Platforms and Networks

Managing Startups: Best Posts of 2011

2011-12-29T15:23:00.000-05:00

Here's my compilation of 2011's best posts about managing startups. I assembled similar lists at the end of 2010 and 2009. Please use comments to suggest additional posts. Happy New Year!

Lean Startup

Eric Ries's book, The Lean Startup, is a must-read for entrepreneurs.
Elad Gil outlines the pros and cons of staying in stealth mode.
Andrew Chen unpacks the concept of product-market fit.
The Startup Genome Project presents research on thousands of startups in a pair of reports. Be sure to read their report on premature scaling, the leading cause of startup failure.

Business Models

Alex Osterwalder, coauthor of Business Model Generation, which introduces the business model canvas widely used by entrepreneurs, lists eight questions you should ask about your business model.
David Skok analyzes key factors for success with the SaaS model.
Mark Suster gives advice for startups with applications that face a substitution threat from in-house versions developed by big platforms.
My 10-part series on business model analysis covers a range of topics.

Naming a Startup

Fred Wilson and Dharmesh Shah each offer advice on how to name a startup.

Customer Discovery and Validation

Laura Klein describes five fast/cheap/easy approaches to user research and usability testing.
Josh Porter of HubSpot explains why A/B testing shouldn't be limited to small changes.
Brant Cooper reviews biases that managers from different functions bring to the customer development process.
Rob Walling on ways to use inexpensive advertising to improve a site and learn about visitors.
This infographic from FormStack (via Boston Innovation) analyzes the anatomy of a perfect landing page.
Ben Yoskovitz describes how and why to use the "day in a customer's life" technique.
Elizabeth Yin offers advice on how to find and interview potential customers.
Three posts by Cindy Alvarez: 1) on how to cope with the fact that research respondents' answer to "Do you want...?" is always "Yes!"; 2) how and why to use personas in product development; and 3) "10 Things I've Learned" about customer development.

Metrics

Mark Suster reflects on how and why startups should use metrics.
Fred Wilson describes conversion metric targets for social media companies.
Presentation by Cindy Alvarez of KISSmetrics on conversion rate benchmarks for different types of web businesses (big download, but worth the wait!).
Andrew Chen collects retention rate metrics from several sources.
Avinash Kaushik on key metrics for small, medium-sized, and large ecommerce sites.

Demand Generation and Optimization

Fred Wilson and Mark Suster each offer general advice on startup marketing.
Cindy Alvarez on how to analyze reasons for low signup rates.
Avinash Kaushik offers three techniques for analyzing website conversion rates and describes why and how marketers should use controlled experiments.
Mixergy's Andrew Warner interviews Miguel Hernandez on how to produce videos that drive customer acquisition (scroll down for podcast transcript).
Akshay Arabolu offers a great compilation of posts on startup marketing.

PR Strategy

Mark Suster on how startups should use PR.
Joel Spolsky and Charlie O'Donnell each discuss the pros and cons of launching at a conference like SXSW.
Erica Swallow offers 10 tips for managing startup PR.

Viral Marketing

Mark Suster interviews Buzzfeed's Jon Steinberg, discussing strategies for building website virality.
A presentation by David Skok on the science behind viral marketing.
Sharad Goel on why virality is more rare than one might expect.

Sales Management

David Skok discusses how to build trusted sales relationships and interviews HubSpot VP-Sales Mark Roberge on SaaS sales best practices.
Mark Suster reflects on excuses offered by startup salespeople and interviews Vince Thompson and Mo Ali, who discuss startup sales best practices.
Steve Blank on the often contentious relationship between sales and marketing in startups.
Michael Woloszynowicz on managing the risk of feature creep when sales offers input during the customer development process.

Business Development

Chris Dixon on the Goldilocks Principle of business development, that is, how a big company may view partnership opportunities with a startup that is "too hot" vs. "too cold" vs. "just right."
Fred Wilson on how an orientation toward users versus brands should drive business development and other priorities at a startup.
Curtis Smolar on the legality of content scraping.

Funding Strategy

In Venture Deals, Brad Feld and Jason Mendelson offer a terrific, in-depth explanation of term sheets, funding strategy, VC motivations, etc.
Fred Wilson discusses working capital financing and customer financing.
Dan Weinreb discusses the pros and cons of working with angel groups in seed rounds.
Ty Danco reviews reasons why an angel will walk away from a funding opportunity.
Jason Calacanis and Fred Wilson each discuss how much money a startup should raise.
David Hornik and Fred Wilson each discuss the pros and cons of convertible notes.
Mark Suster and Fred Wilson each discuss strategy for pricing follow-on rounds.
Chris Dixon on best practices for raising a seed round and a VC round.
David Beisel discusses the importance of having VC partner buy-in for a Series A round.

Pitching

Mark Suster offers practical presentation tips, a primer on how to pitch to VCs, and thoughts on how/why to pitch without a deck.
Matt Rosoff summarizes a panel with five VCs describing best practices for pitching.
Brendan Baker on how to communicate traction to investors.
Jason Baptiste on best practices for demo day pitches, and Jason Cooper on the downside of demo days.
Robin Wauters/TechCrunch describes Adeo Ressi's one-sentence approach for pitching a startup; a list of pitching resources from Ressi's Founder Institute.

Are We in a Bubble?

Commentary on startup valuations and whether we are in a bubble from Eric Ries, Steve Blank, Fred Wilson, Mark Suster (via Alexia Tsotsis), Mike Arrington, Keven Lin (analysis of Crunchbase data), Dominic Rushe/paidcontent, Wade Roush/Xconomy, and KISSmetrics/FeeFighers (infographic via Jolie O'Dell/Mashable).
Errant on the logic behind Color's high valuation.

Founder Issues

Brad Feld on founders agreements and equity splits and on the cap table impact of hiring a CTO.
Mark Suster on why a startup doesn't need a COO and why the advantages of having a cofounder are often overstated.
Fred Wilson on the strengths and weakness of 1st-time vs. serial entrepreneurs.

Recruiting and HR Policy Issues

Jeff Bussgang on why early-stage startups should avoid titles.
Cindy Alvarez on how startups can hire the right "big company" employees.
Brian Balfour on startup hiring best practices.
Michael Woloszynowicz on how non-technical managers should give feedback to developers.

Board Management

Steve Blank offers a radical proposal for reinventing the startup board meeting.
Jeff Bussgang summarizes best practices for running a startup board meeting.

Management Advice, Not Elsewhere Classified

David Beisel on how to do due diligence calls for recruiting, biz dev, fund raising, etc.
Mark Suster on the value of cutting out middle-men in different types of startup negotiations.
Ben Horowitz advises startup managers on how to avoid being misinterpreted by employees.
James Altucher and Ben Horowitz each give advice to startup CEOs on the decision to sell their company.
A 4-part series from John O'Farrell giving advice to startups on going global.
Fred Wilson on target burn rates at different stages in a startup's life cycle.
Jason Cohen on startup problems that cannot be solved with money.

Startup Failure

Analysis by ChubbyBrain of the top 20 reasons for startup failure.
Rid Empson/TechCrunch description of Startup Genome research on premature scaling as the leading cause of startup failure (scroll down for infographic).
Quora thread on why startups fail.
Thoughtful post-mortem from former head of biz dev for OneTrueFan.
Vin Vacanti offers advice on when to shut down a startup.

The Startup Mindset and Coping with Emotional Pressures

Ben Horowitz gives advice to startup CEOs on managing their own psychology and the fine line between fear and courage when making difficult decisions.
Steve Blank on why entrepreneurs are like artists and how aspiring entrepreneurs can fail from lack of nerve.
Dharmesh Shah on 23 questions that keep entrepreneurs up at night.
Jason Cohen on what to do when the emotional pressure of founding a firm makes you want to throw in the towel; Spencer Fry on coping with this pressure.
James Altucher offers 100 rules for being an entrepreneur.

Career Planning Issues

Charlie O'Donnell on how to switch careers and join a startup.
Randall Bennett on what you can gain from working at a startup with low survival odds.
Simeon Simeonov offers 10 reasons to join a startup and gives practical advice on how to find the right startup position.
A 3-part series by David Beisel on how to find the perfect startup job.
Jason Cohen on why it's important for entrepreneurs to blog, and how to get started.
Brad Feld shares the TechStars Mentor Manifesto: great advice for anyone coaching startups.
Rob Go on the downside of a career in venture capital.
Alex Krupp: "How I got into Y Combinator as a Non-Technical, Single Founder."

Tools for Entrepreneurs

Lists of tools for startups from Steve Blank, Startup Weekend, PBworks, NashCoding, Shyam Subramanyan/List.ly, and Jaret Manuel.
List of online survey tools from Eric Leland/ideaware.
List of resources for designers and developers from Brian Hernandez.
A guide to getting started with iOS development from withoutfriction.

Business Model Analysis, Part 10: Getting Started

2011-08-01T21:43:00.006-04:00

This post is part of a series on business model analysis for entrepreneurs. The first post in the series presents a comprehensive list (available as a downloadable PDF) of issues entrepreneurs should consider when designing a business model. Others posts in the series delve into specific issues; this one aims to provide some practical advice for entrepreneurs as they get started with business model analysis.

Why Bother?

Since the list in Part 1 of this series includes four dozen questions, entrepreneurs may see business model analysis as a daunting task and an unwelcome distraction at their venture’s outset. They may prefer to conduct a few interviews with prospective customers, then start building, in order to learn by doing in an improvised manner.

Eric Ries explains the dangers of this “Just Do It!” approach in his book, The Lean Startup, and I won’t repeat his arguments here. However, if the sheer length of the list of questions above is a deterrent, entrepreneurs should keep this in mind: due to serial interdependence between the questions—some cannot be considered until others are addressed first—it is not possible, necessary, or even desirable to answer all of the questions immediately and simultaneously. In the spirit of the lean startup, business model analysis is an iterative and ongoing process.

Most entrepreneurs begin the process with an insight about an unmet need and a potential solution for that problem. They explore the opportunity through customer discovery interviews, and use early feedback to refine their concept. Until their idea settles down, it makes no sense to push for deep understanding of pricing options, customer acquisition costs, working capital requirements, etc.

