps on the best leads (which might not be the largest accounts, but ones with most potential).
• 3-5% buffer in quotas for someone falling sick, a rep leaving… Keeping it a secret, but everyone knows.
• Joining sales calls is a good use of DM time: 2nd set of ears, observes reactions while everyone is talking, coaching reps…

Topic: Ethics in Sales
Case: Lundberg Systems
Article: Ethical Issues in Personal Selling and Sales Force Management by Robert J. Kopp

• Sales people are a risk group when it comes to business ethics: organisational pressure to “meet the numbers”, boundary position from organisation perspective, autonomous operation and little oversight, advocacy role (selling/advertising being inherently unethical as argued by some ethicists)

STRAMGT353: Formation of New Ventures (Holloway/Morgridge/Chess)

Case: VMWare
Guest: Diane Greene (CEO/co-founder of VMWare; board member of Google, Intuit, MIT)

• Never outsource a reference check. Half on hour of CEO time per reference of an exec hire is time well spent.
• Spouses play a good role in dinners before hiring – second observer, second opinion. Goes both ways – for the hiring party, but also someone who is checking out their future boss.
• Climate is now better for early startup hiring: not unrealistic to hire a CFO who can take a company through IPO very early on. Still, makes sense to hire VP Marketing, VP Sales only once the product is real.

Case: Connetics & Relaxin
Topic: Biotechnology
Guest: Tom Wiggans (ex-CEO, Connectics)

• Other long-cycle, capital extensive business models which can have parallels with biotech: materials, energy, clean tech, automotive, aerospace, infrastructure, biofuels, semiconductors.
  • Still there are differences between them too, for example a lot of inertia and long market cycles in pharma (once a safe and effective drug is out, generates profits 10-20 years), in some other capex intensive businesses you are still pressured to renew products despite of huge capex (Intel needing to refresh processors along Moore’s law)
• Drug Development Funnel (from a good collection of pharma info graphics)

• Royalties depend on when the biotech licences their drug out:
  • Phase I: 5-10% royalties on net sales
  • Phase II: 10-15%
  • Phase III: 20-30%
  • FDA approval but no demonstrated market success: 35-50%
  • Building out own sales & marketing: 100%
• Hard to raise venture money for strategies focused on earlier phases (less risk, but much less upside – still a long cycle and partner time needs).
• Currently takes about $1.8B to get a new drug to the market. 60% of drugs marketed by pharma companies originate from smaller biotech companies. Academic roots in core science at universities, but usually turned into a biotech company for drug development.
• You often think, “let’s hire a CEO to figure out the strategy,” whereas you make an implicit strategy decision by which CEO you hire (an engineer, marketer or sales guy by background?)
• Long product cycles change how you hire people: for a marathon, not a 6 months sprint to first launch of software startups. How do you keep the team cohesive? How do you motivate without the lack of intermediate market feedback (or successes at all)?

Article: Note On The Biopharmaceutical Industry

Company visit

Hosted by: Chris Flink, Partner, IDEO Palo Alto

• Chris is a Sloan Fellow and co-founder of Stanford d.school
• Defining egalitarian culture: everyone thinks their discipline is the “cool” one, yet secretly admires some other one deeply
• Output agnostic innovation: you force yourself not to start from the tools of your discipline (e.g software guys always see solution in apps, not clever mechanics)
• Design is about constraints. The best designers relish constraint, creativity is shown in overcoming constraints and managing trade-offs
• Artists, in comparison, love the blank canvas where they can dump whats inside them (pure self expression)
• Design makes strategy better understood – looking at a great tactical execution, you get it
• With changing technology, human systems still stay the same, people are still people. You just choose to intervene at different places and with different tools.
• Powers of Ten (1977): “Starting at a picnic by the lakeside in Chicago, this famous film transports us to the outer edges of the universe. Every ten seconds we view the starting point from ten times farther out..”

• A team living in their prototypes can discover questions they did not know to ask. An example of an emergency room redesign project that started with a designer going through the entire process with a camera attached to his head – mostly staring at the boring ceiling tiles…
• Bring a prototype to every meeting. Be the first to stop talking and start building.
• IDEO approach to design:

• Love challenges in “tired spaces” (where everyone thinks that everything is known and done, no belief that there is something radically new possible): shopping carts, banking…
• How do you get a “more than a fair share of” interesting projects in the world and interesting people to do them?
• Future of Book: created by interested interns, no formal project:

• More videos: IDEO Vimeo channel

—-

For more posts on the Stanford GSB Sloan life – see the table of contents here.

Could be that my future co-alumni will look back at this week from the future as historic: the school came out to the public with news that there will be no more Sloan Program. The competition for the title of the Best Sloan Class Ever (est 1958) will finally be over – as us in the 2013 cohort will remain the Last Sloan Class Ever. The program itself will of course live stronger than ever, getting a fresh rebrand as Stanford MSx Program for Experienced Leaders.

Meanwhile, the academic life of T-7 week carried on, busy as usual. I realize the four keywords picked for the title sound a bit buzzwordy – but there was some solid content behind each. Read the notes below, and also check out the long-overdue video of my LOWKeynotes speech from the Winter Quarter if you happened to miss it.

Covered in this issue:

• Calculating social influence in marketing and building funcional teams
• Calculating effects of advertising spend on demand
• Designing Call Centers and measuring process quality
• Do freemium products need selling?
• Exit dilemmas of Hotmail
• Managing media crises and firing firends
• Being realistic, but curious about Bitcoin
• Founding stories of Twitter and Square
• Mapping out all asteroids in space around us
• Guests: Jack Dorsey of Twitter/Square, Sujay Jaswa of Dropbox, astronauts and folks from Clearwire, DFJ, Lightspeed Ventures

OB322: Networks (Hasan)

Topic: Social Influence & Viral Marketing

• N.A. Christakis and J.H. Fowler, “The Spread of Obesity in a Large Social Network Over 32 Years,” New England Journal of Medicine 357(4): 370-379 (July 2007):

• Later follow-up studies have found happiness, compassion, etc are “contagious” among socially connected people, but also… loneliness.
• On contrary, many other studies have shown that results often don’t hold when controlled many other aspects (even simple ones like physical residence). No conclusive agreement.
• Simple linear equation: y(ego) = beta(zero) + beta(one)*y(peers). At which rate will peer behaviour influence yours.
• Mechanisms (“real processes”) that cause influence:
  • providing information
  • imitation
  • reducing uncertainty (there is risk in adopting a new behaviour, someone else doing it reduces risk)
  • positive externalities (husband on Google Chat, wife on Skype – will align to be able to talk to each other. the other adopting will actually benefit you)
  • persuasion
  • installed base effects (enough people drive a Prius so you end up buying one even if a) none of your direct friends do and b) there is no positive externality – others owning Priuses actually means less room in special lanes & parking)
  • threshold effects
  • snob effect (when too many people adopt something, hipsters disagree)
  • generalized repricrocity effect (a group of people giving each other gifts out of the gratitude for receiving gifts. tricky, as the evidence can be very similar to a wide variety of actual motivations)
  • simultaneous adoption (people discuss and make adoption decision at the same time – impossible to say which influenced the other)
• “Choose your enemies carefully, because you will become like them” – research shows people do adopt behaviours of their competitors
• Fake processes (unknown unknowns) that can mislead in influence assessment
  • environmental / contextual effects
  • reverse causality (can’t say based on limited data which way peer influence actually works in a graph)
  • induced environmental effect (did McDonalds set up shop in an obese neighbourhood, or did people get fat there because McD?)
  • homophily & selection (two people retweet a news about George Bush library. you might conclude one influenced the other to retweet, whereas actually they are just both massive Bush fans)
• Need to eliminate fake processes in analysis, or your viral marketing campaign looks far more effective than it really is. Ways to do that:
  • Ensure the timestamp on y(ego) and y(peer) are not same, needs to be appropriate lag for causality. Works for removing contextual effects, but not others like homophily.
  • Run randomised experiments – really the only way: Intuitively you could try to gather more observables, but can get really expensive and there could be hundreds of millions on unobservables you will never get to.
• Viral product features can be active (you recommend this to a friend) or passive (ambient information about your friends installing stuff – something they did not explicitly share). Increasing evidence passive only works very short term, if at all.
• Article: You had me at hello: How phrasing affects memorability (via Jon Kleinberg)
• Article: Computer Scientists Show What Makes Movie Lines Memorable
• Proven results that you get most out of marketing to the person with highest Eigenvector centrality. (Potentially one per each cluster)

Topic: Team formation

• Selection criteria for effective team members:
  • Potential – research shows you need diversity in both of these dimensions:
    • human capital – smart, hardworking, heterogenous
    • social capital – cohesion, structural
  • Execution capability – a lot of diversity on above can be balanced with homogeneity on other dimensions to achieve faster team startup time, less conflicts, common language
• Book: Organizations (1958) by James March (Stanford professors still, basically created the field of Organizational Behaviour)
• You can think of hierarchies (trees) as the purest form, and measure any social graph against it
  • For a structure to be a hierarchy, it needs to be fully connected (no islands)
  • Connectedness = 1 – (V / N(N-1)/2). V is number of all connected nodes.
  • Efficiency
  • Least Upper Boundedness: if you pick two people, is there someone above them who can resolve their conflict. (In hierarchies always the top boss, if no-one lower)
• N(N-1)/2 – simple way to turn a two-dimensional graph (4×4 for 4 participants mapping their connections) into a single-directional. Matrix minus the diagonal (connections to self) and then just a half of the remainder.
• If everyone in the world tried to build a network with structural holes (for brokerage opportunities), there are two possible equilibria: no network at all or a fully connected network. Neither has any structural holes.
• In prisoner’s dilemma games, there is one subgroup who constantly defects (even though co-optation has higher payouts): people with undergraduate degrees in economics.
• You can build a simple simulation game which tries to solve the utility function of establishing a connection with someone (accounting for “quality” of another node like seniority, authority or fame; cost of connecting; them returning the connection back, etc). Running it on random pairs on any graph, you can get to a pretty good estimate how the network between these people would eventually look like.
  • Some predictable patterns will emerge: for example higher range of “quality” measures (some nodes are “better” for you than others) will lead to more hierarchical networks
• Research showed that when you increase limits of free movement of people between companies (for example, non-compete agreement enforcement in Michigan), the overall innovativeness in economy drops.

MKTG 365: Marketing Analytics (Narayanan)

Topic: Advertising

• GRP or Gross Rating Points = Reach % * Frequency. Common ad industry measure. GRP 100 can mean you reach 10% of audience 10 times, but can’t distinguish exact impact of these components.
• TRP or Target Rating Points – same in a given population.
• New products have a much higher advertising elasticity than established ones (increasing spend increases sales more). (an experiment 5X: 0.26 vs 0.05 respectively). Similarly, copy tests (A/B on changing the ad content) shown to change outcome for 60% of new products, but 25% of established ones.
• Consistent results that if there is no significant effect of advertising in 6 months, there never will be. Yet, absolute periods vary notably (coke vs car sales)
• Log-linear regressions are more useful estimating branding effects of ads (rather than information effects, which are more important for new products)
• Goodwill stock (or adstock): when current advertising is considered to have “full effect”, then past advertising has some effect on current sales too still, and is included multiplied by some depreciation.
  • log(Sales t) = a + c*Goodwill + e
  • Goodwill = log(Adspend t) + depreciation1*log(Adspend t-1) + … + depreciationL*log(Adspend t-L)
  • Alternatively, especially with short period data (weekly advertising?) you can assume geometrically declining lags and express depreciation factor with one parameter: Goodwill = log(Adspend t) + depreciation*log(Adspend t-1) + depreciation^2*log(Adspend t-2) + depreciation^L*log(Adspend t-L)
• Estimating depreciation: either use prior knowledge of a given industry or search for a d that provides a good regression (high R^2)

OIT 262: Operations (Plambeck)

Case: Call Center Design for Lion Financial Services

• US market: 47,000 call centres, 2.7M agents (2% of total US workforce!). 60-80% of cost is agent labor.
• Naive view of call center operations is that agent’s work is a sequence of conversations. In this view, managers don’t see the need to devise & train on process (as even the greenest agent knows how to talk and use the phone). As a result, every agent invents a personalised process for themselves and there is no mechanism for capturing and disseminating best practices.
  • You also need a well-defined and consistently distributed baseline to enable any agent-driven incremental improvements to it over time.
• One potential process view: every agent is engaging in a sequence of service transactions.
• Simplified blueprint for agent training: give the manual, then explain it, then tell them you are going to test them, then work through it again, then test. Not finished until you actually see and hear it done correctly twice.
• Consider very carefully if and how much you want your culture and rewards to nurture “heroic service” – attempt to doing that across the board results in high variation of actual service, longer average call and queue times, etc. Focus on eliminating the disastrous experiences from the low end (and driving more customers into “satisfied” category, rather than “in awe”) could have much clearer payoffs.
  • In similar way, super-friendly service can lead to customers attempt to always speak to the same agent. This can yield to disappointment when that is not possible or takes a long time. Eventually leads to a much more expensive “account management” paradigm, which you want to consciously decide to create, not have accidentally develop in a call center.
• Effective mystery shopping by managers: posing as customer, transact via your own call center and then do the same immediately via a competitor service for direct comparison.
• Would teamwork and peer discipline work in your environment instead of top-down supervision? Have often do you see agents upset over their co-workers poor service or absenteeism today?
• Empathy to consider in change management: managers often feel threatened as they subconsciously see their agents as younger versions of themselves.
• Technical definition of quality: conformance to specification

Article: Teaching by the Book, No Asides Allowed (NYT)

Book: The One Best Way: Frederick Winslow Taylor and the Enigma of Efficiency (Sloan Technology)

• Taylor created “management with a stopwatch”, search from ultimate efficiency in work processes. Saw a way of breaking any process to the tiniest possible steps as a way to reduce dependancy on highly skilled labour and their individual decision making.
• Taylorism: “You are as fast on day one, as on day five or fifty” – completely disposable workers

Topic: Measuring Quality

• W. Edwards Deming and his 14 Points of Quality - pretty much every later framework (TQM, 6Sigma, etc) are variations on the theme he set
• Ishikawa diagram (the “fishbone”): useful for brainstorming to understand potential drivers of quality problems
• Process Capability Index: Cp = Width of Specification Interval / Amount of Variation in Current Process = What Customers Need / What We’re Giving Them = Demand Width / Supply Width
  • Cp = 1 means requirements met 99.7%, Cp = 2, 99.9997% of time.
  • Six Sigma is Cp = 2 case: upper tolerance level is within 6 standard deviations of the mean. Term coined at Motorola
• Even when difference between Cp=1 & Cp=2 seems small, consider potential issues:
  • Weakest link: with a product of 1000 parts, the P(satisfied customer) is the multiplication of the success probability of all components. At Cp=1 the likelihood is 0.9973^1000 = 76.3%, at Cp=2: 0.999997^1000=99.9%. (btw, with 6 million parts in a Boeing, the probability is pretty much zero in either case)
  • Tolerance buildup: if a car door must properly shut to keep out the rain, with Cp=1, each part can be within thickness requirements, but the assembled door still leaks

STRAMGT 351: Building and Managing Professional Sales Orgs (Lattin/Levine)

Case recap: OuterBay & EMC

• Once corporate development hands over to product org, no-one remembers the contract nuances and creation pain. Goes into autopilot: count units and send invoices
• Biggest make-or-break of large reseller
  • does their sales force have competence to sell your product?
  • does their DNA allow selling an externally sourced product?
• Fine line of when a large reseller makes sense for a startup:
  • If you have no leverage to get the terms you want, do you need to make the deal?
  • If you have a lot of leverage to get the ideal deal, do you really need the deal?