While entrepreneurs should avoid over-investing in detailed analysis of downstream topics, at some point early in the process of evaluating an opportunity, they should make a quick pass through all of the questions. Their goal should be to articulate hypotheses for as many questions as possible, and to gauge their team’s confidence that these hypotheses are on target. Back-of-the-envelope economics are adequate at this stage.

This quick but comprehensive scan is intended to ensure that the entrepreneur has not ignored any important business model elements. Likewise, the scan should surface potential “deal-breaker” issues early—in particular, any lack of internal consistency between business model elements—and to stimulate a search for ways to address them. For example, based on the initial scan a team might conclude: “We envision a complex new product that would probably best be sold through a face-to-face demonstration, but our ballpark unit economics suggest we cannot afford a direct sales force.”

Write It Down!

When conducting this first pass through the business model questions, entrepreneurs should write down their hypotheses, along with the key assumptions behind them. As their model evolves, entrepreneurs should adhere to this “write it down!” discipline. They should consider displaying a summary of their model as an information radiator—a prominent visual display that can be readily referenced and amended by team members—like the wall displays that many teams use to track the status of programming projects or progress with their product roadmap. For example, a wall-mounted white board could depict a business model “canvas,” following the approach suggested in Alex Osterwalder’s book, Business Model Generation. For some terrific examples of Osterwalder’s canvas in action, see the presentations done by Steve Blank’s students in his Lean LaunchPad course at Stanford.

Using the information radiator approach, members can affix different-colored Post-It notes in the cells of the canvas, showing the status of hypotheses for each business model question, for example: 1) green = the hypothesis has been validated through a decisive test; 2) yellow = a test that could validate the hypothesis has been identified, but not yet executed; 3) blue = a hypothesis has been advanced, but not a way to test it; and 4) pink = the question is important, it is too early to offer a hypothesis. As the team makes progress, pink notes will be replaced with blue ones, blue will be replaced with yellow, clusters of notes representing competing hypotheses about a single question will be pruned, etc. And, when the team makes a pivot—that is, when it changes its business model in significant ways due to a failure to validate a key hypothesis—large swaths of the board may revert from green notes back to yellow, blue, or pink ones.

Business Model Analysis, Part 9: Outsourcing

2011-07-30T16:16:00.003-04:00

I won't try to expand on those insights here. Rather, I'll focus on the microeconomics of in-house vs. outsource decisions, which, in economists' parlance, are choices about vertical integration. According to Yale Professor Oliver Williamson, there are economic advantages to completing transactions between two units within a vertically integrated company—rather than between two independent firms—when the transactions entail high levels of uncertainty, small numbers bargaining, and asset specificity. With transactions between independent firms, uncertainty makes it difficult to draft a contract that specifies each party’s obligations under any contingency that might arise. Absent a complete contract, the parties periodically will need to renegotiate transaction terms. If either party is subject to “small numbers bargaining,” that is, if it has few potential transaction partners, then that party may be vulnerable to hold-up when it renegotiates. Finally, if either party’s assets are tailored for a specific transaction type and cannot be redeployed into other uses, then failing to complete a crucial transaction—for example, securing an input required for production—may lead to bankruptcy with little liquidation value.

Startups frequently face the conditions that encourage vertical integration. By definition, they confront high levels of uncertainty. Also, when they target new markets with radical innovations, startups may require access to idiosyncratic assets controlled by only a few potential partners.

However, vertical integration poses challenges for resource-constrained startups, because it often requires major investments. Cake Financial, a service that gave investment advice to consumers based on analysis of their online stock trades, illustrates this dilemma. Cake’s founder had a choice between building software that could extract a customer’s trading data (with their permission) from their online brokerage accounts, or licensing access to the data from a firm that had already developed similar software. Concerned about that firm’s fees and whether it would be responsive to a small startup’s needs, Cake’s founder chose to build the software. This consumed most of the $9 million in venture capital that Cake had raised, and put the startup in a precarious position when demand for its service was slow to emerge and then capital markets slammed shut during the 2008 global economic crisis.

Business Model Analysis, Part 8: Crossing the Chasm

2011-07-29T08:29:00.001-04:00

In his classic book on hi-tech marketing, Moore observed that customer adoption of revolutionary new technology products follows a predictable life cycle. Early adopters, according to Moore, are visionaries seeking breakthroughs; they can imagine the new product’s benefits before they have been proven. Because they are tech-savvy, early adopters can self-assemble complementary hardware, software, and services needed to use the new product, and can cope with its inevitable initial bugs. In the next stage of the product life cycle, the early majority—a much larger group—are pragmatists who will only buy a standardized product that has clearly proven benefits. They demand a “whole product solution”—an easy-to-use, reliable bundle of all necessary hardware, software, and services—supported by a reputable firm.

Moore observed that peer-to-peer references are crucial in driving technology purchase decisions. However, early adopters are not considered to be credible references by the early majority: there is a chasm separating the two groups because they rely upon such different purchasing criteria. As a result, new products that are successful with early adopters often stall when startups try to sell them to the early majority.

Moore’s prescription for crossing the chasm is to target a single segment within the early majority; to engineer a whole product solution with clear benefits for this segment; and to overwhelm the segment with an integrated, intensive marketing campaign. From this beachhead, the firm can then leverage referrals to capture other early majority market segments. Moore likens this strategy to World War II’s D-Day, when the Allies landed a massive force at Normandy as the first step in their invasion of Europe.

Most startups targeting fundamentally new markets will not encounter the chasm until they are a few years old; in the meantime, they will be busy cultivating early adopters. Consequently, seed-stage ventures can probably ignore the chasm risk as Steve Blank points out in Four Steps to the Epiphany. However, a “D-Day” strategy requires plenty of planning, so entrepreneurs should begin to watch for the chasm as their startup matures.

Business Model Analysis, Part 7: Bundling

2011-07-28T10:58:00.002-04:00

Bundling entails selling, in a single transaction, two or more items that could conceivably be sold separately. A printed newspaper, for example, is a bundle of news stories, classified ads, comics, obituaries, stock tables, sports scores, etc. Microsoft Office bundles several productivity applications in a software suite.

The ubiquity of bundling is not an accident: the strategy can provide significant benefits, including superior surplus extraction (i.e., capturing a greater share of customers’ willingness to pay), economies of scope, product design improvements, and strategic advantages.

However, pursuing a bundling strategy can be challenging for a resource-constrained startup Most early-stage ventures strain their capabilities to develop and sell a single product, so bundling multiple products from the outset may not be an option. Nevertheless, entrepreneurs should keep the potential benefits from bundling in mind as they design their business models and plan for future product launches.

These benefits include :

Surplus Extraction. Economists define consumer surplus as the difference between a customer’s willingness-to-pay (WTP) for a product and its price. When a firm offers the same price to all customers (i.e., when it does not engage in price discrimination via negotiated pricing, auctions, etc.), bundling two or more products may allow the firm to extract a larger share of total available consumer surplus — and earn higher profits — than it would from selling the items separately. To illustrate this potential benefit, consider an example with two customers, Jack and Jill, and two products, A and B, each sold by the same monopolist. A and B both have zero marginal cost (as with many information goods), and the firm must offer a single price for each product to all customers. Jack’s maximum WTP is $10 for A and $4 for B. Jill’s maximum WTP is the reverse: $4 for A and $10 for B. If the firm sells A and B separately for $4 each, it will sell one unit of each product to both customers and earn total profits of $16. By pricing each product at $10, it will sell one unit of A to Jack and one unit of B to Jill and earn profits of $20. However, if the firm offers an A+B bundle for $14, it will sell the bundle to both customers and earn profits of $28. Bundling is more likely to increase profits in this way when: 1) the marginal cost of bundled items is low or zero; and 2) the correlation of consumers’ valuations for individual items is weak. Weak correlation means that when customers evaluate the items in a bundle, they don't all have the same favorites. Real world examples of surplus extraction through bundling abound. Some HBO subscribers, for example, value its recent theatrical films highly; others love its original series (e.g., True Blood, Entourage); still others are drawn to HBO's boxing matches or concerts. This varied programming mix allows customers with very different preferences to each justify paying a $10 monthly subscription fee.

Economies of Scope. Compared to selling items separately, bundling can also reduce a firm’s costs. Firms can realize economies of scope in customer acquisition activities because they can sell the bundle with a single marketing message, rather than two separate ads or two sales calls for two distinct products. Likewise, economies of scope in production are available when integrated designs leverage shared components (e.g., a single screen and battery when combining a cell phone and MP3 player).

Product Design. Integrated designs may also yield quality advantages through simplification of interfaces, as with Google’s use of a common password across all of its applications and its integration of Gmail into its search service.

Strategic Advantages. Under certain conditions (described in this academic paper), bundling may allow a company that monopolizes a market for one product (call it "A") to profitably leverage its way into the market for a crucial complement to A (call it "B") which previously was supplied only by independent companies. Products are complements when they are frequently or always consumed in tandem (e.g., browsers and PCs; beer and pizza). By offering only an A+B bundle (i.e., "tying" A and B and not allowing customers to buy A separately), the market A monopolist forecloses access to its customers, denying standalone suppliers of B the opportunity to sell to them. The resulting reduction in revenue weakens the standalone suppliers of B and may even force them to exit the market. Of course, such a strategy can run afoul of antitrust law, as Microsoft discovered when it tied the Internet Explorer browser to its monopoly Windows operating system.

Business Model Analysis, Part 6: LTV and CAC

2011-07-27T08:58:00.001-04:00

As they decide whether to race for scale, startups face a function that relates their long-term payoff—the net present value of future cash flows earned as a result of acquiring new customers during the current period—to their level of current-period investment in customer acquisition. Entrepreneurs cannot observe this function directly; they must estimate its shape based on customer and competitor responses to the firm’s initial marketing efforts and historical information for similar products. Early in a product’s life cycle, with limited data available, these estimates will be imprecise, so entrepreneurs should be wary of overconfidence biases that may lead them to overinvest in growth.

The function relating long-term returns to current-period investments in customer acquisition will have an inverted “U” shape. Up to some point—I* in the figure below—increasing investments should boost a firm’s net present value (NPV), but at a diminishing rate as the cost of acquiring each additional customer rises. Beyond the value-maximizing point, I*, it costs more to acquire additional customers than they are worth. Put another way, if you race too hard, or not hard enough, you will hurt your long-term returns.

Net present value per new customer—that is, customer lifetime value (LTV) minus customer acquisition cost (CAC)—may decline with investment levels for four reasons:

Broadening Beyond the Firm’s Natural Market Segments. Most products have attributes — features, service quality, brand image, etc. — that match the needs of some customer segments better than others. When racing for scale, a firm may target prospects outside of the customer segments that find its product most appealing. Large price reductions or promotional expenditures may be required to convert these prospects into buyers.
Prematurely Soliciting Mainstream Customers. Mainstream customers often defer purchases until early adopters have “tested the water” and can verify that a product has an attractive value proposition. If mainstream prospects are solicited prematurely, conversion rates may be low unless inducements are offered.
Pricing and Promotional Battles. Aggressive moves to capture share may precipitate pricing and promotional battles with competitors.
Scalability Constraints. The operational strains of rapid growth may degrade product or service quality. This can hurt solicitation conversion rates and raise acquisition costs per new customer. The resulting damage to the firm’s reputation can also put downward pressure on pricing and customer retention rates, further reducing the payoff from racing.

NPV Impact of Customer Acquisition Investments

The ratio of customer lifetime value (LTV) to customer acquisition cost (CAC) is a useful measure of the productivity of customer acquisition efforts. LTV equals the discounted present value of variable contribution—revenues minus variable costs—earned over the life of a typical customer’s relationship with a company. LTV does not deduct customer acquisition costs (CAC). Unless a firm exhibits viral growth or increasing returns to scale (scenarios discussed below), CAC = LTV is the most that a company can profitably afford to invest to acquire a new customer.

Calculating LTV and CAC

Calculating a firm’s maximum customer acquisition cost (CAC) based on the average lifetime value (LTV) of a customer involves four steps:

Step 1: Determine contribution per customer. Variable contribution equals revenue earned less all variable costs incurred in serving a customer in a given year, excluding marketing costs related to customer acquisition. A back-of-the-envelope approach for calculating the average contribution per customer—usually sufficient for providing a rough “reality check” on a business model—simply subtracts a company’s total variable cost from its revenue for the most recent period, then divides the remainder by the average number of customers served during that period.

A more sophisticated approach recognizes that 1) contribution per customer may vary substantially for different customer segments, and 2) the annual contribution per customer is likely to change over the life of a customer relationship. With respect to the latter point, a company may be able to increase its prices over time. Also, the company should be able to collect information about the customer’s preferences and may be able to use that information to cross-sell related products. Finally, over time, variable costs incurred in serving a customer tend to decline as a percentage of revenues for two reasons. First, experienced customers tend to generate fewer customer service inquiries because they “know the ropes.” Second, as a company grows, it typically can improve its operational efficiency and realize volume discounts in procurement.

Step 2: Determine customer life. To calculate the average length of a customer relationship, one can employ the formula 1/x, where “x” is the annual customer churn rate, that is, the percentage of customers that terminate their relationship with a company from year to year. So, if a company retains 70% of its customers each year, then the average customer life is 1/0.3 = 3.33 years. Of course, the average length of a customer relationship may vary widely for different customer segments.

Step 3: Calculate LTV. The annual cash flows per customer calculated in Step 1 are discounted to their present value, using the number of years for the duration of a customer relationship calculated in Step 2.

Step 4: Calculate CAC. A back-of-the-envelope approach for calculating the average cost of acquiring a new customer takes total sales and marketing expense incurred during a period, then 1) subtracts any costs related to retention and usage stimulation efforts targeted at existing customers (e.g., time spent by sales reps calling on existing accounts, rather than prospecting for new customers); and 2) divides by the total number of new customers acquired during the period.

As with the other inputs described above, average customer acquisition costs will vary considerably by customer segment. Likewise, different acquisition methods may have very different costs. Each method will be subject to decreasing returns during a given period as available prospects in the most attractive segments are converted into purchasers and the company is then forced to target prospects for whom the product is less compelling. For this reason, companies employ cohort analysis: they measure the productivity of their marketing efforts—and optimize their efforts accordingly—by tracking, over time, the LTV and CAC of “vintages” of new customers acquired during a given period through different marketing methods.

Step 5: Compare LTV and CAC. In theory, for any given new customer, a company can afford to increase CAC up to the point that CAC = LTV for that customer. Of course, if CAC = LTV for every new customer that a company acquired, it would not generate enough contribution to cover its fixed costs. For this reason, many companies employ a target LTV/CAC ratio. For many software-as-a-service businesses, for example, the target ratio is 3:1.

Calculating LTV and CAC with Virality and Network Effects

When calculating LTV and CAC for businesses that exhibit virality and/or strong network effects, complications may arise:

Virality. The maximum amount that a firm can afford spend to acquire a customer through paid marketing methods should take viral growth opportunities into account. In theory, in calculating the value of a “seed” customer, one should reflect the LTV of every additional customer who will be subsequently acquired due to free, viral mechanisms that are put in motion by the seed. This could conceivably involve a chain of viral acquisitions that stretches for many years into the future. In practice, it is more conservative to credit the seed customer with only one year’s worth of viral acquisitions. A straightforward way to do this is to multiple the LTV directly generated by the seed customer by the 1.0 + V, where V is the viral coefficient for a new customer of that cohort type.
Network Effects Generate Value for Other Customers. When a business exhibits increasing returns to scale due to network effects or scale economies in production, acquiring a customer in the current period increases future cash flows from other customers. In calculating LTV, this incremental value should be added to the present value of future cash flows derived directly from a new customer, as illustrated in the technical appendix to Part 2 of this series.
Variable Costs Depend on Network Density. When networks have a spatial component, the physical proximity of customers may be an important factor in determining variable costs. For example, an online grocery service can achieve much lower delivery costs per customer when a driver’s stops are just a few minutes apart. Hence, to calculate contribution margins accurately, managers need a reliable forecast for network density.
Two-Sided Networks. Two-sided networks have two distinct user groups whose respective members consistently play the same role in transactions, for example, cardholders and merchants in American Express’s credit card network; job seekers and recruiters in Monster.com’s online recruitment network. To mobilize a two-sided network, platform providers must attract users to both sides—typically simultaneously.

In this context, LTV calculations can become very complicated; explaining their mechanics is beyond the scope of this post. In fact, marketing scholars have only recently begun to develop statistical models that can be used to estimate LTV in two-sided networks; my colleague Sunil Gupta has done some pioneering work on this front. Consistent with the previous point, these models factor the impact on future cash flows from Side B users in estimating the value of additional Side A users, and vice versa.
In most companies serving two-sided networks, distinct organizational units will be charged with marketing to the separate sides; these units must coordinate their plans to ensure that overall marketing spending is optimized. In particular, it is important to avoid double counting the profit increase attributable to network effects when separate organizational units each calculate LTV for their respective sides. Complicating matters further, certain marketing programs will impact user acquisition rates on both sides (for example, Monster.com’s Olympic sponsorships, which built awareness among both recruiters and job seekers). Managers must determine how to allocate these expenses across the two sides when calculating CAC.

For more on LTV and CAC analysis, see F. Reichheld, The Loyalty Effect; Blattberg et al., Customer Equity: Building and Managing Relationships as Valuable Assets; Blattberg & Deighton, “Manage Marketing by the Customer Equity Test”; and posts by David Skok of Matrix Partners.

Business Model Analysis, Part 5: Virality

2011-07-26T09:29:00.001-04:00

A product grows virally when its use spreads through direct, customer-to-customer transmission. Viral growth occurs through four different mechanisms listed below. With the exception of incentives, these mechanisms do not entail any marketing expenditures, so business models that harness strong viral growth can be very attractive.

Direct Network Effects. To function properly, some products must be used jointly by two or more parties. These products are said to exhibit direct network effects, because their users interact directly. For example, early versions of Skype required both a call originator and recipient to use Skype software. When one party who already has such a product wishes to interact with another who does not, the first party can contact the second party to suggest that they acquire the product.
Word-of-Mouth. Even if they do not enjoy direct network effects, products can spread virally when a happy customer recommends them to another party, as when a satisfied diner suggests a restaurant to a friend.
Casual Contact. Like the common cold, some products can spread virally through casual customer-to-customer contact. For example, the free, web-based email service Hotmail grew explosively in 1996 after its founders added a link at the bottom of users’ emails that simply said, “Get your free email at Hotmail.”
Incentives. Many companies structure incentives that encourage their existing customers to recruit new customers, for example, MCI’s 1990s “Friends-and-Family” plan, which offered reduced long distance rates for calls between MCI customers in a circle of up to twenty members.

Virality and network effects are often conflated and confused, so the distinction between them warrants clarification. It should be clear from the list of mechanisms above that not all products that spread virally exhibit network effects. Likewise, not all users of products with network effects are acquired through viral, customer-to-customer transmission mechanisms. Specifically:

A new user of a product with direct network effects might sign up based on press coverage or advertising, then discover parties with whom they can interact after using the product. This pattern was evident in the rapid growth of MySpace, Second Life, Twitter, and the question-and-answer service Quora.
Direct network effects are distinguished from indirect network effects in a two-sided network, in which growth in one side’s user base (e.g., Android phone owners) attracts more users to the other side (e.g., Android application developers), and vice versa. With indirect network effects, the mechanism of attraction is the aggregation of a larger base of users, rather than contact between individual users.

Many startups combine more than one viral mechanism in their go-to-market plan. Dropbox, for example: 1) harnessed a direct network effect when users employed the service to collaborate on documents; 2) benefited from word-of-mouth referrals from loyal customers; 3) acquired customers through casual contact when users emailed links that allowed recipients to download (without installing Dropbox) files stored in the sender’s public folder on Dropbox; and 4) offered a two-way “user-get-user” bonus, that gave both the inviter and recipient an additional 250MB of free storage.

Viral Coefficient

A firm’s viral coefficient is calculated as the number of additional customers subsequently acquired through viral mechanisms for every new customer initially acquired. Startups that rely heavily on viral growth should track their viral coefficient overall and by customer cohort—that is, for each “vintage” of new customers acquired during a given period through different types of marketing program employed by the firm. As shown by the table below, a viral coefficient greater that 1.0 yields self-sustaining growth from an initial “seed”—that is, a batch of new customers acquired in period 1. In the table, we assume that a seed group of 1,000 new customers each purchase one unit of a firm’s product in year 1. These seed customers do not repurchase the product, but through viral means, they attract some additional customers who purchase in year 2, who in turn attract some more customers in year 3, and so forth.