Case: Bausch & Lomb: Salesforce Reorganization (Stanford E213)

Book: Structuring the Sales Force for Strategic Advantage (Ch 4 from The Complete Guide to Accelerating Sales Force Performance: How to Get More Sales from Your Sales Force)

• Efficiency of a sales org is the rate at which a given investment is converted into sales calls. Effectiveness is about the customers response to those calls.
  • Geographic structures usually provide the highest efficiency, but other structures can be more effective due to heterogeneity and complexity of markets and products.
• The technical sales dichotomy: “do I hire an engineer or a fraternity/sorority president?”
• Four fundamental sales force models:
  • generalist: each salesperson sells the entire product line to any client or prospect in defined geography.
  • market-based: know their market segment and customers intimately
  • product-based: aligned to parts of the product portfolio, can be integrated or independent (trade-off: customer confusion), depending how high up they report to a common manager.
  • activity based: for example, different teams for new accounts and maintaining existing client accounts
  • And fifth: hybrid between two or more of those. Risk of complexity: a structure with 4 geographies, 5 segments (financial, health, government…) and 3 activities (new business, maintenance…) will lead to 60 different “selling faces”, a lot to justify & manage for even a sales force with many employees. General advice: as few selling faces as possible.
• Sales coverage matrix: identify dimensions where most heterogeneity exists (and cluster homogenous markets, products…)
• Sales teams as a rule are not efficient – consider if they can be justified by increased effectiveness.
• “Sometimes what doesn’t kill you… almost kills you”: Doesn’t make your org better, creates cultural resistance to next change attempt

Topic: Selling Freemium Products
Case: Dropbox
Guest: Sujay Jaswa (VP of Business, Dropbox)

• In successful freemium examples, the free product provides true value to users as opposed to a “gimmicky,” unsatisfactory product offering; further, the conversion from free to premium is motivated less by frustration with the free product than by hope and expectation that the premium product will provide additional value that exceeds the marginal cost to the user.
• Idea for potential path to enterprise: business to developers (B2D). Instead of the CIO, sell your platform/APIs to developers serving that CIO already?
• If you want real analytical/quant superstars, the bar should be ridiculously high. Think 300 CVs (top MBA programs, Silverlake/Blackstone/Goldman/etc) -> hire top 3.
• Pattern matching in managerial decision making is dangerous, because your dataset is always statistically insignificant. There are no truisms in business – if you hire someone who will try to everything as TheirLastCompany did, they will try to do in 2015 what YourCompany does now in 2013 – which might not be the right thing any more.
• Hire great people into seemingly mundane roles. When a Stanford Computer Science grad with 3.9 grade average has to do support tickets, they will hate it in 6 hours… but you can then tell him to automate the annoying stuff, find creative ways to fix it. And they will.
• On managing culture, find videos and books by Lou Gerstner - “he’s the real deal”
  • BOOK: Who Says Elephants Can’t Dance?: Leading a Great Enterprise through Dramatic Change

Article: Enterprise 2.0: The science of inside sales (GigaOm)

• Inside sales jobs are growing at 15X the rate of outside sales roles
• You’ll need a price point of at least $25 to $50 per user per month to validate the value of your product and make enterprise sales work. At that price or above, a workgroup of 10 to 20 users can be sold within a customer account for $5,000 to $10,000 per year
• First inside sales team should consist of the company founder/CEO and two salespeople. My advice is not to hire an experienced VP of Sales to build your team. Instead, the founder/CEO needs to become a student of the science behind sales
• Eventually quotas settle around $1M per annual revenue, but for first 18 months makes sense to pay per every deal closed despite of their size – until the recurring revenues start ramp up.
• Extra bonuses on deals closed for longer than a year at a time.

Article: Freemium Software: A Guide for Startups (PDF; Institutional Venture Partners)

• If you’re in a market where your customers are asking for pilots, RFPs, RFIs, or other strange acronyms, there’s a good chance that your product is too complex for freemium.
• Investors businesses that can provide us with loads of user cohort data, because it reveals both the current health and the future potential of a company.
• Freemium companies often scared of moving away from a working cycle towards more traditional sales orgs. If customers want to buy your product in a different way, it’s ok to adjust your sales model.

Article: Dan Ariely. “When Free Is Dangerous” Big Think. August 5, 2009.

• Further, the free-of-charge nature of the offering largely removes the psychological barrier and risk of any downside from trial in a user’s mind, serving as an emotional “hot button” and source of irrational excitement that can lead to viral uptake.
• In a famous experiment, behavioral economist Dan Ariely illustrated this tendency by asking participants to choose between a $0.14 Lindt Truffle or a Hershey Kiss for $0.01 (one penny). The vast majority of participants chose the Lindt, understanding the utility of the Lindt to be quite high, even at a higher cost. Yet when the price of both chocolates was reduced by a nominal 1 cent—making the Hershey Kiss free— nearly every participant chose the Hershey Kiss.

Article: Case Studies in Freemium: Pandora, Dropbox, Evernote, Automatic, and MailChimp

• Our cost per effective acquisition per paid user was thousands of dollars for a hundred-dollar product. So the big lesson there is if you adopt a freemium business model your marketing cost is the free users. Search is great for harvesting demand, not creating it.

STRAMGT353: Formation of New Ventures (Holloway/Morgridge/Chess)

Case: Hotmail
Guest: Steve Jurvetson (Managing Partner, DFJ)

• Steve was a student of Holloway in GSB, Holloway got the background check call from Draper – Steve made partner in 6 months after hired.
• On planning your VC pitches: “When you want money, ask for information. When you want information, ask for money.”
• In ideal, transaction-cost less world where your price per share goes to top-right in single continuous curve – you would want to raise funds just in time (week’s worth of cash every Monday? every day? every hour?). That is a baseline from which you could think about also the real world – growth curves with their ups and downs, important milestones, overhead of raising rounds.
• The money you take in first round is “the most expensive you ever took” only when you hit your milestones.
• Right of the first refusal is a bad idea – imagine how the first 2 years of your marriage would look like, if you agree at start you will negotiate the pre nap only then?
• Why not do 10 deals for 2.5% instead of one for 25% equity? Economically should be the same? The issue is limited partner time to be involved.
• Imagine a game where you can flip a coin (50/50 chance) to 10X your entire net worth or lose it all. To what level would you flip still? $10k, $100k, $1M, $100M, $1B, $10B? As a VC managing a portfolio, you want every one of your companies flip much more often and at higher levels than people normally do. Need to design incentives (incl term sheets, double dipping, etc) accordingly.
• If a company gets as close as weeks or a month to complete cash fuse-out today, it would be a shame on the board. Imagine a car driving over a cliff with no skid marks before the edge at all. Puts you in a massively worse negotiating position for the raise.
• As investors, DFJ doesn’t block acquisitions if founders want to do them, but try to convince them not to take the first offer, and even better – create auctions using the VC’s network
• If a big company comes knocking and you don’t want to take the offer, you might not want your entire leadership team get deeply involved in negotiations

Case: Freedom Financial Network
Guests: Brad Stroh & Andrew Housser (co-founders, FFN)

• Bootstrapped with $30k to $100M revenues in 8 years. Then raised $100M in private equity to be capitalised enough to be able to win under new regulation.
• You have to systematically put yourself in a position where you can get lucky
• Were looking for a large market, no dominant player, inefficient, weak competition, technology or regulation are driving change. Did not have a “big idea”, but knew consumer financing with $13 trillion volume is something where you will figure something out.
• Choosing a new office location by where you can get the best person to build it. “This VP is a superstar, we’ll build an office where he can build it” (their hiring network, etc)
• On lobbying: “If you’re not at the table, you’re on the menu”
• Roots of the culture from the bootstrap period where first few hires are working for you for free – democratises like nothing else. Still deal with every new joiner and call every employee on the birthday at 600 people.

STRAMGT355: Managing Growing Enterprises (Dodson/Peterson) – visiting

Article: The 10 most common firing mistakes (by Joel Peterson)

• There is no upside to using “high velocity” language in emails. You can usually cross them out before sending without loosing assertiveness and urgency.

Guest: John Stanton (Chairman, Clearwire)
Case: Managing Successful Partnerships
Topic: managing a change with a friend reporting to you and your board

• When devising talking points, you usually get to about 10. Force-rank them and only go with top 2-3 – others can still emerge (and you’ve talked about them), but drive the conversation with more focus. Also, limits some of the (emotional) objections you can predict on less important items.
• Analyse your counterpart’s audience they are really playing to, that matters to them. “You’re making me into a scapegoat” -> in whose eyes? General public? His direct family?
• If you have actually made a decision about someone, you can not fake brainstorming for solutions with them. People can sense and feel much more betrayed in the end. Remember the image: “if you need to amputate someone’s leg, don’t do it an inch at a time – go straight from the hip”
• In complex board dynamics of a public company, consider when you are creating a record that can be later used by legal process. If a board member is about to send an email about resignation, it is a good moment to ask for a pause to discuss before formalising.
• “Redefine a role” is not just an euphemism for “demoting” or “firing” – it actually opens an constructive avenue to discuss new and potentially appealing tasks and challenges
• Get to the point, really. Skip the “hey, how are you doing” if you know the only answer really could be “not good at all – until I hear what you actually have to say”
• Boards need to be able to replace a CEO in 24 hours (by law in some places). Even as an interim contingency for case of an accident, this requires elementary succession planning (“which other board member will step in?”)
• When you lead, there is no rational need to talk about yourself (or take credit) anyway – in the end of the day, people will assign the failures or successes of the hundreds (or hundreds of thousands) of people working under you personally to you anyway.
• A lot of the things that help the success of a hard conversation are subtle: neutral environment (not either’s home or office), etc
• You can assume the other person will react emotionally and irrationally — and be positive surprised if it is the rare case of having a rational conversation throughout.

Case: Green River Environmental
“Guest”: David Dodson (as protagonist, not Professor this time)
Topic: Managing Crisis & Change

• “Character is like a tree and reputation like its shadow. The shadow is what we think of it; the tree is the real thing.” (Abraham Lincoln)
• Assemble a small brain trust immediately to figure out action plan (PR? lawyer? board member? subject matter expert?)
• In preparation of external briefing / media interview in the peak of crisis:
  • Be sympathetic to the reporters’ job, e.g., help them meet their deadlines
  • Exude confidence and trustworthiness. Be cool under pressure.
  • Take charge of the interview.
  • Make sure the reporter has the facts.
  • Be sensitive to potential legal ramifications.
  • First and foremost communicate sympathy and concern for the victim, their family – good to underline before jumping into first question, hard to take control of the tone later.
• Can you separate the conversation of the reasons why a crisis has emerged and which position it has put you in as CEO from the logistics of the corrective action (for example: termination and severance)?
• Design what would winning mean when solving a crisis: for investors, employees, customers, you personal reputation…
• Stepping down from a role is not about you (as if anything really is), it is about what happens to the company. You might be down, but you must show confidence in the plan for way forward – transition, etc
• Many media organisations have a strict “off the record” policy, which usually is towards “no attribution” rather than “confidential”
• IQ and integrity can not be changed. Temperament is hard to change, but can be earlier in the career. Judgement is hard to change, but you can give people tools that help (especially time, “why don’t you make a decision on this by tomorrow morning”).
• The old playground rule: what would you want if the situation was reversed? Lawyers would ask you not to put things in writing, but maybe you would like a tough news from a person in a letter to have time privately to absorb and understand your response before a face to face meeting? Maybe then it wouldn’t be so risky and weak as you thought?
• Two extremes blaming others and blaming only yourself are two extremes. Reject those, the truth and all the learnings are in the middle.
• Perfectionists are unable to improvise from the small things that always go wrong on the way. If success and failure are a zero sum game, there is a super high risk of disappointment.

High Tech Club BBL

Topic: Bitcoin
Guest: Jeremy Liew (Partner at Lightspeed Ventures)
Blog: http://lsvp.com/author/jeremyliew/

• Bitcoin is distributed, open source currency. Think of the definition of all three of these words separately to get your mind around it.
• When in Palladium prices 100% of value is the industrial use value, in gold 15% — 85% of just an agreement between people that this is what they think it is worth (for any reason). In case of Bitcoin it is just 100% – not that far off from 85%?
• People have one or more wallets, expose the public keys and can send money to other wallets by signing a message with their private key. There are keepers of ledgers who keep track of all wallets in existence and record how many coins each has – that cryptographic “logging” is called mining and is rewarded by giving (decreasing amounts of) bit coins to the miners (ledger keepers)
• Because of the identifiable wallet numbers, bit coins are pseudonymous, not anonymous. And by choice of user they can publish the link between their real identity and wallet number if they want.
• By design there will be 21 million bit coins in existence. Today 11 million done, estimates that will take 30 years more. A big differentiation from other currencies – any government can print more of their currency.
• Energy and computing cost of mining is ROI positive today.
• Bitcoin is 4 years old, but already has had 5 bull markets and 4 crashes this far. Very volatile. To a large part due to inefficiencies – for example notable friction to get dollars to and from Japan to trade on the largest exchange today.
• By design, hard to borrow bitcoin – therefore also to sell short. Which in turn impacts price.
• US$100-330 million traded per day on $1.5 billion. 70% of bit coin that has been sent has never been re-sent. People who got in early have more, distribution of wealth uneven.
• Probably a notable share of bit coin has been lost – you lose you private key, you lost your money, no recovery process.
• Most mining happens in Eastern Europe today, used to be more evenly distributed.
• Miners can take a transaction fee, but today it just jumps you to the top of the queue.
• Early adopters: cryptographers (fun), libertarians (no government control) and anonymity-seekers (who are probably doing what they should be doing). Because online gambling it is illegal in US, a large (50%?) share of last group are gamblers.
• Big problem for adoption: you want your income and cost denominated in same currency to not carry that risk: a bitcoin-selling bakery buying flour for dollars…
• Regulatory guidance is so early and unclear, you can’t even say if they would like to increase oversight of exchanges, or maybe also miners and maybe all wallets as money transfer agents…
• Protocol evolves by open source community dynamics. Recent example of a dot-release of software that broke compatibility with past versions (to fix some issue). Miners basically voted by upgrading or not – and as ledgers are consensus based, one side prevailed in hours.
• Some Bitcoin protocol flaws new competition could want to address:
  • blocks in 10 minute batches – can’t guarantee real-time transactions
  • issue of “losing the wallet”
  • significant energy cost of mining
  • ease of use: very technical audiences, clunky software, cryptic looking codes
  • downsides of distributed storage: if a single copy of your wallet is on your PC, what about malware, hard drive crashes…
• Huge promise of removing friction and cost from transactions:
  • micropayments: media per consumption, gaming ($5 for virtual goods is not so micro)
  • non-profit: 30 cents of handling fees on a $1 you give for a good cause online?!
  • doesn’t have to be zero (hope bitcoin on protocol level will be), but up to the companies facilitating to define pricing that works for different scenarios
• Fraction management: you can transact in 8 digits after zero, but this is not a hard limit. Yet, this again is not user friendly: explain to your mom how much did they pay exactly when they used 0.0007102 online?