	Number of New Customers
Viral Coefficient	Year 1	Year 2	Year 3	Year 4	Year 5
0.3	1,000	300	90	27	8
1.0	1,000	1,000	1,000	1,000	1,000
1.3	1,000	1,300	1,690	2,197	2,856

When modeling viral growth dynamics for customer relationships that have a multi-year life, it is important to be specific about whether the viral coefficient should only be applied in year 1, or in each year. In some contexts, new customers are likely to quickly exhaust word-of-mouth recommendations or other viral mechanisms (e.g., opportunities to leverage “member-get-member” bonuses).

David Skok of Matrix Partners discusses viral coefficients in depth in this post, and Adam Penenberg’s book Viral Loop provides many examples of viral customer acquisition.

Business Model Analysis, Part 4: Racing for Scale

2011-07-25T10:08:00.002-04:00

"Should we step on the gas pedal?" Based on my work with startups, this is the strategy question that entrepreneurs raise most often. It's crucial to understand whether accelerated growth is a priority, given the attributes of your business model. A flawed plan to "Get Big Fast" can be fatal for a resource-constrained startup.

A new venture has incentives to race to acquire customers if it enjoys: 1) increasing returns to scale due network effects or strong scale economies in production; 2) high customer switching costs; or 3) other first mover advantages, such as opportunities to preempt scarce assets or patents. Potentially offsetting these incentives are scalability constraints and late mover advantages. In this post, I won't address the complex question of whether to adjust a startup's growth strategy in response to a valuation bubble.

Addendum, July 26, 2011: My friend Joel West, a scholar/entrepreneur at San Jose State, makes the very important point that growth is often a choice for entrepreneurs that hinges on their personal preferences as much as on economics. I've written below with the tacit assumption that a founder will always want to grow a business if she can. Of course, there are many lifestyle companies with business models that could be profitably expanded, but also can be operated successfully at a more modest scale, consistent with the owner's goals.

Increasing Returns

Most businesses exhibit constant returns to scale, that is, their margins do not vary much over the range of sales volume that a firm might reasonably achieve. In such businesses, as output expands, scale economies may be offset by diseconomies of scale, including the additional cost of coordinating more complex operations and cost escalation for increasingly scarce inputs (e.g., skilled labor, attractive locations for facilities).

A business that enjoys significant scale economies—either due to strong, proprietary network effects or the ability to leverage high fixed costs—may exhibit increasing returns to scale: its profit margin may continue to improve as the business grows. Firms are strongly motivated to race to acquire customers when they can exploit increasing returns.When customers’ willingness to pay increases with network size or when unit costs decline with greater production volume, acquiring a new customer yields two streams of benefits: 1) profits earned over time directly from that new customer; and 2) profits earned over time from all other customers. For example, with proprietary network effects, as long as a new customer remains affiliated with a platform, all other customers should be willing to pay slightly more for access to the platform. Although this increase in willingness to pay may be infinitesimally small for any given customer, these tiny amounts add up when aggregated across the entire customer base.

Consequently, when acquiring customers in a business with increasing returns, startups can afford to spend up to the present value of future profits earned directly from the new customer (i.e., benefit #1 above) plus the present value of incremental profits from other customers (i.e., benefit #2). By contrast, in businesses with constant returns to scale, companies should spend no more on customer acquisition than the present value of future profits earned directly from a new customer (i.e., benefit #1). In short, when they enjoy increasing returns, firms have a stronger motivation to race for scale. However, when several firms simultaneously enter a new market with increasing returns, they all may pursue an aggressive growth strategy, dissipating any above-normal profits available from the market.

Switching Costs

In a new market, high customer switching costs also can motivate startups to race to acquire first-time buyers—those not yet affiliated with any vendor. After locking in these first-time buyers, a firm should be able to raise prices to them by an amount just slightly less than the switching cost they would incur by changing vendors. As with increasing returns, when several firms simultaneously enter a new market with high switching costs, intense competition for first-time buyers may dissipate profits. However, dynamics may be different if a pioneer achieves a big time-to-market lead and amasses a significant customer base before rivals enter the market. Under these circumstances, switching costs may encourage the pioneer to be a “fat cat,” pricing high to its existing customers but ceding remaining first-time buyers—and market share—to entrants, who offer a lower price.

Additional First Mover Advantages

In addition to factors that yield increasing returns to scale and switching costs, pioneers in a new market may enjoy other first mover advantages, including opportunities to preempt scarce assets, patents, or capacity:

Preemption of Scarce Assets. Through purchases or long-term contracts, first movers can lock up valuable assets, for example, component supply contracts, skilled labor, or attractive geographic locations for factories. Scarce assets become more costly to acquire after rivals enter. In some cases, entry may be impossible once a crucial asset is locked up, as with government-licensed spectrum for telecommunications services.
Preemption of Key Patents. By exploiting a head start in research and development, first movers may be able to secure patent protection for key technologies. If the pioneer decides not to license its intellectual property, prospective rivals may find it costly to “invent around” the patents and, at the extreme, may be unable to enter the market.
Preemption of Capacity. If the minimum efficient scale of production facilities is high in relation to the expected size of the mature market, the first firm to build such facilities may be able to deter prospective rivals from ever entering the market. In the early 2000s, for example, Teledesic and SkyBridge both proposed to spend billions of dollars to launch scores of low Earth orbit satellites that could provide high-speed Internet access anywhere in the world. However, it was unclear whether the market was large enough to support even one competitor, so each firm had to decide whether to offer its service at all if its rival managed to launch first.

Scalability Constraints

Startups must consider operational capacity constraints on their pace of growth. Rapid expansion is more feasible when production and customer service functions can be outsourced to vendors equipped to handle volume surges. For example, by relying on Amazon’s S3 cloud storage service rather than building its own infrastructure, Dropbox was able to acquire 25 million customers within four years of its founding.

When operations are kept in-house, capacity can be scaled more readily if general-purpose equipment is employed. Rapid growth is more challenging when companies rely on specialized production and distribution facilities (e.g., proprietary equipment for packaging Keurig’s coffee capsules; Webvan’s use of highly-automated warehouses for its online grocery service). Such companies must build capacity well ahead of sales if they plan to pursue accelerated growth—a risky proposition when demand in a new market is still uncertain. Webvan took this gamble and failed after investing $800 million.

Likewise, when customer service interactions are complex, a startup’s growth may be constrained by the pace at which it can attract and train skilled personnel. This constraint is relevant for online stock brokerages, whose call center reps must respond to customers’ questions about a wide variety of transactions, including “stop loss” instructions, options trades, and margin calls.

Late Mover Advantages

Potential late mover advantages include opportunities to:

Reduce R&D Costs Through Reverse Engineering. While some pioneers enjoy strong patent protection, others are unable to prevent rivals from copying their products. Reverse engineering usually results in significant R&D cost savings compared with amounts spent by the pioneer on the original product.
Leapfrog Leaders with Newly Invented, Superior Production Technology. Late movers may also gain an edge in terms of cost or product performance by leveraging new technology that was not available when the pioneer launched. For example, Qualcomm was a successful late mover in establishing standards for cellular telephone equipment. Qualcomm’s CDMA standard leveraged leading-edge spread-spectrum technologies that deliver superior capacity and reliability relative to competing digital standards.

Free Ride on Pioneers’ Investments in Educating Customers. In the earliest stages of a new market’s development, a large investment in “missionary” marketing may be required to educate prospective customers. The pioneer may bear 100% of the investment in education, only to find that late movers reap a return on that investment without sharing its cost.
Avoid Pioneers’ Positioning Errors. Pioneers often face great uncertainty about customer needs, so they are more likely than later entrants to need to pivot, that is, change their business model. When a company dramatically changes its value proposition, it may destroy goodwill by confusing partners and alienating existing customers who were sold on the original brand promise. If a pioneer pivots and customer attrition rates rise in response, then the value of earlier investments in accelerated growth may be negated. Late movers can avoid such positioning errors by learning from the leader’s mistakes.

If, in aggregate, late mover advantages are strong, they can encourage what might be thought of as reverse racing. Prospective pioneers may have an incentive to throttle back their marketing efforts or even delay their launch plans when they can secure significant late mover advantages through reverse engineering, by free riding on missionary investments in customer education, when a major technological breakthrough is expected, or when trailblazers are likely to get lost in terra incognita.

Business Model Analysis, Part 3: Switching Costs

2011-07-24T16:46:00.001-04:00

Switching costs are incremental expenditures, inconveniences, and risks incurred when a customer changes from one supplier to another. For example, when consumers switch from a Windows PC to a Macintosh computer, they must buy and install Mac-compatible application software and invest time in mastering a new interface.

Switching costs fall into three broad categories:

Redundant Relationship-Specific Investments. Because their old and new vendors may have different requirements, customers who change suppliers sometimes must invest in new software/hardware or repeat certain activities they have already completed. For example, to switch online stockbrokers, users incur the hassle of transferring funds and securities. For that reason among others, customer retention rates for online stockbrokers have been high.

Disruption Risks. When businesses outsource “mission critical” activities, changing vendors may involve considerable risk. For example, some companies reduce their need for IT staff and infrastructure by relying on cloud computing services. Switching from one cloud service to another exposes a company to significant risk if customer records are lost or corrupted during the transfer.

Contractual Penalties. Companies can impose penalties on customers if they end a contractual relationship prematurely. For example, mobile phone carriers often charge an early termination fee to customers who have signed multi-year contracts. In some cases, these contractual penalties significantly exceed the true costs incurred by a firm due to customer attrition.

When its current customers confront high switching costs, a firm is well positioned to capture a portion of the value it creates. Specifically, by raising its price just below the point where current customers are indifferent between staying with a firm’s product and switching to a rival’s, a firm should be able to earn above-normal profits equal to the sum of the switching costs confronting its customers (“above normal” implies profits in excess of a competitive return on capital; see the technical appendix below for analysis of the impact of switching costs on pricing and profitability).