View From The Top

Videos will appear here: Stanford View From The Top YouTube Channel

Guest: Jack Dorsey (founder, Twitter & Square). Instead of notes, just wait for the video at above link.

Space BBL

Guests: Ed Lu & Scott Hubbard (B612 Foundation)

• Ed has did 4 shuttle flights and spent half a year on International Space Station before… becoming a product guy for Google Street View.
• B612 is a foundation is set up to launch a UV-telescope on Sun’s orbit inside Earth, looking “outside” and mapping out all asteroids that could potentially threaten Earth on collision (from demolishing forest to wiping out Paris or the entire humankind).
• The surprise Russian meteorite is a good example – did not crash into ground, but still caused UV-burns (4 times brighter than Sun!) for people nearby. Timing of these videos hitting youtube helped B612 fundraising…
• The cost of the program is around $400M and has never passed the scientific review boards by NASA or anyone else. Now financed from private donations (will cost less than the privately funded new wing of SFMoMa museum, and just 3x more than the new Bing concert hall in Stanford!)
• On track for the telescope launch in 2018.
• Once known asteroid is approaching, detouring it to safety is a surprisingly straightforward mission (delivered by a small satellite colliding into it – no Armageddon-movie-like BS of nuclear attacks). Cost ~$1B – but likely the funding would be easily found from national governments if there is a proven high risk threat.
• New Space – new peer reviewed magazine of the field

This is probably the longest-prepared short speech I’ve ever delivered, as a finale of a whole-winter-quarter-long LOWKeynotes program at Stanford GSB in 2013. A text version is below the fold, and if you got anything out of watching this, I’m sure you would enjoy all of the videos from my peers in the program. All of the outcomes were worthy of the effort put in, but if you need help from where to start, try the videos from Lukasz Strozek (on digital hoarding) and Evan Moore (on not believing in God) first.

Due to the iterative presentation development process of the LOWKeynotes program, I actually have a much more thorough written trail of how this story came together. Including two versions below, the final draft (which the video version came structurally pretty close if you would rather read) and for historic reference also the first idea cut (scroll all the way down):

The Future That Should Be Here Now – FINAL DRAFT

I am 35 years old and I wrote the first cheque in my life a few months ago. It was for something or another at my son’s public school, and for some reason they were not willing to accept debit card, credit card, Square or PayPal. I didn’t even offer a wire transfer – the only time I’ve tried to do that in the US, I had to take an hour to physically go to a bank branch and fill out 2 sheets of forms. So, I wrote a cheque to the school, pen on paper.

Two weeks ago I went to San Francisco, parked my car on the street — using my mobile phone as any normal person would. I got a ticket 2 minutes later. When I went to the Transport Agency’s [SFMTA] website to challenge it — figuring that if you can pay fines online, proving you’ve done nothing wrong shouldn’t be any harder — I learned I again need to print two pages paper, sign them on paper, get an envelope and a stamp from somewhere, and send it to San Francisco by snail mail to start a few weeks of dialogue just to argue I was actually legally parked. I did nothing wrong and spent a few hours at least on this.

* * *

These little adventures back into the 19th century would be just amusing, and well aligned with the horses and carriage my US bank proudly wears on their logo, if they weren’t so tragic. Today I want to talk to you about…

1. the miserable waste they generate for the society,
2. how unjustified these pains are,
3. the future that should be here now.

* * *

First, everyday frictions cost a lot to us personally and to the society. Why is that when Facebook is 2 seconds slower than usual, you are starting to get anxious — but another hour wasted at the DMV is a necessary evil you’ll just bare with?

Let’s say that we are all spending, on average, 10 minutes a week  on this analogue bureaucracy.

10 saved minutes a week is about 1 full working day a year.

Between 38M people in California alone we’re talking 100,000 human years wasted in a single year. Or, if this “years per year” construct is too abstract, think of it as 1,500 human lifetimes wasted a single year.

If you want to think entire US - add a zero. 15,000 human lifetimes.

And by the way, I’m afraid wasting just an extra, unnecessary 10 minutes a week on things you have to do, rather than want to could be a slight underestimate, don’t you think? The stories I shared above were all several hours each to sort out…

* * *

Secondly, let me remind you, it is 2013 and we are in the middle of Silicon Valley. In the one place that is infamous around the world for creating all these beautiful technologies meant for consumers on the internet. The newest and coolest tools for social networking and search and sharing pictures and buying things and meeting people and listening to music and watching movies — they are often born in the Valley and then expand to take over the rest of the world.

There is no way to argue that these pains of being a good citizen are about lack of understanding of technology, lack of talent or ability to design human-friendly services. Think about it – If Apple designed the DMV’s retail experience, what would it look like?

* * *

I came to Stanford from from Estonia. How many of you have heard of Estonia?

Those of you who raised a hand, let me guess what you’ve heard. Maybe that unfortunate incident when we were occupied by a certain Communist superpower for 50 years? Or that it can get quite cold and dark and snowy so far up North?

What I guess many of you might not have heard is a Freedom of the Net Report published by Freedom House in Washington, DC, United States ranks honourably #2 in the world for internet freedom. Surprisingly, the #1 spot belongs to Estonia.

We have a million people online, which wouldn’t be anything that remarkable if you didn’t consider we have less than 1.3M people in Estonia over all.

The country is covered with high speed wireless broadband and for the large part it is free. 100% of schools and government have been online for a while now.

80% internet penetration is pretty similar to US, but more importantly, let’s look at what that ubiquitous connectivity is used for. *SLIDE* 99% of bank transactions are online. We joke that these 1% of transactions are made in banks by the truly rich – those rich in excess time.

94% of tax reports are filed online. And by the latter I don’t mean that there is a web form you have to fill, but that you click “next-next-submit” to verify a pre-populated electronic data and, in majority of cases, get any returns on your bank account 2 days later.

We put a nationwide digital signature in place since 2001, which means that by today 92% of the people carry digital certificates on their ID card or in their mobile phone. This allows them to legally sign any document or log into any site proving that they are who they claim to be. As one application, in 2005 we were the first country in history to hold nationwide elections on the internet and in 2011, already a quarter of all votes for our Parliament came digitally. But more importantly, as many other parts of the open infrastructure this is not “a government thing”, but something private people and companies can freely use to securely transact with each-other too.

* * *

I am talking about the Estonian example here only because that’s where I’ve spent most of my life. I’m sure there are people in the audience who come from Singapore or South Korea or Finland or the Netherlands – they would have similar stories to share.

The point is not to brag or compete here. Given where we’ve gotten to with the accessible computing power, with the fact that every one of you is carrying it in your pocket right now and you’re always connected: Things that we have to do shouldn’t be so much harder than the things we want to do.

There is a future that should be here now. Because it is already here for many people who have similar or even less tech than you have in Silicon Valley.

When you leave this campus, into businesses or non-profits or government — or just your role as a citizen, I call for all of you to demand that future, or even better – dream it, define it and deliver it every day.

And if you could use some further inspiration, some tiny countries around the world would be happy to share. Thank you.

—

And for history, this was Week 1:

LOWKeynote on being a digital citizen – IDEA PROPOSAL

• coming from small Nordic country, tech advanced
• shocked when coming to US, how big are the differences
  • how people perceive technology
  • how people barely use tech for a good citizen
    • file taxes
    • make a bank transaction
    • sign a contract
    • park a car
• paint a picture of the world that could be
  • how tech could help people in their daily lives outside of social networks and search engines
  • Digital Society Components (E-Estonia)

—

UPDATE: re-used the content above to experiment with Medium: the post with inline slides.

No bandwidth to say much more that you will find not one or two, but three beautiful graphs I drew this week with my two little hands below the fold. And that the sales game hinted towards last week concluded with a triple win by Sloan Fellows (who sold $4.1..$4.7M/each over a quota of $2.6M; among those your’s truly being #3).

The two View From The Top events this week were different but great – my notes won’t do them justice. As there is some lag in getting videos out you should just subscribe to the playlist and stay tuned.

Covered in this issue:

• Visualizing social networks and Empirical demand modelling
• Toyota’s unique manufacturing process
• Startups selling products into huge companies
• Compensation tuning for founders versus early employees
• Tough conversations before, during and after letting an employee go
• Guests: Al Gore, Co-CEOs (incl the original “Ari Gold from Entourage”) from William Morris Endeavor, Nuru International, KKR Capstone, Accel, EIR for New York, StudyBlue

OB322: Networks (Hasan)

Article: Visualizing Social Networks, Linton C. Freeman

• VC Associate networking task: stay in touch with founders who get stuck on director levels in large companies after acquisitions. The become often overlooked, can catch them early when they are planning their next venture, and even if they want to spend more time in large co, can be lured into advising new startups.
• Primacy & recency effect in action: if asked now, you would list in your best GSB contacts, you would have say a) the people you met first when coming to school, and b) the ones whom you met in the classes in this quarter.
• Deepening effect on bridging professionals: if you are a lawyer, but know other lawyers not just professionally (as colleagues and competitors), but as an organiser of a non-profit in the fields, social events, conferences…
• Results of quick in-class survey, processed by UCINET NetDraw. Color: Sloan/MBA1/MBA2, shape: gender, size: betweenness.

Who do you consider your friend?

Who would you go to for help on a project:

MKTG 365: Marketing Analytics (Narayanan)

Topic: Empirical Demand Modeling

• Old notion of demand modelling: characterise relationships between input variables (ex: coupons increase sales), ask managers/salespeople fro estimates of parameters, simulate sales with these assumptions. Conduct a campaign for live data and refine model.
• Empirical demand modelling: Start with a mathematical model, use historical data to estimate parameters, predict fitted values and evaluate appropriateness of the model.
• Multiplicative demand model: S=AP^-b (A = location of demand curve or intercept; b = price elasticity of demand).
  • Adding an error term to represent effects of unobserved factors would result in: S=AP^-b + e (but issues: sales must be positive, can not be solved with linear regression…)
  • Alternative: S=A*P^-b*exp(e). e could be any negative or positive value, but exp is always positive. With normal distribution, exp(e) fluctuates around median 1.0
• Using Data Sources: time series (sales in weeks 1…54) vs cross-sectional (week 8 in locations X, Y, Z):
  • Need to properly control for environmental factors. Generally time series preferred since most sales environments relatively stable in the short term (1-2 years)
  • Cross-sectional analyses often fail to control for differences in consumer tastes, distribution, competitive environment, merchandising, demographics…
  • Panel data: contains both time series & cross-sections
• Stronger brands have lower demand elasticity because there are more things than just price you consider
• Effect of consumption patterns on elasticity: soda customers are loyal, but can buy a lot ahead during promotions (easy to store). Can lead to different elasticity if calculated based on weekly VS monthly sales.
• Some substitutes can have negative correlation of elasticity (when Coke’s price goes up, Pepsi can go down): for example a retailer who works with different suppliers to build a calendar of promotions – in retailers interest to always have something on sale to draw customers.

Case: Predicting impact of changing hold on profitability of slot machines

• A good example of where dummy variables make sense instead of a single numeric one: pay-in denominations. If a machine takes bets in $0.25, .., $1, …, $10,…, $100 denominations, linear relationships are unlikely and quite useless (when graphing)
• Finding elasticity: regress logS logP. If coefficient of
  • logS or sales: how much people play or log(salesperday=coin_in/days_active)
  • logP or price: percentage how much the casino holds log(hold)

. regress logS logP

      Source |       SS       df       MS              Number of obs =   30439

-------------+------------------------------           F(  1, 30437) = 7577.56

       Model |    4462.261     1    4462.261           Prob > F      =  0.0000

    Residual |  17923.6907 30437  .588878362           R-squared     =  0.1993

-------------+------------------------------           Adj R-squared =  0.1993

       Total |  22385.9517 30438  .735460665           Root MSE      =  .76738

------------------------------------------------------------------------------

        logS |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

        logP |  -1.346866   .0154725   -87.05   0.000    -1.377192   -1.316539

       _cons |   10.96853   .0355516   308.52   0.000     10.89885    11.03822

------------------------------------------------------------------------------

• logP coefficient -1.35 is the negative elasticity (drop the price/hold and sales will increase). However, should re-run controlling for denomination, location (based on correlations hinted by graphs, intuition)

OIT 262: Operations (Plambeck)

Case: Toyota Motor Manufacturing, USA, Inc

• jidoka: make any production problems instantly self-evident and stop producing whenever problems detected.
• kaizen: change for the better, e.g as soon as anyone established a standard way of doing a job, that person sets out to demolish it proactively for an even better way.
• heijunka: spreading production of variations evenly over time (as oppose to batches of one variation) to match sales and enable suppliers to avoid pileups.
• muda: any waste in the process to be avoided. Including employee brain power going unused.
• Piggly Wiggly supermarket was a pioneer of pull-based process flow in US (e.g new milk ordered from suppliers once a customer buys milk)
• Water & stones metaphor: if water is the production flow, most companies try to remove all kinds of problems (tooling issues, employee absentees, supplier quality, whatnot) from the river in order to have water flow freely. Toyota tries to lower the water level (in-process inventory) to the minimal levels, in order to expose and fix any “stones” in real time.
• Given the demand doesn’t drop off in real time, the cost of a minute delay is the cost of the workers staying overtime to complete the production later. Grows as a step function, as buffers in the preceding production line are exhausted (e.g first minute stop late in the line costs only the people directly involved * one minute overtime pay; a Toyota plant could grind to a complete halt ~30 minutes when earlier steps don’t have empty buffers to produce into)

BOOKS:

• The One Best Way: Frederick Winslow Taylor and the Enigma of Efficiency (Sloan Technology)
• Japanese Manufacturing Techniques: Nine Hidden Lessons in Simplicity

STRAMGT 351: Building and Managing Professional Sales Orgs (Lattin/Levine)

Case: EchoPort
Guest: Kirk Bowman (Accel)