Switching costs are important in securing a first mover’s position. In their absence, an entrant that offers a superior product or a deep discount might quickly usurp the market lead.

Given these economic benefits, companies often seek to deliberately increase switching costs, either through contractual or technological means. Apple, for example, has employed proprietary digital rights management software, locking in its customers by making it difficult for them to transfer iTunes Store purchases to rival media players. However, efforts to increase switching costs are not likely to escape the notice of prospective customers, who will fear hold-up in the form of a “low then high” pricing strategy. Anticipation of lock-in can represent a significant barrier to adoption for a new product. To ease customers’ concerns about lock-in, firms sometimes license their products to “second source” rival suppliers.

Technical Appendix: Switching Costs and Pricing Leverage

This appendix is only likely to be of interest to readers who want to dig deeper into the economic foundations of business models. To see how switching costs can impact firms’ pricing flexibility and their motivation to race for scale, consider the following example, adapted from my note “Racing to Acquire Customers.”

Assume that two firms, Alpha and Beta, offer identical products. A typical customer purchases one unit of either firm’s product in period 1, then requires a replacement unit in period 2. The variable cost of producing one unit is the same for both firms: $100. In a perfectly competitive market without any switching costs, Alpha and Beta would both price their products at $100 in periods 1 and 2. If either firm tried to raise its price, the other could steal all of its customers by slightly undercutting the new, higher price.

Now assume that each firm has a base of customers that it acquired in period 1, but these customers would incur a $50 cost by switching suppliers in period 2. Assume further that during period 2 the firms are able to offer one price to their existing customers and—in an effort to steal share—a different, lower price to their rival’s customers (e.g., a “special promotion”).

If Alpha wanted to steal Beta’s customers in period 2, it would have to compensate those customers—in the form of a lower promotional price—for the $50 switching cost they would incur. However, Alpha must also recover its $100 variable cost, so the lowest possible price it could offer in period 2 to Beta’s customers and still break even would be $150. If, in response, Beta set its period 2 price for existing customers at $149.99, its existing customers would not bother to switch to Alpha’s product. Beta would retain all of its existing customers and earn a period 2 profit equal to $49.99 from each of them.

Finally, imagine that a new, first-time buyer—as yet unaffiliated with either Alpha or Beta—enters the market during period 2. Like other customers, this first-time buyer will wish to purchase a replacement unit—albeit during period 3. Also, like other existing customers, the first-time buyer will face a $50 switching cost after he commits to a vendor.

Assuming once again that Alpha and Beta can offer different prices to existing versus new customers, what price would they offer to this new, first-time buyer during period 2? Following the logic above (and ignoring the time value of money), we can see that the lowest period 2 price that each firm could afford to offer would be $50.01. Whoever secures this new customer would then raise their period 3 price for this customer to $149.99. Total revenue across the two periods would equal $200, as would total cost, and net profit across the two periods would be zero.

In this manner, switching costs allow firms to raise prices to their existing customers, but the resulting profit opportunity also motivates them to race to acquire first-time buyers. In a competitive market, deep discounts or heavy marketing spending to attract these first-time buyers will dissipate any above-normal profits that otherwise would accrue due to switching costs.

Business Model Analysis, Part 2: Platforms and Network Effects

2011-07-23T15:34:00.002-04:00

This post is part of a series on business model analysis for entrepreneurs. The first post in the series presents a comprehensive list of issues (available as a downloadable PDF) entrepreneurs should consider when designing a business model. Other posts delve deeper into specific issues; this one provides an overview of platforms and network effects.

Network effects are evident when any given customer’s willingness to pay (WTP) for a product depends on the number of other customers with whom they can interact by using the product. In the classic example, the first fax machine sold was worthless until someone purchased a second machine. By providing another potential destination for messages, the subsequent arrival of each new fax machine increased the value of every existing machine and likewise increased the WTP of prospects who had not yet acquired a fax machine. (See the technical appendix below for more on how network effects impact customers’ willingness to pay.)

Network effects arise in platform-mediated networks (PMNs), which include networks of customers—often called users—who wish to interact with each other, along with one or more intermediaries who provide a platform, encompassing infrastructure and rules to facilitate users’ interactions. PMNs form the heart of the computer, telecommunications, media, and Internet sectors. However, PMNs are not limited to information industries. They can also be found in financial services (e.g., stock exchanges, credit cards, ATMs), health care (e.g., HMOs), energy (e.g., the power grid), transportation (e.g., airlines, container shipping, gasoline stations), and retailing (e.g., shopping malls, barcodes). A diverse array of matchmaking businesses mediate network transactions, including auctioneers, executive recruiters, realtors, and travel agencies. Ranked by market value, sixty of the world’s 100 largest companies earn >50% of their revenue from platform-mediated networks, including American Express, Cisco, Citigroup, Time Warner, UPS, and Vodafone. In short, PMNs are essential features of modern economies.

Many startups aspire to either build new platforms (e.g., Facebook) or to profit by offering complements that leverage platforms (e.g., Zynga, which relies on Facebook to host its social games). Platform business models can be extraordinarily complex, and design mistakes are common. Consider Sony’s defeat in the Betamax/VHS standards battle. Or the billion dollars that IBM invested in the failed OS/2 operating system. Or the hundreds of millions of dollars squandered when Yahoo! and Amazon launched full frontal assaults on eBay’s dominant U.S. auction franchise. Or eBay’s own failure to capture auction markets in Japan and China when it imported its U.S. pricing model into those markets, where its rivals initially offered free transactions.

It’s especially difficult to get pricing right in platform-mediated networks, so I’ll focus on that topic here. Specifically, I’ll explain why a platform must be proprietary—controlled by a single company—for its provider to capture value created through network growth. I’ll also give a brief overview of rules for setting prices in a “two-sided” network—one with two distinct user groups.

But getting your pricing right is just the tip of the iceberg when designing business models that leverage network effects. It would take a series of long posts to cover all of the relevant topics, so I’ll just mention a few of them below and point to some articles and notes that should be helpful to an entrepreneur grappling with these topics.

(Links marked below with an asterisk are available as PDFs for $7 apiece from HBS Publishing; others are available as free downloads. Sorry for not being able to provide all of my work for free, but we fund our research at HBS through the sales of cases and notes.)

Proprietary Platforms and Pricing Leverage

When a platform-mediated network’s user base expands, the resulting increase in users’ WTP for platform access does not automatically translate into higher prices for the platform. Providers only gain pricing leverage with network growth when they keep their platforms proprietary. A proprietary platform has a single provider who exclusively controls its technology, for example, eBay, Federal Express, or Google. With a shared platform such as barcodes, DVD, or Wi-Fi, multiple firms collaborate in developing the platform’s technology, and then compete with each other to provide differentiated but compatible versions of the platform. Compatibility ensures that users can switch between products offered by a shared platform’s various providers without incurring significant switching costs. For example, when switching from one Windows-compatible PC to another—say, from Dell to Compaq—a user need not invest in new application software. By contrast, switching to a rival platform—for example, from Windows to Macintosh—would be much more costly.

For a shared platform’s providers, lower customer switching costs limit pricing leverage. By contrast, a proprietary platform’s sole provider can raise its price when user base growth increases users’ WTP, up to the point where a user is indifferent between sticking with the platform and switching to a rival platform, if one exists. With such pricing leverage, startups can capture more of the value they create through network effects. Consequently, entrepreneurs will normally prefer proprietary platforms when designing business models. However, as I explain in this article*, there are circumstances when it may be advantageous to share platforms with rivals. In particular, when many equally matched rivals simultaneously enter a new market that is destined to be dominated by a single platform due to very strong network effects, the firms may be better off sharing the market than conducting an expensive winner-take-all battle*. Even though they offer competing versions of a compatible platform, firms may still be able to profit by contributing their technology to the shared platform, perhaps earning licensing fees or gaining a time-to-market edge.

Sharing a platform implies an open architecture. As I’ve written elsewhere on this blog, the concept of an “open platform” can lead to confusion, because platform-mediated networks encompass several ecosystem layers—each of which can be open or closed. In this paper, Geoff Parker, Marshall Van Alstyne and I describe circumstances under which platforms may find it attractive to open or close as they mature. In a more technical paper, we describe a strategy we call platform envelopment, which entails one platform owner moving into another’s market by offering a multi-platform bundle. Through envelopment, firms can surmount the high entry barriers that often protect incumbents who harness strong network effects. Examples include moves by Microsoft (with Explorer, Windows Media Player, Silverlight, etc.), Apple (with the iPhone/iPad, which has enveloped MP3 players, feature phones, PDAs, handheld gaming devices, standalone GPS units, eBook readers, etc.), and Google (with Checkout, Docs, Chrome, Android, etc.).

Pricing in Two-Sided Networks

Platform-mediated networks can be categorized according to the number of distinct user groups they include. In some PMNs, users are fairly homogenous in terms of the platform functionality they require. For example, although a given stock trade has a buyer and seller, these roles are transient; almost all traders play both roles at different times. PMNs with homogenous users are called one-sided to distinguish them from two-sided networks, which have two distinct user groups whose respective members consistently play a single role in transactions (e.g., cardholders and merchants in credit card networks).

Platform providers who seek to mobilize new two-sided networks face a Catch-22: each side will refuse to join until the other side is on board. To avoid this impasse, platform providers often subsidize users on one side, that is, they price below marginal cost to that side or even give its users free access to the platform, as with Adobe’s PDF reader software or Google’s search engine. Due to network effects, attracting users to this “subsidy side” boosts users’ WTP on the network’s “money side,” as with customers for Adobe’s PDF creation software or Google’s advertisers. Generally, it makes sense for platform providers to permanently subsidize the network’s more price sensitive side and charge the side that increases its demand more strongly in response to the other side’s growth.

Parker, Van Alsytne and I discuss pricing rules for two-sided networks in this article.* In a note* for MBAs, Andrei Hagiu and I examine staging strategies that avoid the Catch-22 in two-sided networks by first bringing on board one side in large numbers before seeking to mobilized the other side. Amazon used such a staging strategy. Amazon attracted a big base of customers using a traditional merchant business model, then later morphed into a two-sided platform by hosting third-party merchants who leveraged Amazon’s infrastructure and customer base.