• Having VARs register leads in your CRM is a price protection: 2 competing resellers with your product drive the street price (and VAR margin!) down. Beware of getting “the full phonebook” registered though – use time limits, number of deals per VAR/salesman.
• Closing being the hardest part of sales is a myth: opening doors and gaining access is. And this is what VARs provide. Resellers are not cheaper, you still need internal sales to support them. You pay VARs extra for access.
• “When in doubt, raise the quota” – always the balancing lever when equalising compensation or making other changes that risk raising sales cost.
• VARs also cease your control: if you need that one sale in the end of the quarter, your VAR might just not care, often much less quarterly driven than manufacturers.
• Why do you care for accurate forecasts?
  • supply chain issues and production planning
  • delaying necessary investments (because you don’t know if you can afford them) – hurts your competitive position
  • public markets expect predictability (can’t miss a quarter after IPO)
• Pre-IPO company is an extra motivator for sandbagging. If an early VP Sales has 2-3%, this could mean he has $20M on the table – beats any short term incentive and job preservation becomes the main thing.
• Competence gap: the creative, resourceful, hunter type superstar salesman will likely not have the quantitative/analytical skills to build forecasting models
• Forecasting idea: draw cohort-based productivity curves of your VARs
• “Time kills all deals” – makes profit-based compensation very hard: a salesman is willing to leave 3% of comp (and company margin!) on the table just to close a deal
• Sales FTEs (full time equivalents) calculated based on tenure: 50% in 6 months, 75% in year one…
• Managing end of quarter discipline goes directly to bottom line: lowering sales managers allowance to give discounts by 1%?
• Weekly forecasting routine (even for quarterly incentive schemes):
  • not to change quarterly targets, but to allocate resources more dynamically to make sure we meet it
  • most successful companies do this, but might not bubble all the way to the top in larger companies (stays on team / direct reports level). Common format is weekly interactive calls with field reps, not one-directional reports.
• Hybrid models dividing accounts between direct sales & VAR channels creates conflict, but you can build successful hybrids around task division: VARs do access and fulfilment, internal sales the work in between. Incentive to do smooth handovers in process.
• The “merger of equals” story ends once a VP of Sales is settled in seat: will be clear whose culture will dominate

Case: OuterBay & EMC

• Why do large companies sign startups up as suppliers?
  • innovation and R&D outside their balance sheet
  • more products to sell
  • time to market
  • increasing competitive pressure to your internal R&D (rarely works – demotivates all)
  • CONTROL without shareholding:
    • locking in a M&A target
    • decreasing competitive pressure
• Dependency changes over time (as and IF the startup succeeds):

• Large co mental model for royalty negotiations: if you spend 10-15% of your revenue on internal R&D – why not ask a startup for 90% of royalties to start (viewing them purely as a tech provider, not company)
• With full-on starting terms, a large co often effectively tries to buy the company (economics, control…) without actually investing

STRAMGT353: Formation of New Ventures (Holloway/Morgridge/Chess)

Case: NanoGene
Topic: Founders vs early employees, equity & pay
Guest: Melinda Thomas (EIR for New York, case protagonist as VP Operations)

• San Mateo court refused to judge (in a Tesla case) what is the definition of founder
• When compiling a founding team, besides the personal/functional things that each person brings to the table, what are the external resources they can help gather? (Financing, hiring…)
• Equal equity splits are not bad per se (used in ~1/3 of startups), if they are a result of a thoughtful process and tough conversations. (Too often they are just a result of avoiding hardship). Matters both internally to the founding team as well as signalling external parties (like investors).
• Balancing life science startup teams: too often they fail because they let just the joy of research drive them, do experiments that are intellectually interesting without a strong look toward market
• Equal pay does not scale. Imagine a 500 person company with equal salaries. At what point do you change then, if you start out equal?
  • Deal idea: OK, as the first exec I’ll take the same pay as the founders – but whatever they hire the next executive for, we’ll go back and compensate the difference. (VCs thought it was too messy, so settled on a deferred pay on hitting milestones in year 1)
• At early stage, real negotiations around external exec salaries are weird: If you negotiate $5-10k of annual salary – I will add more value anyway, why waste time and energy? If we’re negotiating $50k, the gap is perceived value is too wide to what market thinks I’m worth (offers from other Series A).
• You can think of equity as all upside… but don’t based your negotiations on saying that.
• As a founder taking lower salary you have a bargaining power over incoming executives (“this is what I’m taking, and I’ll give you more…”)

Case: StudyBlue
Topic: Tuning the business model
Guest: Becky Splitt (CEO, StudyBlue)

• As a startup searching for quickly prove your ability to make revenues, think twice if you even want to experiment with advertising. Those who make money there, are really focussed, hire their own sales force, cover web, mobile, tablets, etc. All of this might not be worthwhile to even attempt if you see advertising only as a short-term gap filler. And without all of this you won’t make money anyway.
• Examples how premium subscription features can emerge:
  • raise of mobile creates a completely new value prop of ability to use the product offline (for previously web-only company)
  • “no ads” package most popular for middle school parents (to spare their kids from unnecessary commercial exposure)
• Cisco’s learning in M&A: the way location is important goes two ways – in remote locations people who have their roots there don’t want to move away, and no-one wants to move there from “hotter” locations. Makes impossible to distribute talent as needed and provide growth paths for people.
• The large distribution platforms (mobile OS-s, Facebook, etc) move so fast, their APIs/SDKs change. The way you really can benefit from them is being informally in the network of people building them – almost impossible to do if you are thousands of miles from Silicon Valley and only react to the already published changes, not what’s behind the curve.

STRAMGT355: Managing Growing Enterprises (Dodson/Peterson) – visiting

Case: Nuru International
Guest: Jake Harriman (founder, Nuru)

• “Fair” in a negotiation is a zone, not a single point. Can change your perspective completely when building deals.
• Feedback on giving feedback: acknowledge the intent of the person to achieve something with that feedback (even if malformed) and frame your comments to help him/her to be more effective in achieving those results. (for ex: attack issues, not people, etc)
• People want to be treated fairly, not perfectly
• When it comes to coaching and discussing behaviour change, favour of single topic meetings.
• Consider using role play in rehearsing complex situations in the org: for example, when you want to understand how an experienced manager in your org will handle firing a problematic employee.
• Directness of then positioned (and disguised) as a form of respect
• How the organisation let’s people go is a critical part of the culture. While building the culture, as a leader you want to either be in the room observing, or be the one leading the termination conversation with others observing.
• Fine tuning the language of termination conversations:
  • “We” as opposed to “I” can sound like ditching responsibility
  • Getting to the clear message fast
  • How do you not talk down to the person, but stay respectful
• When you’re building a feedback culture with a bunch of high performance, you can also get a lot of noise. Need to balance the culture with humility.
• High performers want to have influence. A very good way to direct their feedback is to alert them when they are starting to lose it: “if you keep being so abrasive about other people, you think you’re giving feedback, but in reality you’re becoming a squeaky wheel, people start avoiding you and you lose influence”.
• Don’t micromanage people, but test them regularly. Fine line, but the intent shows: “I think when you have that hard conversation, these problems could come up – just wondering how would you address them when they do?”
• A bad day for a social entrepreneur in Africa: you get hit by lightning, get malaria and see a graph of the farmers who have died and won’t continue in your program.
• Diffusing fear is the prerequisite of having a sensible conversation. For example: when meeting with another team member after firing one: “If you were fired, you would know right now and I would be walking through with these things with you personally. So you have nothing to worry about. But I will not go into the details of the case of <person left>, of course, as I wouldn’t with you”

View From The Top

Videos will appear here: Stanford View From The Top YouTube Channel

Guest: Al Gore

• Of great leaders people say: “we did this ourselves”
• If the organisation is viewed as a computer system, with CPU, other processors and memory fields… leaders activity resembles very much a complex software that makes it work
• Leaders need to take responsibility for constantly elevating the visions, something that let’s the organisation avoid the urgent taking priority over from the important
• You may think that you have heard everything you want about the climate crisis, but if you are not personally busy in some way solving it – you have not thought enough.
• There are people who move from denial — through “OMG!!!” — directly to despair, without pausing for a second to actually solve any problems.
• Population of a society has two mail equilibrium states: high birth rates, high death rates and large families (to compensate/buffer) and the opposite (low/low/small).
• By mid-century, Africa will be more populous than either China or India. By the end of the century: than those combined. Will change the relationships on the planet.
• Current rate of adding pollution to atmosphere: 90 million tons per day. The total amount of extra energy from sunlight trapped in atmosphere (and warming the planet) equals to about 400,000 Hiroshima bombs per day.
• Inequality is good, because it is the flip side of some of the basic incentives that create progress in capitalist system. But you should think about it similar to inflation and hyperinflation – the latter destroys too much value for the overall system to continue operating.
• 93% of new income since the last recession in US has gone to top 1% – this is a fact, not Occupy Wall Street slogan.
• We don’t ever have perfect information, we can not argue that maximum liquidity is a goal in itself (if you look at issues with automated trading) – can conclude the standard model is becoming obsolete. Many people think behavioural economics will give the next answers, but it is above my pay grade to answer this one.
• “Will to act is in itself a renewable resource”
• Both capitalism and democracy are examples of the massively parallel processing. (as a side remark, both Adam Smith‘s Wealth of Nations & Declaration of Independence were both in 1776). And both systems have been hacked on the way.
• Inventor of GDP explicitly asked for it not to be used as a metric guiding economic policy. Leaves out both negative and positive externalities: pollution, inequality – but also, education, infrastructure, where the resulting return flow over time is not accounted for.

Guests: Ari Emanuel and Patrick Whitesell (Co-CEOs of William Morris Endeavor)

• Hollywood talent agents for Matt Damon, Seth McFarlane, Ben Affleck, Denzel Washington, Jessica Alba, etc etc. Ari is the prototype for Ari Gold in Entourage.
• Co-CEO setup works if both are able to leave their ego behind. You need a partner to trust you, but also to go out and kill someone who gets in your way if necessary.
• Hollywood is a meritocracy: everyone starts in the mail room
• Culture-wise, there is no such thing as “a merger”: one side eventually wins, the other loses. Taking over a larger/older company works great if they have no strong leadership or culture – easier for you to win.
• Walk the walk – you can’t ask someone to work hard, stay curious, keep standards high if you don’t.
• Getting over barriers, working hard, having difficult conversations… these should be not thought as “difficult things”, these are everyday life. [Ari] was brought up in a way that when others “fight” for his family it was “having a conversation”
• Content (and those who represent them) grow and win, distribution is the part that is changing. (And it used to be the main asset only in 1995!)
• Record labels suffered from mishandling Napster, but over time the value of their catalogue only increases.
• Old model still feeds the business today: no new media channel supports spending $16M on a single pilot yet (Homeland)
• Netflix is just another cable channel (for content producers)
• Which clients do you want to represent tomorrow? (Cooking has gone from shows to books, TV channels, celebrities… and now tours!)
• Silicon Valley did not respect content, but has notably changed. Understands the need to differentiate their tech with content – and the latter is very hard to produce, needs a lot of cash and talent. We are still way away from a $100M sequel to Batman getting crowdsourced.
• Old saying in music business: “you gotta have ears”. Hard to be any more specific about the recipe for success (as an agent)
• If you look at social media & tastemakers closely, you find that it is still the same, professionally managed celebrities who drive the largest parts – with occasional exceptions of internet-only phenomena.
• Raised money from Silverlake private equity for technology & Valley support strategically – did not any meadia-focused PE firm.
• Since 2008, major studios have gone from 200 -> 110 big movies per year. As an agent you can choose to syndicate an idea through a studio (who then fills global distribution channels), or go direct: licence all markets/channels yourself. Latter is much more profitable for the content creators.

Private Equity Club BBL

Guests: KKR Capstone team

• Operations team designed for scarcity: if you have 60 people globally and 80 portfolio companies – drives the right allocation of resource where it can really add value
• Consultants work is to demonstrate opportunity and then get out. Private Equity operators follow through execution – work in a team of portfolio company, 18 months+

By “the topic” I mean the ever-worsening pain: humans’ inability to cope with their email load (and what the cultural implications of that are). As someone whose Mail client showed the following unread counts (aka “unprocessed”, really, in my personal methodology), I am obviously part of this global problem:

The shame of not answering in time (or ever) used to follow me for years, and the sorrow of not getting responses to many e-mail I send was there too, as a counter-punishment. I say “used to” and “was”, because it really does not any more.

Back in 2010 I scripted the following auto-responder message that for a while went out in return for every single incoming e-mail:

Hello,

I have adopted a new — if I may say, “zen” — approach to e-mail.

Imagine me as a buddhist monk sitting on the bank of a mighty river, watching the thousands of weekly messages flow by. Several times a day I reach into the river and fish out a dozen or a few of them. Others swim by unstopped as there is no way I could fetch them all. Yet, deep down inside I know that the messages that really need to find me, will. One day.

However, if you’d like to be more sure of my timely response to your message, please add me on Skype. My Skype name is “sten” and I’m increasingly online via our desktop and mobile clients (http://www.skype.com/intl/en/get-skype/).

Talk soon,

Sten

In a few weeks some of my frequent correspondents got too annoyed and I reconsidered the “auto” part: generating 1:1 matches for every incoming e-mail doesn’t help towards solving the overload for anyone, unfortunately. But 3 years later I still happily stand by the contents of this message for the most part, with a few comments and clarifications learned since:

• My diligence and discipline of getting back to people on Skype chat is still far better than e-mail, but there still is no universal conversation management tool for all. In business environment there was more convergence and control, but being back at school I need to juggle people and groups whose preference of communication channels can also vary between Facebook, LinkedIn, WhatsApp, Kakaotalk, GroupMe… Each context switch runs the risk of reducing response rates further.
• One thing for sure, e-mail is still part of this attention portfolio, but not it. The occasional excitement about some new UX paradigm with the promise to be more effective in handling e-mail comes and goes, but for example when the Mailbox app makes me swipe a few hundred (or 17,000?) times on a smaller screen as opposed to arrow-keying around on a larger one – the productivity benefit is too incremental to really win a notable share of my day or toolkit.
• Most importantly: it is pointless to be mad at people who don’t respond to you in expected time, if at all. 95% of the cases that piss you off would be just a fundamental attribution error in action (assigning blame not on situation but on character). If it burns, ask again. And better yet – think what you can do to make your first message as easily actionable as you can: short, crisp, with a clear action ask or response expectation, if any.

Or, as the author concludes on a very constructive note in that NYT piece:

So, here’s my idea. Those of us waiting for replies shouldn’t be so quick to leap to negative conclusions. It’s unlikely your best friend suddenly hates you. Or you’ve alienated a co-worker. Reach out again.

And to those who habitually don’t respond, try sending a quick e-mail just to say you can’t answer now. And if you really mean no, say no. Most of us can handle rejection. We just can’t handle not knowing.

 

Compared to the build-up from the first half of the Sloan program, the theme of the Winter quarter felt like more of everything. As the days get shorter and weather chills (mind you, we’re still talking in Californian terms here, you know the kind where for entire two mornings per year you can see slightly frosted windshields at 8am! *gasp*) people get indoors: more academic units, more study group work, more external guests and more parties.