Technical Appendix: Network Effects and WTP

This appendix, adapted from my note* "Racing to Acquire Customers," will only be of interest to readers who like to lift up the hood to understand how a motor works. Below, I dig into the economics of network effects and, using a stylized example, show how they can impact customer willingness to pay, product pricing, and the pace of investment in customer acquisition.

In a market that exhibits network effects, a company must consider the present and future benefits of building its customer base when making current-period pricing and investment decisions. Absent network effects, a company typically maximizes current-period profitability by setting its price and marketing spending at levels that equate current-period marginal revenue and marginal cost. With network effects, however, optimization is less straightforward. Acquiring an additional customer—say, Mary—in the current period yields revenue directly from Mary during that period and also boosts—very slightly—the willingness to pay of all other customers, who value a larger network. Other customers’ willingness to pay remains higher by this tiny increment in future periods, until Mary exits the network. This increase in other customers’ willingness to pay in current and future periods should be factored into decisions about whether to invest in accelerated growth.

These dynamics can be illustrated using an example of a fictional company—let’s call it “Blossom”—selling spreadsheet software in 1989 (before most office productivity software was sold in bundled suites). Then as now, spreadsheet software was subject to a fairly strong network effect: users valued the ability to exchange files and required software that employed a compatible standard to do so. I use this example because scholars have estimated the impact of network effects on customer willingness to pay for spreadsheet software in 1989 using an econometric technique called hedonic regression (Brynjolfsson & Camerer, 1996, “Network Externalities in Microcomputer Software,” Managment Science 42: 1627-1647) Those estimates are employed in the stylized example below to show how network effects can influence optimal marketing spending levels.

Assume that Blossom’s unit price was $360 and its variable expenses (excluding customer acquisition costs) for supplying an additional unit were $72, or 20% of revenue. To keep the analysis simple, we also assume that a new customer would use the product for four years, at which point the product would be rendered technologically obsolete and the customer would be in no way loyal to Blossom. This assumption is unrealistic, but it lets us avoid factoring the present value of future product sales into our calculations. In reality, customers might prefer to buy Blossom’s next-generation product if it provided backward file compatibility or reduced the time spent mastering a new interface.

First, consider a scenario in which spreadsheet software was not subject to a network effect. What is the largest amount Blossom should be willing to spend to acquire an additional customer in 1989? This calculation is straightforward: the company could afford to spend up to $288 ($360 minus pre-marketing variable expenses of $72) to acquire a customer. Beyond $288, the marginal cost of acquiring and supplying a new customer would exceed the marginal revenue from that customer.

How would network effects influence the maximum amount that Blossom should be willing to spend to acquire a customer? The research mentioned above provides the following formula for predicting the logarithm of a spreadsheet’s product price in 1989 (“P”), based on its publisher’s installed base share (“S”), after controlling for differences in product quality. Consistent with the concept of network effects, the formula indicates that customers would pay more for a spreadsheet used by a bigger installed base:

Log P = 5.7376 + .0075S

If we assume that Blossom had a 20.000000% share of the total installed base of spreadsheet software, then this formula would predict customer willingness to pay equal to $360.5389517 (the reason for showing so many decimal places should be apparent in a moment). Assume further that:

The total installed base for spreadsheet software was exactly 50,000,000 in 1989—about half of the actual size of the worldwide installed base of personal computers in that year.
The spreadsheet market was not growing. This is clearly not realistic, but it simplifies our analysis in ways that do not meaningfully change the conclusions.
Annual unit sales for the industry equaled one-quarter of the industry’s total installed base, corresponding to the four-year replacement cycle described above.

Based on these assumptions, how much pricing leverage would Blossom gain by boosting its installed base by exactly one user, from 10,000,000 to 10,000,001? This corresponds to a market share of 20.000002%, which yields a predicted price of $360.5389571, and hence a price increase of $0.0000054. Across Blossom’s 1989 unit sales of 2,500,001, this additional pricing leverage would be worth $13.50 (2,500,001 x $0.0000054). If Blossom maintained its new market share of 20.000002%, it would realize this tiny pricing advantage in each of the three subsequent years. The present value of $13.50 over four years at 15% is $38.54. Thus, with network effects, it would be economically rational for Blossom to invest up to $326.54 ($288 + $38.54) to acquire a new customer—13% more than the $288 maximum it could spend under the “no network effects” scenario.

Obviously, a company cannot raise its price in increments as small as $0.0000054, but larger market share gains may yield material pricing benefits. Specifically, based on the formula above, a 1% share increase yields a 0.75% increase in customer willingness to pay for spreadsheet software in 1989. For this reason, network effects provide an incentive for companies to invest more aggressively in accelerated growth strategies—that is, to race for scale.

Business Model Analysis, Part 1: Key Questions

2011-07-22T15:02:00.008-04:00

Business model analysis is central to the lean startup approach developed by Eric Ries, Steve Blank, and others. When entrepreneurs “run lean,” they formulate hypotheses about major elements of their business model, then they devise experiments to test those hypotheses with minimal waste of time and resources.

But what exactly is a business model? We use the term constantly, with mutual understanding taken for granted. However, the term’s meaning is rarely spelled out, and when it is, definitions can vary widely. At the risk of compounding the confusion, I’ll offer another definition that is based on the concept of strategy taught at the Harvard Business School: A business model is an integrated array of distinctive choices specifying a startup’s unique customer value proposition and how it will configure activities—including those of its partners—to deliver that value and earn sustainable profits. These choices can be grouped into four broad categories pertaining to a startup’s customer value proposition, technology and operations plan, go-to-market approach, and profit formula.

The choices are listed below (and summarized in this single page PDF, downloadable from my Dropbox public folder). My goal is to be comprehensive—to include all of the important issues that an entrepreneur must address when designing a startup’s business model. In my experience, founders sometimes forge ahead, unaware that they’ve failed to consider some crucial issues. I hope the list helps prevent some “I-wish-I’d-thought-of-that-sooner” mistakes.

In a series of future posts, I’ll describe how my ideas about business models relate to work by John Mullins & Randy Komisar, Clay Christensen, Alex Osterwalder, and Ramon Casadesus-Masanell. I’ll also share some practical guidelines for conducting business model analysis. Finally, I’ll delve deeper into several topics that are salient for early-stage startups, for example, network effects, switching costs, and other sources of first mover advantage; attributes of a scalable model; LTV/CAC analysis; viral coefficients; and “chasm” challenges.

After I incorporate feedback on the posts, my plan is to consolidate them into a note on business model analysis that will be available in the fall of 2011 from Harvard Business Publishing.

Customer Value Proposition

The starting point when designing a new venture’s business model is articulating a customer value proposition. This entails addressing the following questions:

What unmet needs will the venture’s product address?

In creating customer value, will the venture emphasize differentiation, by increasing customer willingness to pay relative to rivals’ offerings, or low cost, by reducing expenses that customers incur—again, relative to rivals’ offerings—for a comparable bundle of benefits? If it emphasizes differentiation, will the venture’s edge principally be vertical (i.e., outperforming rivals’ products on dimensions for which most customers would agree that “more is better,” for example, a car’s gas mileage) or horizontal (i.e., distinguishing itself on dimensions of taste that cannot be intrinsically rank ordered, for example, an Audi’s styling compared to a Saab’s)?

Which customer segments will the venture serve upon launch? How will targeted segments change over time?

Using Steve Blank’s terms from Four Steps to the Epiphany, will the venture serve: 1) a fundamentally new market, offering a radically innovative product; 2) an existing market, offering a product that offers superior relative performance against well-established benefit and/or cost metrics; or 3) a re-segmented market, offering a product that offers superior performance on familiar attributes strongly valued by a subset of the existing market’s customers?

How large is the total addressable market (TAM) for the venture’s product, and how fast is the market likely to grow?

What will be the venture’s minimum viable product for launch, that is, the smallest set of features needed to validate key business model assumptions? What is the plan for adding features over time, that is, the product roadmap?

To access a whole product solution, will customers need to acquire any complements or ancillary services from third parties? If so, who will provide them, and under what terms?

Will pricing be structured per transaction, per period (as with a subscription), or through some hybrid approach? Will prices/fees be: 1) fixed per transaction/period (e.g., a $20 book; a flat fee for a consulting assignment; a $10 monthly subscription to Netflix), 2) variable, based on a fixed fee per unit of activity (e.g., hourly billing for a legal case; a per unit royalty to a patent holder; a per minute charge for long distance calls), 3) tiered based on feature/service level (e.g., “freemium” pricing that is free for a basic product or $10/month for a “pro” version) or 4) linked to some outcome (e.g., “pay-per-click” advertising; a broker’s fee on a home sale)?

If the venture’s product is a physical good, will it be sold outright—transferring title—or will it be rented/leased?

Will the venture pursue a skimming strategy, that is, charging a premium to early adopters with high willingness to pay for the product? Alternatively, will the venture engage in penetration pricing to exploit strong scale economies or other first mover advantages?

Does the venture have opportunities to capture value through price discrimination, for example, through negotiated pricing, auctions, or discounts for early booking? Through bundling, for example, by including after-sale service with the product’s purchase? (See Part 7 of this series for an overview of bundling.)

Will the venture leverage strong, proprietary network effects? (See Part 2 of this series for an overview of network effects)

What switching costs will confront customers, and what will be the expected average life of a customer relationship? (See Part 3 of this series for an overview of switching costs.) Can the venture improve customer retention through incentives, for example, a penalty for early contract termination or a rewards program for heavy users?

Relative to offerings from likely rivals, how will customers’ willingness to pay (WTP) for the venture’s product compare to their expected total cost of ownership (TCO)?

Technology and Operations Plan

Having defined their customer value proposition, entrepreneurs next should consider the following choices for technology and operations management:

What activities are required to develop and produce the venture’s core offerings?

Which of these activities should the venture perform in-house? Put another way, to what extent should the business be vertically integrated? Who will perform outsourced activities, and under what terms?

What are the cost drivers for key activities (e.g., unit volume, capacity utilization, number of customers)? Can the venture exploit scale economies in operations by substituting fixed for variable costs? By leveraging learning-by-doing opportunities?

Will the venture create any valuable intellectual property? If so, how will this IP be kept proprietary?

How strong are first mover advantages (FMAs) related to technology and operations management, for example, scale economies in production or preemptive access to scarce production inputs? How do FMAs compare to late mover advantages, such as opportunities to reverse engineer pioneers’ products or leapfrog leaders by leveraging new technology?

Given capacity and hiring constraints, will rapid scalability be possible?

Go-To-Market Approach

A go-to-market plan specifies how a new venture will generate and fulfill demand, addressing the following choices:

What mix of direct channels (e.g., in-house sales force; company website; wholly-owned retail stores) and indirect channels (e.g., wholesalers; independent reps; value-added resellers; franchisees; third-party retailers) will the venture employ to educate prospects, configure and deliver its products, provide after-sale service, give feedback for future product development efforts, etc.? What margin will channel partners require? Should any partners be granted exclusive distribution rights?

Does the venture have strong incentives to race for scale due to network effects, high switching costs, or other first mover advantages (e.g., economies of scale in production; opportunities to preemptively acquire scarce assets, patents, or capacity)? How do these incentives compare to factors that may discourage aggressive investments in customer acquisition, in particular, scalability constraints and late mover advantages (e.g., opportunities to reverse engineer pioneers’ products; to leapfrog them with superior new production technology; to free ride on their missionary marketing efforts; and to avoid their positioning errors)? (See Part 4 of this series for an overview of factors that encourage and discourage racing for scale.)

Given the expected lifetime value (LTV) of a customer, what average customer acquisition cost (CAC) will the venture target? (See Part 6 of this series for an overview of LTV and CAC calculations.)

What mix of free and paid demand generation methods (e.g., mass and targeted advertising; product sampling; trade promotions; “freemium” pricing; public relations; customer word of mouth) will the venture employ at each stage of the conversion funnel (i.e., awareness > interest > trial > repurchase)? What will be the resulting shape of the funnel? What will be the average CAC for each paid demand generation method?

If the venture relies heavily on free customer acquisition methods, how will its product’s design encourage virality, and what will be its viral coefficient? (See Part 5 of this series for an overview of virality and viral coefficients.)

If the venture sells a fundamentally new product, is it likely to confront what Geoffrey Moore describes as a chasm between early adopter and mainstream customer segments? If so, what is the plan for crossing the chasm? (See Part 8 of this series for an overview of chasm issues.)

Profit Formula

Analysis of a new venture’s profit formula does not require an entrepreneur to make additional choices about business model design. Rather, this analysis evaluates the venture’s long-term economic viability, based on assumptions about its customer value proposition, technology and operations management, and go-to-market plan. To confirm that these assumptions yield a profitable business, an entrepreneur must answer the following questions:

What contribution margin (revenue - variable costs/revenue) will the venture earn? What will be its unit economics, that is, contribution per unit of product sold?

What fixed costs will the venture incur? What breakeven level of capacity utilization and sales volume does this imply?

What share of the total addressable market does the breakeven sales volume represent?

How much investment in working capital and property, plant & equipment will be required per dollar of revenue? Can the venture reduce working capital by delaying payments to suppliers? Receiving payments from customers before delivering its product (as with subscriptions)? Shifting inventory to partners?

How will the venture’s contribution margin, fixed costs, and investment/revenue ratio change as the business scales?

Given projected growth, what is the profile of the venture’s cash flow curve? In particular, how deep is the curve’s trough, and when will it be reached?

It is useful to analyze a new venture’s economics by following accrual accounting principles for the timing of revenue recognition and the depreciation and amortization of investments. However, for resource-constrained early-stage startups, managing cash flow is absolutely crucial. For this reason, when designing business models, most entrepreneurs should put accrual accounting on the back burner—at least at temporarily—and focus on cash flow using this formula, which captures the key metrics in the questions above:

In the formula, TAM = Total Addressable Market, VC = Variable Costs, and FC = Fixed Costs.

Projecting the formula above over time—and taking into account any interest payments and taxes—yields the venture’s cash flow curve, which plots cumulative cash flow from operations. The curve reveals the two most important facts that an entrepreneur needs to know about her business model: what is the magnitude of maximum cumulative negative cash flow, and when will that point be reached?

So, is this the right list of issues? Do any questions seem too trivial or vague? Have I left out anything important?

Launching Tech Ventures: Part IV, Readings

2011-01-08T11:19:00.012-05:00

This is the fourth of four posts about Launching Technology Ventures (LTV), a new MBA elective course I'm developing at Harvard Business School to explore lean startup management practices. Part I provides an overview of concepts covered in the course. Part II describes LTV's class sessions. Part III lists a set of tools and techniques that I think any MBA working in a tech venture should master.

[Addendum, Apr. 10, 2011: if you are interested in lean startup concepts, you should also check out the LTV course blog. Students write blog posts instead of taking an exam. They've done terrific work.]

Below, I've compiled a list of readings — mostly blog posts, but also some books — that cover topics relevant to my course, i.e., lean startup management practices, product marketing/management, and business development. I don't list readings on funding a startup, motivations for founding, or co-founder relationships. For readings on those topics, see my earlier compilation for web entrepreneurs.

Of course, learning-by-reading is no substitute for learning-by-doing — but almost everything on the list was written by people who work or invest in new ventures, so there's a lot of wisdom here. If I've missed valuable readings, please let me know in your comments below.

Lean Startup Concepts

Steve Blank's Four Steps to the Epiphany details Blank's customer development process. This MUST READ book lays the foundation for lean startup principles; Blank's blog amplifies the ideas in his book, e.g., why you need to get out of the building to interview users, attributes of a scalable startup, the definition of a business model, and why startups should track progress with hypothesis validation, not traditional accounting metrics.
Eric Ries explains lean startup principles in a series of MUST READ blog posts; topics include the minimum viable product concept, when to release your first product, the use of Toyota-style "five whys," the value of split test experiments, how to manage a business model transition, a case study of continuous deployment, macro vs. micro testing in lean startups, and the merits of continuous deployment.
Marc Andreessen of Andreessen Horowitz on product-market fit.
Ben Horowitz of Andreessen Horowitz on the lean vs. "fat" startup tradeoffs and the elusive nature of product-market fit.
Getting to Plan B by John Mullins (London Business School) and Randy Komisar (Kleiner Perkins) explains why and how entrepreneurs should pivot.
Running Lean, by entrepreneur Ash Maurya, summarizes lean startup/customer development principles and does a great job of adapting Alex Osterwalder's business model generation process to web startups. Maurya's blog extends these ideas; he applies customer development processes to his current startup in this post, and discusses metrics in this one.
The Entrepreneur's Guide to Customer Development by Brant Cooper and Patrick Vlaskovits provides a clear summary of Blank's ideas and other lean startup concepts.
Reflections on lean principles from Bradford Cross.
The case against releasing early/often from Jason Cohen at OnStartups.com.
A 3-part post from Ooga Labs on startup management practices for "going fast."
Alistair Croll and Sean Power of Watching Websites on lean startup metrics to track.
Mark Suster of GRP Partners on the wisdom (and ethics) of failing fast.
Michael Woloszynowicz on misperceptions about minimum viable products.
Introduction to lean startup principles by Abby Fichtner (@HackerChick), Microsoft's developer evangelist for startups.
Tristan Kromer, co-founder of StartupSQUARE, develops a taxonomy of pivot types.
Design firm slicedbread on when continuous feature/fix releases are good and bad for users.
Aardvark co-founder Max Ventilla on user-driven design.
A step-by-step example from Aymeric Guarat-Apelli of "smoke testing," i.e., assessing the viability of a new business concept by measuring consumer response to ads for a dummy site; Aymeric was inspired by this post by Tim Ferriss on how he used social media and A/B testing to build "Four Hour Workweek" into a best-seller.
Donald Reinertsen is a consultant and author of Principles of Product Development Flow. The book is dense and theoretical, but patient readers will learn a lot about the economic benefits of fast development cycles.

Business Model Analysis

Presentation by David Skok of Matrix Partners on the SaaS business model plus related post.
Steve Carpenter's TechCrunch teardown of generic web business models and key metrics for each.
Dave Chappell, founder of TeachStreet, on challenges when transitioning from a free to paid model; MailChimp on their experience with this transition.
Dharmesh Shah of Hubspot lists 16 factors for success with a SaaS model; another post from Shah on the same topic.
Nice summary of the freemium model by Spencer Fry, CEO of Carbonmade. For more, see Chris Anderson's book Free.
Chris Dixon post describing Geoffrey Moore's concept of the "bowling pin" strategy for solving the chicken-and-egg problem when mobilizing network users.

Product Management

Marty Cagan of Silicon Valley Product Group's Inspired is a comprehensive guide to the product manager role; a MUST READ book; the SVPG blog extends the book's insights.
All product managers should be familiar with Geoffrey Moore’s classic books Crossing the Chasm (a MUST READ) and Inside the Tornado
Rob Go of NextView Ventures had compiled a series of interviews with senior product managers.
The Product Box, a technique from Innovation Games for eliciting product feature ideas from consumers.
Quora response by Ian McAllister of Amazon on Amazon's approach to product management.
David Barrett, founder/CEO of Expensify, on "Why products suck" and how to improve them; Marty Cagan of Silicon Valley Product Group on the same topic.
Marty Cagan of Silicon Valley Product Group on pros and cons of having a startup CEO as head of product.
Fred Wilson of Union Square Ventures on challenges in product and engineering as startups scale and principles for web app design.
Mark Suster of GRP Partners on designing products for mass adoption.
Jacques Murphy of Pragmatic Marketing on introducing formal product management at a startup.
Presentation by Joshua Porter of Performable on designing social media products for virality.
User experience designer Whitney Hess on how to conduct user interviews.
Vikas Vadlapatla on resources for learning about usability.
Marketing guru David Meerman Scott offers examples of buyer personas and how they are used in product development and marketing.
Creating Passionate Users blog on managing expectations and securing useful feedback with a product demo.
A primer from Six Revisions on wireframing.
Hacker News thread on how to learn design basics.
The Inmates are Running the Asylum by Alan Cooper ("The Father of Visual Basic") makes the case for interaction designers playing a pivotal role in tech product development.
Extreme Programming Explained by software guru Kent Beck and Cynthia Andres provides a good introduction to the advantages and key practices of agile software development.