As my friend Juliano says, a program this short and intense has no middle: there is the excitement of the beginning and and then you’re starting to wrap up. Our core Finance class in Winter was the last where all Sloans were together in the same lecture. Beyond a few class meetings and a social event here and there (and average participation of either is usually way below 100%), this will not happen again before the graduation dinner.

To compensate, our social chairs went out of their way in Winter. I am slightly biased here, of course, as Ede held one of the seats on our Partners side, which meant I could see the effort and pain behind the scenes up close and that we spent exactly zero Fridays outside the circle of classmates. But what events those were: the eclectic European night, a full Brazilian day (modestly called “lunch”) which brought the first truly warm sunshine out into the bossanova rhythms, kimono-clad and sake-soaked Japanese evening, musical and overeating-risked Chinese New Year, a cunning Belgian beer tasting…

There was also a bit of real winter in these months (as per the meaning of the word I grew up with) as people made proper use of the renowned geographic feature of Silicon Valley: you can get to the Ocean in an hour, and to the snowy slopes in three. As a tip to future Sloans, forget the hotels at Lake Tahoe and rent one of the massive private homes up there. With 5-7 bedrooms you’ll end up housing 20+ latecomers who can survive on couches, and the joint cooking events, conversations shouted over karaoke backdrop, improvised full on cocktail bars, games of pool and poker, champagne in the jacuzzi and the sight of classmates’ morning attempts to complete various financial analysis papers before hitting the slopes again will remain with you as some of the better memories of the year.

Academically, and going beyond Professor Strebulayev’ infinite source of amusing quotes, I will think back of this quarter mostly in terms of finance (in which I took four different classes - and loved them all) and juggling various study groups in parallel to deliver papers and presentations. With one team we built a business plan and pitched a crowdsourced service for proofreading and language learning feedback to dyslexics, foreign students and white-collar immigrant workers. With another team, we designed an elaborate proposal for taking a real public company private, shifting their product portfolio for higher exit multiples, levering up the non-existant debt, restructuring operations (yup, the founder’s private jet and golf tournaments need to go) and getting out at 10X of our money in 5 years. With third group, we pre-analysed and went through 6 real startup pitches along with a real VC mentor and decided to maybe just to give money to one of them.

Despite of the chaos of trying to schedule ~20 people into these different groups at conflicting times, I really enjoyed actually doing stuff (as opposed to just discussing in class) with my MBA peers. The ease and breakneck speed at which almost anyone in this school can deliver complex quant models and high quality analysis still amazes me. And then you can pull pretty much anyone on stage with a 5 minute heads up to present the outcome with coherent, engaging story line.

And finally, the other thing that peaked through Winter of course was the incoming flow of guest speakers. It is one of those things where you have to actually remind yourself that having Eric Schmidt run a class a day after he returns from North Korea or just the weekly routine of dropping a lunch meeting with one innovator or founder you admire for another one due to the scheduling conflicts is not quite normal way of life of most people in your field and with your interests in the world. What used to be almost as intimidating as exciting upon arrival has now has become “yet another day at the Farm” for us. These interactions are to be grateful for.

As Mike Maples Jr put it (yes, when meeting students on campus, of course): “Part of the Stanford miracle is the flow of the amazing founders walking through (I had McNealy, Osborne, Jobs in class as an undergrad…) – you sit there as a student and you say, hey, these guys are not that much smarter than me – I could do this as well.” This way of running a school interwoven with the industry is all about confidence building. And I believe we came out of the super busy Winter more confident than ever about our ability to manage time, deliver to deadlines, not to leave you peers behind, prioritise family and social life and get anything done you set your mind to – in the started Spring quarter, and especially beyond.

Head down in two inches of readings for this week and a fresh flow of first written project deadlines, like a two-degrees-deep analysis of your friends and advisors social graph or a set of regressions to be run on profitability data of a bank who has no clue if there is any connection between the demographics and profitability of their customers.

For a little different entertainment in the Sales Orgs class we are running a simulation game where you need to manage yourself through the pipeline as a sales rep of a medical devices company. Four virtual “years” in four weeks. After a miserable first year (I hit merely about a quarter of my quota – should have read the manual before I tried to just figure the game mechanics out for 2 “quarters”) I look forward to the Tuesday class from a much more comfortable position after “year” 2. I guess the hours spent as a teen with Civilization and the likes can sometimes pay off?

GSB hosted a fun networking event this week called “Fewer than 300″ – bringing people in from over 30 companies who are about to grow out of their startup phase (but are yet to break 300 employees), but have raised money and shown traction and still are just burning of enthusiasm about what they are doing. Think of the likes of Uber or Nest or Visual.ly. Good people and good conversations.

Covered in this issue:

• Analysing network centrality measures and deriving composite relationships from simple a matrix
• Using Bass diffusion models for new product adoption predictions
• Handling variability in processes (from job shops to continuous flow)
• Economics of selling SaaS subscriptions and merging sales teams after M&A
• More team-first entrepreneurial models
• History of Sloan Program at Stanford
• Data Science learnings from LinkedIn and other Greylock companies
• Guests: Vinod Khosla, Mark Leslie & part of Veritas exec team, Corey Leibow, Eric Botto, George Parker, DJ Patil

OB322: Networks (Hasan)

Article: Network Analysis Toolkit (see Centrality)

• Degree Centrality
  • Indigree centrality: number of incoming connections to a node. In friendship terms, this is popularity, “how many people nominate you as a friend”
  • Outdegree: # of outgoing connections. Shows level of gregariousness.
• Closeness Centrality
  • aka “access”, average distance from the focal node to all other nodes in a network. How many hops do you need to any information in the network (on average), or how likely are you to spread a disease to anyone else.
• Betweennes centrality
  • Degree to which a node acts as a bridge between other nodes in the network. Looking at all pairs of nodes, to which degree the focal node exists on the shortest paths between 2 nodes.
  • Can get complex to calculate, but fortunately recent research has shown the local betweenness (based on only the focal nodes direct connections and connections between those) is highly correlated with the larger betweenness.

Article: Informal Networks: The Company Behind the Chart

• Managers often pride themselves on knowing how the informal networks operate in their org (who confers on technical matters and who drives political conversations over lunch). What’s startling is how often they are wrong. They may be able to diagram social links of 5-6 ppl closest to them, assumptions outside of immediate circle are usually off the mark.
• For good overall picture, diagram three types of relationship networks:
  • advice network (prominent players on whom others depend to solve problems and provide tech information)
  • trust networks (which employees share delicate political information and back each-other in a crisis)
  • communication network (employees who talk about work related matters on regular basis)
• To survey people for network mapping needs, sensitivity and willingness to answer questions varies wildly across cultures. Example questions:
  • Whom do you talk to every day?
  • Whom do you go to for help or advice at least once a week?
  • With one day of training, whose job could you step into?
  • WHom would you recruit to support a proposal of yours that could be unpopular?
  • Whom would you trust to keep in confidence your confers about a work-related issue?
• We don’t usually ask negative questions (“who do you hate?”, “who are you trying to avoid?”) – but if you can, these can be extremely predictive of actual behaviour
• If possible, interesting to map the managers impressions of answers to above while at it (“whom do you think Steve goes…”) to be able to compare the accuracy. Also, cross-check answers: if John claims to talk to Mary, dismiss that link if Mary doesn’t mention ever talking to John.
• Frequently, senior managers presume that formal work ties will yield good relationship ties over time, and they assume that if they trust someone, others will too. Not true.
• To shape and extend informal networks: a manager picked up a bills for “power breakfasts” between any two employees, as long as they were from different departments.
• Counterintuitive: on a enduring/qualitative VS event based scale, people are accurate explaining enduring ties and completely wrong about events (empirical research done on cruise ships, having people recall specifically who did they exchange emails with most during last week). Know much better who are your friends, who do you ask for advice.
  • Surveying for events based data has to a large part become redundant anyway – for a huge part of events you can get precise digital data (emails, calendar entries, calls, etc).
• Cultural differences in surveying: a German study where subjects where offended by “how do you even dare to ask about my friends, they are important to me!” VS in Italy where everyone claims to be friends with everyone and analysis shows too few distinct patterns

Software for network analysis: UCINET (Professor’s comment: “I decided to use UCINet over R or NodeXL for two reasons. I think the learning curve for R is a bit too steep for such a short period of time- even though there are native versions for the PC and Mac. Also, while NodeXL is useful for learning the basics of network analysis, it is much less scalable than UCINet.“). Sample output:

MULTIPLE CENTRALITY MEASURES

--------------------------------------------------------------------------------

Input dataset:                          projecthelp (\\psf\Home\Desktop\projecthelp)

Output dataset:                         projecthelp-cent (\\psf\Home\Documents\UCINET data\projecthelp-cent)

Treat data as:                          Auto-detect

Type of scores to output:               Raw scores

Network projecthelp is directed? YES

Value of Beta was:                      0.193373281787215

Principal eigenvalue was:               5.14548851218693

Centrality Measures

                 1        2        3        4        5        6        7        8        9

            OutDeg    Indeg OutBonPw InBonPwr Out2Step  In2Step OutEigen  InEigen  Between

          -------- -------- -------- -------- -------- -------- -------- -------- --------

  1 6827     0.000    5.000   -0.000  763.244    0.000   12.000    0.000    0.427    0.000

  2 6828     4.000    5.000 1021.042 1209.812   12.000   16.000    0.528    0.679   22.333

  3 6829     4.000    5.000  907.382 1160.921   15.000   12.000    0.469    0.652   54.655

  4 6830     6.000    9.000  988.072 1541.914   13.000   16.000    0.510    0.864   43.464

  5 6831     3.000    4.000  505.190  638.731   11.000   11.000    0.261    0.358    0.000

  6 6832     7.000    3.000 1094.473  382.783   16.000    8.000    0.564    0.214   78.583

  7 6833     3.000    4.000  813.978  946.402   13.000   14.000    0.421    0.531    1.417

  8 6834     8.000    7.000 1663.193 1543.478   15.000   15.000    0.860    0.866   38.424

  9 6835     2.000    7.000  390.509 1276.391    9.000   17.000    0.202    0.715   26.233

 10 6836     1.000    2.000   29.138    6.863    3.000    3.000    0.014    0.002    0.000

 11 6837     2.000    2.000   35.773   18.284    3.000    4.000    0.017    0.009   34.000

 12 6838     5.000    2.000  976.164  645.099   13.000    9.000    0.505    0.363    2.119

 13 6839     9.000    8.000 1934.070 1544.184   15.000   16.000    1.000    0.866   67.724

 14 6840     6.000    8.000 1271.704 1781.501   15.000   15.000    0.657    1.000   42.655

 15 6841     4.000    2.000  672.071   77.347   17.000    6.000    0.347    0.042   96.750

 16 6842     5.000    0.000  856.711   -0.000   12.000    0.000    0.442    0.000    0.000

 17 6843     9.000    6.000 1608.924 1140.729   14.000   15.000    0.831    0.639   34.950

 18 6844     5.000    4.000  925.960  725.724   12.000    9.000    0.478    0.407   16.048

 19 6845     7.000    3.000 1302.305  599.769   13.000   14.000    0.673    0.336    7.895

 20 6846     1.000    6.000   76.514 1208.649    3.000   15.000    0.039    0.678    1.750

 21 6847     3.000    2.000  145.513    6.862    6.000    3.000    0.073    0.002   35.000

----------------------------------------

• People who others come to for project help (InDeg) and who go to how many people for help (Outdeg).
• Out2Step & In2Step – network size (how many people you’re connected to)
• Eignevector centralilty – measure of importance of the node
• Betweenness network centrality – extent of bridging
• From a simple relationship matrix (“parent of: yes/no”) of relatives, you can manipulate out two-directional relationships (transpose for “child of”), compound relationships (=MMULT(first table, first table) for grandparents… Think of additions and subtractions which filter out only aunts, uncles and cousins. All from the same simple binary matrix.

MKTG 365: Marketing Analytics (Narayanan)

Case: Sony
Topic: Sales Forecasting

• Think of data modelling as mapmaking: the ideal map of a city is life-size 1:1 ratio, but in that case it would not be useful as a map any more. What is the reasonable simplification trade-off for a given use case (pocket map? wall map? digital & zoomable?)
• Bass Diffusion Model uses qualitative insights on the intrinsic differences in adoption groups. Genesis in epidemiology literature (since 1969), but used for new product adoption predictions: innovators, early adopters, imitators. Works for single-purchase situations (cumulative adopters each buy once), less useful for frequent repeat purchases. Can be applied to subscriptions, given there is usually one purchase decision (and automatic from there on) and easy access to data on new/returning users. Often useful to apply on entire product category prediction (VCRs launched into market), rather than single products.
  • Advantages: easy to implement, uses simple and available inputs, easy to interpret parameters, parameters can be compared across products & markets.
  • Intuition: In every period, some innovators (p) will adopt. Similarly, some imitators (q) will interact with past adopters – and adopt. Cycle through the periods, applying the p & q coefficients to the remaining market potential (N) from the previous period:
    • N=100, p=0.03, q=0.1
    • Period 0: Innovators = 0, Imitators = 0, Total = 0, Remaining potential = 100
    • …
    • Period 2: Innovators = 0.03*97 (Period 1 remaining potential), Imitators = (0.1/100)*3*97=0.29, Total = 3.2, Remaining potential = 93.8
  • The resulting curve looks a lot like the product life cycle curve (slow start, acceleration, plateau and an eventual fall-off)
• Article: Modeling Multinational Diffusion Patterns: An Efficient Methodology (Gatignon, Eliashberg, Robertson 1989) – showing innovation and imitation coefficients differ notably across different markets (maybe you launch in Sweden, even if it is a smaller market than Germany, but more likely to adopt early & fast). Recently these coefficients have jumped a lot in absolute values thanks to internet & social media.
• To acquire estimates, you might get p & q values readily calculated from Gartner, or raw data on similar category sales (VCR data when launching CD players) from Forrester and the likes.
• Bass is useful for sanity checking of projections created using other means. For ex: a student project showed how the p & q values of 3D TVs sales projections were larger than any other product category in history (e.g being order magnitude off from the sales later shouldn’t have been such a surprise). There are products of course, that can change p & q dramatically (iPad).

OIT 262: Operations (Plambeck)

Topic: Variability and Impact on Process Performance

• Arrivals (of flow items) is stationary when shifting a constant time window back and forth (like 1 hour in 8 hour day) does not change the expected number of arrivals.
• Two types of utilisation for inventory build-up analysis:
  • Implied Utilization: demanded resource capacity, represents the load you are trying to put on the system (may be >1)
  • (Actual) Utilization: flow rate resource capacity (minimum{Demand, Process Capacity}) determines actual bottlenecks, accounting for inventory buildup. Must be <=1.
• For queue analysis need to look at a period of time in which the arrival rate and service rate are both stable.
• Coefficient of Variation or CV = Standard deviation / Mean. Unitless measure of relative variability.
  • When std dev = mean, CV=1: indicates exponential distribution (Poisson Distribution). Can be empirically measured and mathematically proven that this distribution naturally arises when all arriving customers (or flow units) are independent and the population is large.
• Two kinds of CV per queue: for interarrival time (time between flow units arriving) and processing/service time (time it take for a server to process a flow unit)
• The higher the utilisation, the stronger the benefits of pooling (for ex if you have a long line for tourist class checkin and you let even just one customer use the empty business class check-in desk, everybody in the long line wins in waiting time)
• Queueing Theory was created by Erlang to describe Copenhagen telephone exchange.

Topic: Batching and Product-Process Matrix

• Capacity at the bottleneck is extremely valuable (1 hour lost there = 1 hour lost in entire process) -> make batch sizes large
• Capacity at non-bottlenecks is free -> make batch sizes smaller to minimise inventory.
• BOOK: From the American System to Mass Production, 1800-1932: The Development of Manufacturing Technology in the United States (Studies in Industry and Society) (Ford and others innovating operations)
• Main process types:
  • Job Shop – general purpose resources, skilled labour, functional layout, complex routing
  • Batch – simpler routing, processing many identical units at once
  • Cell – equipment grouping in “cells” focusing on different products, skilled workers responsible for multiple machines and activities
  • Assembly line – specialised equipment, single item flow, workers repeat short, simpler tasks
  • Continuous flow – constant flow of basic material (cement, iron, chemicals, paper)
• Process types can be mapped diagonally in matrix, with the other axis being: Low volume / unique products -> High volume / few types. Problem if you go out of the diagonal (for ex, try to do high volume in job shops or unique products in continuous flow).
• Continuous flow (among other benefits) most energy efficient – no need to boot up systems (for example heat and cool). Many industries drive towards that – even historically batch processed beer now more flow-y (add sugar from bottom and take alcohol from top of barrel constantly).
  • New challenge: dynamic adjustments in continuous flow. A chemical plant in Texas that can adjust process with 15 minute intervals depending on real-time energy prices.
• 3D printing’s huge promise: no setup time before any unique item
  • BOOK: Fabricated: The New World of 3D Printing
  • Some featured companies: Shapeways, Bespoke (now with 3D Systems), Organovo.
  • Stanford Product Realization Lab.

STRAMGT 351: Building and Managing Professional Sales Orgs (Lattin/Levine)

Case: Mercado (Stanford E296)
Article: SaaS Economics – Part 1: The SaaS Cash Flow Trough
Guest: Corey Leibow, ex-CEO Mercado

• This post provides SaaS entrepreneurs with an Excel spreadsheet model and graphs that show the cash flow trough that happens to SaaS, or other subscription/recurring revenue businesses that use a sales organization. These kinds of SaaS businesses face a cash flow problem in the early days.
• When to cap the compensation: do you have any supply constraints? Do you expect problems if a large part of sales blows through targets?
• In most sales orbs individual pay is very transparent, winners are celebrated very publicly
  • How do you celebrate (and motivate) #2?
  • Celebrate… and double the quota next year
• Existing sales rep selling more is amortising fixed cost: their each next sale per period is increasingly cheaper than if a new rep came in to do that.
• Top sales guy is probably a “lone wolf” contributor by character. Usually becomes unhappy when moved into management of training to help others sell more – takes them away from sales (that they are great at) themselves
• Account Ececutive as CEO of their territory (controls marketing, sales engineering…)
• “Sales compensation is like Rubik’s cube: you get one side all pretty and aligned, look on another to see the mess of incentives you just created”
• Compensation transition: transfer really happens once new incentives have reached comparative absolute levels.

Case: Veritas 1999
Guests: Mark Leslie, Michael Sutnick, Paul Sallaberry

• Merger “efficiencies” (aka people you can/have to get rid of)
  • General & Admin: ~40%, every one expects, will be peaceful – RISK: 2/10
  • Engineering: 0%, most people go to work, same job, same boss, more to do – RISK: 0..2/10
  • Marketing: external opportunities to save (ads, PR, media buys, agencies) – RISK: 3/10
  • Sales: ~40% of management jobs, but NOT peaceful, very competitive people, top-line revenue at stake – RISK: 10/10
• Not integrating and kicking the can down the street will polarise separate cultures & conflicts under same roof
• DO you want a very good company or a category of one? Different ways to.
• Attacking a competitor with bad customer support reputation: dial their helpline for quiet background music for your sales meeting with their client.
• Clean cut, back-room designed changes are painful, but eventually you wish you did them (as opposed to long open interview processes for people to stay in their same jobs – will p*ss off top performers first)

Article: Keeping your sales force after the merger (McKinsey Quarterly)

• In 3 years following a merger, mere 12% of companies grow more quickly than before (even though revenue growth has usually been touted as one of the reasons of a merger)
• Offsetting mere 1% of revenue growth decrease could require exceeding cost cutting targets by 25% – massive lever. Company can always go back and cut costs, but revenue is fragile and much harder to restore.
• Sales force needs to be pampered as the first line communicator to customers on the benefits of the merger. Win over the sales force first in internal comms. Anything less than direct and immediate comms from CEO is too little and too late (example of a CEO who made it to some office on a road show 2 weeks after merger, and 9 of 12 sales people there had already accepted offers elsewhere).
• Uncertainty of a merger creates a perfect environment for competitors to attack: best people headhunted, customers pitched…
• In two-thirds of all mergers, new CEO is chosen before deal announcement Same urgency should be applied to sales manager.
• In case of dragging regulatory review or other obstacles, establish a “clean team” that can access data on both sides, proceed with integration planning and is legally limited from sharing any data received with the other side should the deal fall through.
• Consider short-term extra sales incentives (extra +10% on existing commissions on meeting sales targets during three months of merger, additional +15% for exceeding targets during 6 months after close…). Counters the inevitable distraction of the merger, not only good for bottom line (even if slightly overpaying for sales work), but creates a lot of positive energy and signals externally that the company is staying on the ball.
• Set up a temporary management structure, a “war room” for post-merger sales & client relationships leadership. Gathers data, generalises issues, has a direct line to exec team, has a licence to remove red tape and make fast decisions, should any concerned customers risk churn (or sales people are lost on important accounts).
  • Important to put credible, respected and decisive people on this team, e.g your best sales guys. Even if they are not actively selling for 3-6-9 months, the overall benefit to company is larger.
• Map all accounts on a graph: Account (or sales rep) vulnerability / Account profitability (not revenue)
  • HIGH-HIGH: Senior exec sponsorship, active intervention, personal phone calls & visits with senior manager follow-up
  • LOW-HIGH or HIGH-LOW: Personal calls, follow-up from account teams
  • LOW-LOW: Personal letters and scalable speeding of goodwill

BOOK: (Chapter 15 “Building a Potent Sales Force Culture” from) The Complete Guide to Accelerating Sales Force Performance: How to Get More Sales from Your Sales Force

• In sales culture, norms define how an individual should behave when they are faced with a choice, and values how he/she should aspire/desire to behave.
• Culture-defining sales management choices:
  • Short/long term
  • Cost/Market opportunity (accept high cost of sales force to capture more market VS drive for maximum efficiency; good ex: wine’n'dine expense control)
  • Asset: customers/employees
  • Control/Empowerment
  • Incentives based on Activity/Results
  • Individual/Team success valued
  • Adaptive/Stable
  • Hunting/Farming
  • Hiring/Training
  • Extrinsic/Intrinsic Rewards (financial rewards, acknowledgement VS survival, achievement, social affiliation, ego, power)
• Elements that set sales force cultures apart from other functions in company:
  • Salespeople usually work alone, meat managers infrequently and some other peers maybe 1-2 times a year at a larger conference. Many weak & no strong ties.
  • Work is largely unobservable, results usually measurable and performance is public. Feeds competition and makes collaboration harder.
  • Sales forces tend to be non-hierarchical.
  • Most energy directed externally.
  • Job involved considerable rejection, making rewards and recognition more important.

STRAMGT353: Formation of New Ventures (Holloway/Morgridge/Chess)

Case: ProjectSHED
Guest: Eric Botto, co-founder of ProjectSHED
Topic: team-first (as opposed to idea-first) entrepreneurship

• Case on 2 MBAs and 2 Sloans getting together in this very class and working for 6 months to find a new business problem together + eventually building a successful company doing mobile cell interconnect networks out of it.
• Compared to search funds: you are searching for an idea, not for a company
• Specifically looking for a problem (not an idea!) that will emerge 2-3 years later and none in the industry is well positioned to address for some reason. Agreeing on attributes that are important to us before:
  • “We want to be pre-culture, the founders who define the culture”
  • “Large and large+growing industries only, because high tide raises all boats”
  • “B2B only, we want blue chips to pay our bills”
• The genius engineer sitting in a lab without a business partner is a myth: in Stanford all of them are talking to VCs directly, not via business school
• Going to industry conferences worked, picking people’s brains on what are the unsolved issues that annoy them
• In early days of Cisco, sales guys prohibited to go to conferences, only engineers. This way you can find out about what the customers really think about the problems of your product – sales guys go into the mode of reconvening clients the problem is not there.
• Team-first approach means you slot founder into senior roles in an unknown industry. How do you hire experts under them? How far will they scale?
  • Have to be intellectually honest about splitting roles. Agreed that each person is willing to take any role, no matter which titles, as long it is better for the company. Were looking for an external CEO, but the real luminaries were not hungry enough and founders needed to step up.
• Potential motivations for very senior advisors to get involved in startups:
  • doing something useful after retirement
  • giving back
  • way to still stay in touch with your friends in industry
  • stay relevant/up to date or even position yourself still on the cutting edge even after a long time in business
  • have young and eager teams execute your ideas
  • investment opportunities
• “The only way to change a culture [after designing it in startup] is to have 3 bad quarters”
• When you are solving an issue that several existing players are not positioned to solve, your important goal is still to align their interests with yours. ProjectSHED got all 3 cell tower operators to invest almost $100M together, and their CEOs joined the board. That said, this kind of board can be misaligned with founders on financial exits (vs their strategic long-term issues)
• After exit took some time off, and the same team started a new search. One person had moved to Seattle and ended up starting Tango Card (other helped fund). The other three started Slice.
• When considering how aligned are your incentives with a strategic investor, think about today (at inception), but also 3 or 5 years down the road (exit)

Case: Sun Microsystems, first business plan
Guest: Vinod Khosla

• If you can’t afford to iterate on prototypes enough, maybe you can find a customer who is happy to buy a half-made product because they want to make it better themselves? (Sun selling to CS departments of top universities)
• With competitive positioning as a startup, think not just low cost of entry, but also low cost of exit in your value prop. Should the client fear getting stuck with your tech if it doesn’t perform?
• When you find someone with tech you need and instead of licensing it you manage to hire the person: you get the goose, not just the golden egg it laid.
• How do you motivate your suppliers to build things that match your plans in specs & timing? Drop or limit (6 month lead?) exclusivity, allow them to develop better tech and sell to anyone.
• Make sure you’re asking the precisely right question when devising plans – surprising how often that does not happen.
  • For example: do you win the largest customer ever or do you actually want anyone but your largest competitor win it? Or maybe even have them keep in housee and not buy from anyone? Resulted in Sun giving their tech to Computervision so they wouldn’t buy from Apollo.
• BOOK: On Being Certain: Believing You Are Right Even When You’re Not (Identifies a gene that changes risk perception, same basic mechanism that makes people religious).
• Paper: What makes entrepreneurs entrepreneurial? (PDF)

Sloan BBL

Guest: Mark Leslie (ex-CEO, Veritas; Lecturer at GSB)
Topic: Leadership learnings from building Veritas

• Leadership learnings from taking Veritas from <$100k to $1.5B company (Fortune 1000) in 11 years
• Are you a proprietor or a steward as a leader? Do you ask “what can you do for me?” or “what can we do together?”. You can actually learn this about a new person you meet via the subtitles of language & vocabulary they use.
• Be visible & be invisible. People expect to see their leader – for many years I did not even have an assistant (no-one had to give anyone else a reason to talk to me). Yet, remain humble – it is not about you, but your role.
• Glamorous is opposite of leadership. You can be a comrade to followers, but never one of them – it is a lonely job. When a CEO gets up at 3am to be on TV program for another timezone, it is not unlike an engineer chasing bugs through an all-nighter. Nothing glamorous, just each doing the job that the other can’t for the company.
• If you’re loyal to one, you’re loyal to none. CEO can’t have any favourites, mission the base of prioritisation above all else.
• The further away from the problem the less you know. Very top knows nothing about anything – get the direct source do a decision making forum (e.g in site downtime, have an engineer in exec staff meeting, not reports through command chain).
  • The higher you are, the less you should decide. The employee who came for a decision, but whom you asked to decide themselves and own the consequences will leave the office 1 inch taller.
• The more power you give away, the more you have. Allowed anyone to call in and listen to exec staff meetings – over time the novelty wore off and business returned to normal + more trust that there are no secrets behind those meeting room doors.
• Trust to be trusted. Leader needs to take the first step here – and know that when you trust, you will be betrayed at some point.
• You can do even the hardest things (firing, negotiating, demolishing competition) with grace & caring. That said, when someone leaves, it is far more important what those who remain around you think.
• CEOs job is to fix broken stuff, which presumes they know the truth about what’s broken.
  • Beware of the value of your voice – an eager person might execute on your casual comment (that the hallway would look cooler with green walls)
  • Always honour the messenger
• In the darkest days, show the light in the end of tunnel, but not false hope
• Only the paranoid AND courageous survive. (Only the first don’t do much)
• Character is not unlike IQ. If you don’t ask someone in a IQ120-job with 90 score to “change themselves”, why do you hope someone poisoning office politics will change?
• If you fail market expectations for a while, you might get fired. If you listen to your investors on how to run your business, you will get fired for sure.
• For strategy planning, get the smartest people in the room, irregardless of their titles, and ask them to come up with the stupidest ideas for starters.

Sloan BBL

Guest: George Parker (Professor, former head of Sloan Program)
Topic: History of Sloan Program

• Fundraised from Alfred P. Sloan personally in 1956 to get 6 PhD students and 6 hotshot executives to sit together for an intensive 9 months education experience – so that one side becomes more analytical and the other more practical. Asked for $100k to support PhD & faculty (execs paid by their companies). Sloan asked after 1 hour: “when can you start?”.
• First class of Sloan Fellows graduated in 1958. The entire program was taught around a custom made round table. Zero electives, one section, identical course for 15 years.
• Alfred P. died in 1968 and the new President of his Foundation stopped the support of PhD students. (The program by then 35 people, still 6 of them PhDs).
  • Went to all executives & all sponsored. All US, virtually all men.
  • Since then there has been no contractual or other obligation to keep the Sloan name. And then, what people who don’t have a good answer say: “We’ve always had that name”
• All PhD programs are hard to get into, but essentially free at Stanford – they are accounted for (there is tuition), but you get a matching scholarship.
• 1973 Parker became director of Sloan program
• 1975 – the first degree granted: MS in Management. Intended to highlight the difference with MBAs, based on what is needed to get in (2-5 VS 10-15 years of work experience) and what happens when you graduate (entry level jobs VS next level of leadership). Also, first electives allowed (1 in Winter, 1 in Spring)
• Class size grew to 57 in the old business school campus, and in Knight Management Center could be extended to 67 (2012) and 80 (our class). In comparison, MIT Sloan program went to 2 sections in early 2000s and is now at ~120.
• Tradeoff managed over time: lots of electives are the enemy of a cohort.
• Changes in cohort composition over the years:
  • There are two schools at Stanford where the share of women is below society representation: Engineering and Business. MBA program has today 35%, Sloan around 20%. Hoping these both to grow to and above 50%.
  • Addition of international Fellows
  • Non-profit managers (always had business and government)
  • Self-sponsored. Corporate sponsorships are inevitably falling along with the notion of lifelong careers in a single company (who then would invest in return for your 20-25 year commitment)
• You can design an academic program for size or caché. Think of Ford versus Aston Martin, even if both good cars, how do you think differently about them.

CS547: Human-Computer Interaction Seminar

Note: full archive of videos for this course available here.
Guest speaker: DJ Patil (Data Science @ Greylock, ex-eBay, ex-LinkedIn)

• BOOKS: Two free e-books by DJ:
  • Data Jujitsu: The Art of Turning Data into Product
  • Building Data Science Teams
• Experimental stuff: http://engineering.linkedin.com/linkedinlabs/
• Hollywood (with Moneyball movie) helped the data science hype a lot
• 20,000 planes in the air, 43k engines spinning right now. Every engine recording 290 parameters 16x per second. Could the engines tell us when they are going to fail, change flight patterns, even teach us about climate change?
• Most of the most sophisticated data products don’t have a display, let alone draw graphs.
• Building data products needs to be a strategic arc – do The Dish before Mr Everest
• The heavy lifting of organising incoming data is still a huge problem
  • Unstructured data that users still expect to see unstructured (entering a company name, as opposed to picking from the longest drop-down in the world), but gets carefully mapped to structured data in the background
• Seeking out commonality during the process of data collection changes the engineering task completely: you are now building not for ambiguity, but with intent (to solve particular additional problems)
• Simple matrix adjacency problems become tricky at scale: maybe your “recommended products” query could be run only once a quarter on Oracle, then you try Greenplum and then you need to build up a completely new infrastructure on Hadoop?
• Data Vomit: Amount of engagement is inversely proportional to the amount of data on the page. When was the last time you tried to click on an infographic?
  • Good filter: what is the daily habit? Is this piece of data something that could have changed from yesterday, or is it more of a yearly report?
• When you put more data out there and you get it wrong, it can be irrationally offensive to people:

• Expectation management via form factor: you behave differently when a household robot brings you coffee and spills on you when a) it is shaped like a puppy or b) a human-like android. Even though their intelligence and functionality can be identical.
• Teflon tech, take yourself out of the blame of the system. Instead of recommending jobs to your users, ask his friends to forward pre-populated jobs to him. Adds relevancy and no-one blames you if they go wrong.
• Zero overhead rule: When did you last go to a training for any tool? You expect the product to teach you. The best example: successful mobile games.
  • Distilling it down aka dumbing it down.
• How do you manage the legal and regulatory limitations to data processing in your software development process? Still unsolved, a lot of this is getting provisioned to data stores, but there are still these plain core repositories. Potential to build a great business in this space.
• The only place where I’ve seen value in truly real time data analysis has been around security and ops (load balancing)

The highlight of the week was really non-academic: we got to host the Class of 2014 for the orientation we received in April a year ago. In a proper student timing fashion, when a day before the event you’re not sure what will come out and some of the slides getting still edited by the speaker at the time he is being introduced on stage – I think we eventually put on an all right show balancing more serious talk about academics and transitions with some glimpses into the less serious side of life we’ve had too. (Some videos of the performance of our class’ house band The Spillovers are online here).

Personally, it was especially heart-warming to hear from a bunch of incoming Sloans that they have found this blog and some other writing from our class useful when researching about business schools, Stanford and even deciding to join the Sloan format in particular. Frankly, I did not consider this as a goal for ongoing writing throughout the year originally. About a year ago I decided to keep posting rather to keep in touch with friends (especially back in Estonia) as well as from the more generic life philosophy that while you’re generating content (such as the class notes) for yourself anyway, you better have a clear reason why you would keep it private if there is any likelihood of someone else potentially benefitting too. So when this actually happens, it is a cherry on top.

Cheers to the 2014-ers and any future GSB colleagues reading this, then!

Covered in this issue:

• Brokerage, trust and reputation in social networks analysis
• Regressions for customer profitability
• Predictable variability in Operations
• Paying sales people well for performance
• Search funds
• Guests from: Jive Software, Sequoia Capital, Brown Robin Capital

OB322: Networks (Hasan)

• Because the forbidden triads break time over time, strong ties (and the overhead cost of maintaining them) are less effective for bridging clusters of networks. (Strength of Weak Ties - PDF, Granovetter)
• Self-monitoring - high scorers are much better bridgers of social gaps, they like to hang out with other people and are able to “chameleon” themselves to be part of different groups. Fare less well in cliques, they don’t want to be there, rarely live in their home town and hang out with high school friends.
  • “Snyder originally developed the scale in 1974 as a 25-item measure. In his original study he found that Stanford University students scored significantly higher on the scale than did psychiatric inpatients, but significantly lower than people in the acting profession.”
  • Online test
• Benefits of being the broker across structural holes:
  • Control: blocking or distributing who gets what information
  • Information: access, timing, referrals
• BOOK: The Double Helix: A Personal Account of the Discovery of the Structure of DNA
  • Francis Crick and Maurice were part of the same social clique of English researchers. As the latter was working on molecular structure of DNA, it would have been offensive to get into it. Watson came from outside that circle and had no such limits. Crick & Watson together, plugged into 3 different sources of important nuanced information wrote the double helix paper weeks before another group could have discovered their triple helix manuscript’s errors.
• No difference between men and women in their ability to broker networks. (Data used to show gaps, but from an era where women’s role in workplace used to be generally weaker)
• Network efficiency formula:
  • (Actual Size) – (A+B+C+D+E) = Effective Size.
  • Effective size / Actual Size = Efficiency %
  • Actual size is the number of your connections
  • A, B… = how many OTHER people besides you each node is connected to, divided by actual size (e.g A connected to 2 people besides you is 2/5)
• To grow a network (and efficiency), spot redundancies and grow size. Not just latter.
• Exercise for practical network mapping: keep a stringent log of all your meetings over a few months: who did you meet, when did you see them last, which other people where there, did you learn anything new from this interaction?
• Think about diversified clusters, more than ties.

Article: Closure, Trust and Reputation by Ronald Burt

From BOOK: Brokerage and Closure: An Introduction to Social Capital (Google Books)

• You trust someone when you commit to a relationship before you know how the other person will behave (contractual terms incompletely specified). Distrust is a reluctance to commit without guarantees.
• “You can settle any dispute if you keep the lawyers and accountant out of it”
• Investing in people with whom you think good things will happen before you know what those things are captures the essence of brokerage. Building relationships to put yourself at increased risk of productive accident.
• Structural holes decay, structure is unstable.
• Social tie strength change over time:

• For bridges the curve is sharper, because there is less in common between you and bridge per definition, there is competition (maybe some better bridge appears).
• There are different brokering strategies: introducing people who need to talk or nonchalant “don’t worry about this, I’ll get it done for you you” which keeps the broker as a single known node for you.
• Differentiate between social ties that are person to person VS position to position.
• Structural holes are generally good for resource access and bad for shared identities.
• Social graph is completely agnostic to what kind of functional skills you as a person have. Latter is a question of category spanning.
• Extremely robust research results (public offices, eBay sellers, etc) showing that people in highly specialised categories (for ex: finance, engineering, etc) perform better and advance quicker towards top positions that generalists spanning categories. Questions that surrounding people have that prohibit progress for category spanners:
  • “What exactly does this person do?”
  • “Will she stay in the role we need her in?”
  • “Is this a jack of all trades and a master of none?”
• Way to compensate is to specialise + have a diverse network.
• In this context being a manager (for ex CEO) is a specific skill, not being a generalist.
• James Lincoln at Berkeley has done a lot of org culture & networks research
• Signals link observables to the unobservables. Important qualifier for signals: it is hard to fake. A Stanford diploma, being an olympic winner, etc VS wearing an expensive suit to signal being generally well off.
• Matthew effect - rich get richer, poor get poorer. Application in academia: those who get awards get access to better resources and are more likely to eventually become better in what they do.

MKTG 365: Marketing Analytics (Narayanan)

• Causality vs Correlation issues (latter not indicating former)
  • There is a third variable not considered
  • Reverse causality
  • Data quality & biases (80% of US bar fights are police reported to be started by the person who got killed – survivors obviously blame him)
  • Coincidence and random effects of large data sets
• Ordinary Least Squares - alternative name for simple, linear regressions

STATA: . regress emails rating

      Source |       SS       df       MS              Number of obs =    1366

-------------+------------------------------           F(  1,  1364) =  333.02

       Model |    270983.3     1    270983.3           Prob > F      =  0.0000

    Residual |  1109912.75  1364  813.719027           R-squared     =  0.1962

-------------+------------------------------           Adj R-squared =  0.1956

       Total |  1380896.05  1365  1011.64546           Root MSE      =  28.526

------------------------------------------------------------------------------

      emails |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

      rating |   24.24498    1.32858    18.25   0.000      21.6387    26.85126

       _cons |   30.15573   .7844888    38.44   0.000      28.6168    31.69467

------------------------------------------------------------------------------

• Interpretation:
  • Increase of 1 in rating increases number of emails by 24.24 (Coefficient for rating)
  • For rating zero, there are 30.16 emails (and for 1 rating = 24.24+30.16 = 54). (Coefficient for constant)
  • Rating explains only 19.62% of the changes in emails (R^2)
    • Really matters when you have to make predictions
    • 100% possible only in deterministic relationships with no randomness
  • Confidence level that what we’re looking at is not random is 100% (1 – P-value)
  • Adjusted R^2 penalises for additional variables, e.g the complexity of the model (just 1 here so no change)
  • Statistically significant results with 95% confidence: t-stat > 2 (or 1.96 to be precise). t-stat = coefficient / std error.
• For a variable with suspected non-linear relationship, add a data column with that variable ^2. This will capture four different curves of a matching line (parabolas, hockey sticks), depending on the sign of the coefficient of the variable and its square that come out of a multiple regression.

Case: Pilgrim Bank: Customer Profitability

• When evaluating R^2, consider both the cost (or even possibility) to get it higher AND the cost of getting it wrong (single marketing campaign VS NASA trying to predict asteroids hitting Earth)
• Before/after comparisons are often flawed: for example looking at the effect of a promotion, but not controlling other variables. And these others can be very hard to find (how do you include increase macro trends, overall increase of positive image of brand). Comparisons of regions with and without promotions are mitigation – difference across time and across comparable groups at the same time.
• To measure regulatory effects to behaviour (say, cell phone usage while driving) you can not just compare California to Nevada. Smarter to pick smaller bordering regions on each sides – similar cars, similar demographics, different laws on two sides.

OIT 262: Operations (Plambeck)

Article: Predictable Variability: Inventory Buildup Analysis

• Unpredictable sources of variability in production process can be uncertain, random, probabilistic. All can need a slightly different response/planning.
• Inventory in the context of operational processes is the number of flow units in the process at a given time (work in progress). Not to be confused with the inventory of output product, which accountants see as an asset – operational inventory is rather a liability.
• Little’s Law: Average Inventory = Average flow rate * Average flow time.
• When analysing a process “attach yourself” to a flow unit, and try to take it’s perspective: intuitive with service design (looking at a hospital experience through patient’s eyes), but also applicable to production (how would iron ore see what is happening to it through a metallurgy plant?)

Case: National Cranberry

• Variability will lead to backlogs if the arrival (or demand) rate is greater than capacity. Logical to attempt to add processing capacity to bottleneck – but not the only/cheapest way!
• Sometimes the cheapest way to get pile-up due to bottleneck constraints under control is not to invest in removing bottlenecks, but to negotiate with suppliers of flow items to deliver the same amount, but at a specific schedule (enough in the morning, break after inventory pileup, etc).
• At other times, add storage capacity to enable waiting. Or change product mix to maximise the revenue & margin generated with bottleneck resources.
• If you are a farmer, you must like gambling – because so many of their business successes depend on luck anyway. That’s why there can be cultural objection to attempts to remove gambling from the system, even if economically effective.
• Monte Carlo simulations often not good enough for process capacity analysis, need Discrete Event Simulation

STRAMGT 351: Building and Managing Professional Sales Orgs (Lattin/Levine)

Case: Vocera

• In indirect sales channels, trust is critical. Any hint of a direct/hybrid model can hurt that trust, limit VAR investments in your sales, etc
• Channel is a fulfilment engine, not a push engine (think of pushing a rope). Channel will sell when customers are aware of a product and know to ask for it. (Marketing)

Case: Jive Software

Guest: Dave Hersh, Founding CEO

• “I spent a lot of time trying to get management to focus [on enterprise vs SMB], but they resisted, pointing to the fact that the company was hitting its quarters. It was a classic symptom of an early executive team that was not scaling.”
• First resistance to Jive’s price point should have signalled that they were talking to the wrong customer. Instead, it instilled a sense of panic that they were losing traction in the market.
• Thing to do in a first all hands meeting after layoffs: put up a slide with names of every person who left, so people stop looking around the room wondering about it.
• In initiation phase of a startup, customer satisfaction is hard to measure (who will do the survey?) – sometimes clean proxies (“are they deploying the product they bought?”) are easier in practice
• Quotas: on revenues, bookings or margin?
• In early stage startup you want a heterogenous client base: watch out for sales incentives/quotas that fulfil with 1 massive deal (= massive risk for startup)
• Think of refactoring early “renaissance reps” to new products, new territories
• Sales learning curves are per product, not per company!
• “Sales is not a machine, it is an organism. Get out of the wrong metaphor, and others like DNA, organ rejection, etc start working”
• Early stage sales are just building a business not filling a process, get people who are exited about that prospect – MBAs?
• Hire from companies whose DNA is similar to where you want to get next. Just reacting to the mass of CVs you get from IBM, Oracle, BEA is risky…

Article: The Sales Learning Curve by Mark Leslie & Charles Holloway (HBR)

BOOK: (Chapter 10 “Paying for performance” from) The Complete Guide to Accelerating Sales Force Performance: How to Get More Sales from Your Sales Force

• Abstract: Because new product launches often take longer and cost more than expected, many promising offerings are prematurely aborted. Smart companies give themselves time and money enough to climb the sales learning curve before ramping up the sales force.
• Traditional methods of sizing required sales force (starting from margin contribution if they sold their “target quota”) fail startups. Need to start from the opposite end, minimal contribution expectations and scaling up only once the existing reps cover their own costs.
• Sales yield: average annual sales revenue per full-time, fully trained and effective sales rep. Shaped as an S curve, grows much steeper the more mature the product (follow-on/upgrade) and market.
• Phases of sales learning curve:
  • Initiation phase: from after the beta test to break-even point (sales yield reaches a point where revenue per sales rep covers their cost). Pointless to assign large quotas and do heavily commission-based plans with large teams. Still could use a few (3-4) sales people to ensure problems encountered are not just bad hires. Needs “renaissance reps” who sort out the unknown and facilitate product improvements.
  • Transition phase: From break-even to “real traction” (depends on industry, but consider 2X sales rep cost in sales yield). Developing a repeatable sales model. Renaissance reps still learn, new ones can be more “regular”, but still contributing to the evolving sales model.
  • Execution phase: hire “coin-operated reps” (require nothing more than a territory, sales plan, price book & marketing materials) as fast as finances allow.
• Rule of life for a sales rep: your quota will always go up and your territory will always shrink. (Mark Leslie)
• Compensation plan decisions
  • Level – total to expect on good performance (ex: $75k)
  • Mix – base vs variable pay (aggressiveness) (ex: 30k + 45k on quota)
  • Measures – revenue? new clients? units? margin?
  • “Shape” – curve of how payouts increase with additional sales (linear, step pings, changing slope)
• Avoid designing potential clawbacks into the system: everyone feels bad, demotivating, hard to admin.

STRAMGT353: Formation of New Ventures (Holloway/Morgridge/Chess)

Case: Sequoia Capital 2010

Guest: Mark Stevens, Partner, Sequoia

• Conflict management in portfolio: Yahoo in Sequoia Fund 6, Google in Sequoia 8. To make it more interesting even the same partner on both boards. Took a few years before they became fierce competitors, at that time Yahoo already public – fund distributed the shares to LPs before Google IPO, etc.
• Two-tier fund structure: named year funds raise from LPs (1.35B in 2010) and distribute to “primary funds” (Venture China, Venture US, Growth China…). Only latter funds take management fees. LPs would like to, but don’t get to decide on granular diversification.
• Syndication tends to go away in internet boom times, currently again most deals are syndicated.
• As an entrepreneur, worth asking a VC for their fundraising prospectus as one of the inputs of understanding what matters to them and how they talk about it.

Article: Search Funds – 2011: Selected Observations (PDF)

• Definition: Search fund is a pool of capital raised to financially support the efforts of an entrepreneur(s) to locate and acquire a privately-held company for the purpose of operating and growing it.
• Stanford Center for Entrepreneurship runs a bi-annual study. 150 funds since 1983. First year with more than 10 funds raised was only 2003.
• IESE does similar report on non-US search funds (Latin America, Western Europe & India)
• As an asset class, search funds have achieved 11.1X ROI by 2011 (down from 13.5X in 2009) and 34.4 IRR (since 2001 has fluctuated 32..38X), but median fund returned 0.8X of initial search capital. Top fund returned 500X.
• Very few top funds make the day – excluding top 3 funds, asset class ROI falls 11X -> 2.9X, excluding top five: -> 2.0X
• 2/3 single principal, 1/3 partnership. BUT, top quartile of actual return outcomes is 75% of partnerships and lower quartile 75% solo!

Case: Brown Robin Capital

Guests: Ryan Robinson, Lucas Brown (GSB ’07 grads; co-founders of Brown Robin Capital), Jim Ellis (search fund entrepreneur, Stanford lecturer)

• Analytical matrix for finding interesting target companies for a search fund:
  • “We created a chart with two axes. On the Y-axis were opportunities, industries, and ideas, such as ‘aging population’ or ‘big data.’ To seed it, we read a lot of popular media, we talked to a bunch of our friends at private equity firms and hedge funds, and we brainstormed macro trends. That led to 60-70 opportunities. On the X-axis, we devised the criteria that would make for a promising business. This included things like industry growth, whether it had a blue- or white-collar work force, and everything in-between. That gave us about 25 criteria”
  • “On the X-axis elements, it was naive to think that we could find a business with 25 characteristics that were all ‘right.’ What we found is that there are three or four things that matter above all else, so we started focusing more on those.”
• Sample raise for a 2-person search fund team: aiming to fund 2.5y search (industry average 18mon+), 15 investment units of $35k/each, $525k in total.
• Daily quotas: each partner should place at least 5 calls a day to new brokers or business owners. 2000 calls over almost 2 years search.
• “When you don’t know someone very well, you’re trying to impress them and show them what you can do. [Friends have] to go out of our way to hold each other to account in the absence of a manager.”
• When doing diligence of a target company, call your own investors regularly for an update of where you are. Listen carefully to the questions they ask you to build up a deep set of rich questions that matter you can use when talking to target companies in this industry.
• In the beginning had a list of 12 target geographies that we could be happy with (as personal living location) and girlfriends had veto power. Investors complained this was putting a non-economic limitation to ability to bring them a good deal.
• Partners had agreed on the titles and division of responsibility in target company (who is CEO, who is President, who controls board, etc) prior to the formation of the fund.
• “The pain with search fund process is that until you buy the company – you fail every single day.” (Ellis)
• Market growth is a huge runway, covers all sins. You can make all kinds of operational mistakes when selling to a market that grows +50%/year. And coming in as a search fund, you expect to make things more efficient, but in reality you will first raise cost: for example as an MBA who knows how to use excel, you need data to make decisions – to have data in a formerly small family business, you need to hire a controller, an IT guy, put in a few systems first.
• Try run on a company you know you’re not going to invest in ever: company visit, valuation discussions, first draft LOI, etc. So even if you have not bought a company before, you feel and look like having some experience when you’re finally talking to the one you’re actually interested in buying.
• As an investor you like entrepreneurs who have gone and pitched 75 people to sell their 20 investment units. It tells me something about you, and it has for sure made you better.
• “Everybody was carrying guns to office; landlord didn’t like our employees having sex in fire escape” – giving examples of what kind of cultural interventions were necessary in first 60 days at one company an entrepreneur bought after a long search

After a quick 2-day trial hop to visit a few firms like Boeing and Starbucks in Seattle in November, the Sloan Class of 2013 spent a full week of our spring break on East Coast. This time it was less about the particular companies and public organisations we saw, but more about the people, the leaders we met and their learnings and ideas.

All of our hosts had a little theme tip on “resilience” included in their brief (inspired by the Resilience: Why Things Bounce Back book by Andrew Zolli), which some of them chose to focus on and others less so. But most importantly, everyone seemed to just be themselves – which, mind you, can mean something quite different in bluntly direct New York compared to politically polished Washington, D.C.

I certainly appreciated the trust of the open conversations and I am holding back on too detailed notes from the meetings. Yet, just listing the names would be boring too – so let me include just one or two ideas from each. Which, as I am doing this weeks after the actual trip and by heart, are implicitly the concepts or questions that stuck with me – even if the wordings are my interpretation, not direct quotes.

Barry Rand, CEO of AARP (wikipedia); former CEO, Avis & EVP, Xerox

Quite amazing how the US social system has grown to a different direction than Europe as far as the state vs private responsibilities differ. AARP is an “interest group” (aka a non-profit association, what the un-initiated might just call a lobby interest) to represent 40 million members among the American retired people. And to make things more confusing, they have a billion-dollar profit making arm in this non-profit structure. Strategic ballet of the day: how do you play the “old VS young” tension in a way that today’s elderly feel connected & happy, but tomorrow’s elderly don’t grow up shrugging the brand away as something not for them?

Moises Naim, Senior Associate at Carnegie Endowment for International Peace; former Editor in Chief for Foreign Policy Magazine and pre-Chavez Venezuelan Minister

Due to a whole set of macro trends (social, technological, etc), power dynamics in the world are shifting from large, dominant, “ruling” players (countries, parties, corporations, etc) to small and seemingly weaker players. This has a ton of interesting side effects like changing criteria for what “winning” means (“denying others from winning” as opposed to “making others lose”), what that changing meaning does to progress and innovation (if the seemingly weak and small can veto everything, how will bold decisions get made?) and the tension between vetoism and democracy (for example, just 2 countries could decide real fixes for global warming, but 198 would feel overridden and left out in the process).

For more, read Moises’ book: The End of Power: From Boardrooms to Battlefields and Churches to States, Why Being In Charge Isn’t What It Used to Be

Jeffrey Williamson, Chief Human Resources Officer, US Postal Service

What makes US Post special is just the sheer scale: How do you manage 500,000 employees? If congress is pointing at your “billions of dollars of free cash on bank accounts”, do they realise that it is enough to pay the bills for… 2 days? Is a decision to not deliver snail mail on Saturdays an economic or cultural, if not religious one? Does e-commerce package delivery really make up to the fact that people don’t write and companies don’t bill each other on paper any more? Due to the complexity of last mile, the UPS-s and Fedex-s are still top5 customers for national postal service…

Carroll Doherty, Associate Director, Pew Research Center for the People & the Press

There has been a lot of talk about Obama bringing home the Hispanic votes, but the seismic shift in US politics that is driven by demographic change runs much deeper than just “Republicans are anti-immigrants, thus Democrats will win”. There are signs of a growing demand for an economically liberal, socially moderate power regardless of ethnicity (free market loving government that respects gay rights, anyone? Mr Bloomberg?), but a quick birth of a 3rd party in US tradition is very unlikely. At the same time, there has been a massive polarisation towards bipartisan extremes in voter preferences (questions like “do you elect those from your party who stand firm or are able to compromise for results?”) in last few decades.

Michael Kaiser, President, The Kennedy Center for the Performing Arts

Kaiser is a culture and arts turnaround guru, who besides running the impressive Kennedy Center (own symphony, opera, etc + few thousand various events per year, with a planned pipeline outlook for 5+ years) as both the business executive & artistic leader is preaching a arts management model that puts the quality content front & center. Since 2008 he has travelled the world to help suffering arts organisations (Holland cut 40% of their state funding for culture in 1 year!) to break out of the vicious loop that in tough times you do less performances, which in turn leads to less public interest, which in turn worsens the funding further.

See more from his books: The Art of the Turnaround: Creating and Maintaining Healthy Arts Organizations and Leading Roles: 50 Questions Every Arts Board Should Ask.

As another interesting US vs Europe tidbit, Paris Opera receives 120M EUR from state. Kennedy Center gets 120k USD of artistic support (+ some 30M USD of real estate maintenance support) from the government out of its 200M annual budget. The rest is 50/50 in a constant mode of fundraising + ticket sales. 80M USD from private sources. Every year. No wonder every orchestra performance has some presenting family name attached to it in this country…

Ben Bernanke, Chairman, US Federal Reserve Board

More memorable than the discussions of different use cases between fiscal and monetary policy levers was just meeting a man whose job has been probably under more public scrutiny since 2008, but has been re-appointed by Presidents of both parties nevertheless. Imagine that when you make a statement, the media & markets don’t just analyse what you said, but also spend hours on the nuances of your word choice and pauses. And now imagine, that you are as stereotypic of a gentle professor type (also with Stanford GSB years behind you), intelligent, humble, human and introvert in the middle of this frenzy. Somewhat surreal, but the more awe-inspiring combination to witness.

Duncan Niederaurer, CEO, NYSE Euronext

Apparently a basketball-loving kid born into a first generation immigrant family in the outskirts of New York can still think and speak as one even after years as a Goldman Sachs partner & now running a 200+ year old company. This uncensored authenticity made me realise that compared to NYC, people on the West coast say “f*** this s***” far less than they actually mean it, which might not always be a good thing. On other news, apparently the memories of a flash crash can be quite different between the CEO of the exchange and an in-flesh market maker on the floor whose brains we got to pick too.

Steve Miller, Chairman, AIG

After graduating GSB with Class of 1968 (note: the only class that has a building named after them in our Knight Management Center campus), and since the Chrysler turnaround in the 80s, Steve has been living a life where extremely broken companies call him up and ask for help. Every time the call comes, he knows that things are much worse than he is told, and he has 24-48 hours to take a job if he wants and appear in the office. It was a quiet day, the most recent call had only been 4pm the day before and Steve was still in the Chairman seat of AIG where he was asked to come in in 2010 to fix a certain ~$180 billion hole left by the mortgage crisis. He was just beaming love for this lifestyle it in a way that no man could ever fake when speaking with a student group – my new model of how to (not!) retire happy one day.

His book is definitely high on the reading list: The Turnaround Kid: What I Learned Rescuing America’s Most Troubled Companies

John Seifert, Chariman & CEO of North America, Ogilvy & Mather

Walking into an ad agency office, let alone in Manhattan brought back some great personal memories from the days of when DDB bought my first venture. After the “halls where Churchill & Roosevelt sat for the Arcadia conference” and nondescript board rooms of largest insurance companies of the world, it was refreshing just to walk into a place where the mellow lightning and plastic designer chairs follow the Pantone red precisely from the brand book. John’s talk revolved around two complex cases of brand reinvention for their clients: chaining BP into “beyond petroleum”, before — and through — the Gulf spill chaos, and creating “smarter planet” frameworks for IBM. In an era where ad agencies fight for talent with tech startups rather than their own kind, the intrigue and appeal of these old & huge company turnarounds could be counterintuitive, but I think I get it.

Susan Lyne, CEO, AOL Brand Group; Vice Chairman, Gilt Groupe

Again, the NYC authenticity school – I can list about 150 countries in the world where someone who has done stuff like exec roles at Disney or launching Lost in their career would never talk about their experiences, including getting fired so openly, and even with a sense of pride in learning.

Andy Dunn, Founder & CEO, Bonobos

I’ve shared notes about Andy’s adventures in building a kick-ass vertical e-commerce retail brand before, after his BBL at his alma mater (which soon will be also mine). Not much to add, besides that I added a pair of pink Bonobos to my wardrobe, to accompany an already perfectly fitting pair purple ones. And when I wore them first a week later, got a 2X priced offer to buy them off me in San Francisco, on the street. I would never have thought I could ever get more than indifferent about a clothing brand, but people who solve clothing shopping for men who hate shopping… will go far.

Herbert Allison, CEO of Fannie Mae; former Assistant Secretary of US Treasury for Financial Stability

Another fellow GSB alumni, who has another killer job of fixing a business recently that the world learned to hate seemingly beyond repair. His book of how to fix the global financial system (and not hope that anyone else will do it for you) is still in editing, so no link yet – but will keep an eye open. Nevertheless, he concluded our week with a perfectly simple though: as long as you are measuring your own success by something other people control (income, titles, praise…), you are screwed. You can never be satisfied, and that will be miserable.

Celebrate things that are solely under your own control to make happen: like helping someone else to be successful & look good. Every day.