Customer Conversion Funnel Analysis/Optimization

Avinash Kaushik, analytics evangelist for Google, is the author of the Occam's Razor blog and Web Analytics 2.0, a MUST READ book that provides comprehensive and practical guide to analytics.
Viral marketing analytics, including viral coefficients, explained by David Skok of Matrix Partners in one of several MUST READ posts, which also include customer acquisition funnel optimization, the need to balance average customer acquisition cost with lifetime value of a customer, and metric-driven business model analysis.
Startup metrics discussed in this MUST READ presentation by serial entrepreneur and angel investor Dave McClure, in which he describes his "AARRR" framework: acquisition, activation, retention, referral, revenue.
Inbound Marketing by HubSpot co-founders Brian Halligan and Dharmesh Shah offers advice on how to use Google, Facebook, Twitter, blogs, etc. to drive traffic to a website; presentation by Mike Volpe of HubSpot on this topic; HubSpots Marketing Hubs collect a wealth of resources and articles on online marketing.
ReadWriteWeb on the pros and cons of A/B testing for startups.
ABtests.com shows side-by-side examples of test pages and their performance.
Chance Barnett, founder of GIG.FM, on landing page optimization; FormStack on the anatomy of a perfect landing page.
KPI Library (via Mirror42) on measuring customer retention.
Jeremy Liew of Lightspeed Venture Partners on how to calculate lifetime customer value with cohort analysis.
Kontagent on conversion funnel metrics for social games.
Ash Maurya on troubleshooting trial offers.
Presentation on startup marketing by Highland Capital's Michael Gaiss. See links to SEO/SEM online resources on slide 33.
Design firm slicedbread on when to use A/B testing vs. qualitative user research.
Tips on going viral from Irata Labs CEO Chris Abad; Mashable on how startups build online communities.
Rand Fishkin, CEO of SEOmoz, on how to set up a new site for analytics.
Case study by Luke Wroblewski on how Twitter boosted sign-ups through gradual engagement.
A Stochastic Technologies' historious blog post cautions A/B testers to get more data from their tests; the post cites an A/B test of identical pages that yielded, with 99.8% confidence, a 30% improvement for one of the pages after 1,000 visits and 3 days! After a few more days with more data, the improvement faded. Jason Cohen, founder of Smart Bear Software, explores the similar issues in this post.

B2B Selling

David Skok of Matrix Partners on building a sales and marketing machine; a MUST READ post; Skok on how sales complexity impacts customer acquisition cost -- and how to mitigate its impact.
MUST READ Harvard Business Review article by Mark Leslie and Charles Holloway on the sales learning curve. Academics: note that Leslie, former founder/CEO of Veritas, has helped build a terrific case-based course on enterprise software sales management at Stanford Business School, STRAMGT 351.
Ben Horowitz of Andreessen Horowitz on how the priorities of enterprise customers have evolved.
Software entrepreneur David Cummings on cold-call hit rates.
Mark Suster of GRP Partner on scaling a salesforce, why seasoned sales reps often don't work out, tools/information required by a startup salesforce, and on dangers that startups face when they rely on channel partners for sales.
Steve Blank on how sales culture must change as an enterprise software startup matures, customer development in big companies, and firing customers
HubSpot co-founder and CTO Dharmesh Shah on building a sales team.

Public Relations

Mark Hendrickson, co-founder of Worldly Developments (and former TechCrunch blogger) on how to pitch a tech blogger.
Brant Cooper makes the case that startups should not hire a PR agency.
TechCrunch post on PR impact of SXSW launches of Twitter and Foursquare.
Mark Suster of GRP Partners on how to manage relationships with journalists and on how to blog.
Serial entrepreneur Jason Calacanis on running a trade show booth and why you should fire your public relations firm.
TechCrunch's Michael Arrington on public relations in Silicon Valley.
Xobni founder Matt Brezina on how to drive early traffic through PR strategies. Be sure to follow link in post to slideshow.

Business Development

Charlie O'Donnell of First Round Capital describes business development challenges for startups.
Alex Iskold, founder of AdaptiveBlue, describes how biz dev is evolving in parallel with the APIs of platform-based businesses; Shaival Shah on how to substitute an API for bus dev.
Angel investor/Hunch CEO Chris Dixon on how startups should think about incumbents.
Mark Suster of GRP Partners on why you're most vulnerable right after you've won a deal.
Mashable on key factors for success in biz dev jobs.
HubSpot co-founder and CTO Dharmesh Shah on the dangers of partnering with big firms.
Marc Andreessen on how dealing with big firms is like pursuing elusive, unpredictable and dangerous Moby Dick.

Recruiting/Organizational Issues

Vinicius Vacanti, co-founder of Yipit, on how to find a technical co-founder and if you can't, whether to hire a programmer or to teach yourself to build a prototype; Hacker News thread on whether a non-technical founder should teach him/herself to code.
Michael Woloszynowicz on finding a technical co-founder.
Miriam Naficy, founder of Minted.com on how to hire a startup CTO.
Mark Suster of GRP Partners on CTO vs VP Engineering roles.
Angel investor/Hunch CEO Chris Dixon summarizes ideas from software guru Joel Spolsky about how to hire programmers.
Ken Norton on how to hire a product manager.
Jason Cohen on hiring startup marketing talent.
Charlie O'Donnell of First Round Capital on reasons for hiring a design lead before a CTO or technical co-founder.
Mark Suster of GRP Partners on which functions need to be co-located in a startup, and on hiring salespeople.
Ben Horowitz of Andreessen Horowitz on organizational challenges confronting startups as they scale rapidly.
Eric Ries on why startups should not have departments, sources of engineering-marketing friction, and the right amount of process for a startup.
Investor/serial entrepreneur Furqan Nazeeri on what functions startups can outsource; Fred Wilson of Union Square Ventures on outsourcing and offshoring; Vivek Wadhwa on the pros and cons of outsourcing product development.
Steve Blank on how the transition to professional management can have unintended consequences as a startup matures; more from Steve on the same theme .

More Books and Tools

Do More Faster, edited by Brad Feld and David Cohen, compiles advice across a range of topics from TechStars entrepreneurs and mentors.
Founders at Work, by Y Combinator's Jessica Livingston, collects her interviews with two dozen founders relating their lessons learned.
Tom Hulme at IDEO has compiled and crowd-sourced a list of tools for tech startups, organized by function and company life cycle stage; a similar list compiled by Shyam Subramanyam; another list from Jaret Manuel; and my own list of free software tools for lean startups.

Launching Tech Ventures: Part III, Tools & Techniques

2011-01-07T11:51:00.016-05:00

This is the third of four posts about Launching Technology Ventures (LTV), a new MBA elective course I'm developing at Harvard Business School to explore lean startup management practices. Part I provides an overview of concepts covered in the course. Part II describes LTV's class sessions. Part IV lists recommended readings for the course.

[Addendum, Apr. 10, 2011: if you are interested in lean startup concepts, you should also check out the LTV course blog. Students write blog posts instead of taking an exam. They've done terrific work.]

Here’s a preliminary list of tools and techniques that I think MBAs should master if they are working in product management, marketing, or business development functions in tech startups or in new ventures in established tech companies. In LTV, students will work on a team-based field project that applies one of these tools/techniques in a new venture.

IDEO-style Idea Generation. IDEO has a structured approach for applying ‘design thinking’ to new product development.

Business Model Generation. Ash Maurya has adapted Osterwalder’s business model generation framework for use in info tech startups.

Customer Discovery Interviews. In Four Steps to the Epiphany, Steve Blank describes interview processes for assessing demand for a product idea.

Focus Group Research.

Survey Research.

Persona Development. A persona is a fictional character with demographic, psychographic, and behavior attributes of a representative product user. Many tech companies assess new features in terms of their fit with personas’ needs.

Competitor Site Benchmarking.

Wireframing. A wireframe is a skeletal depiction of a website showing key navigational concepts and page content. Many wireframing software tools are available, e.g., Balsamiq.

Prototype Development.

Usability Tests. For $39, UserTesting.com will videotape a representative consumer trying a product or feature.

A/B Test of New Feature.

Conversion Funnel Analysis/Optimization.

Landing Page Optimization. Performable specializes in landing page optimization.

Search Engine Marketing Campaign Design/Optimization.

Inbound Marketing Campaign Design/Delivery/Analysis. This book by Hubspot’s founders explains the logic for relying on blogs, Facebook, Twitter, etc. to drive traffic to a site.

Public Relations Strategy. Startup CEOs disagree on whether a DIY approach to PR makes sense, and how long startups should wait before they invest money/time in PR.

Site Redesign Based on User Experience Analysis.

Analysis of User Requests for Product Support.

Analysis of User Suggestions for New Features. Dropbox has implemented a site feature for soliciting feature suggestions, called “Votebox."

Product Roadmap/Feature Prioritization Process Design. Startups who rely on agile development methods use project management tools such as Pivotal Tracker.

Design/Delivery of Sales Pitch for Early Adopters.

Sales Lead Prioritization Process.

Design/Delivery of Charter User Program. Silicon Valley Product Group, a consulting firm that helps tech companies improve their product management capabilities, makes the case for a structured approach to working with beta customers.

Net Promoter Score Analysis. NPS, a measure of product loyalty used by many tech companies, is obtained by asking customers: "How likely is it on a 0-10 scale that you’d recommend our product to a friend/colleague?" Respondents are categorized as Promoters (9-10 rating), Passives (7-8), or Detractors (0-6). NPS = Promoter % - Detractor %.

Lifetime Value of a Customer Analysis.

The links in the list above are added to help with definitions rather than to serve as a comprehensive guide for someone who wants to learn about these techniques. See Part IV of this series for more books and blog posts that discuss the techniques and their application.

Over time, I’d like to build a knowledge base — perhaps through a wiki — that captures what MBAs and others working in tech ventures need to know about these tools and techniques. In particular, when/where/how/why is the technique best used? Are there conditions where the technique is not appropriate? What mistakes do people make with the technique? How can MBAs acquire the skills needed to apply the technique?

I'd value your feedback. What tools/techniques are missing from list? Do you have thoughts about the questions I pose above?

Launching Tech Ventures: Part II, Class Sessions

2011-01-07T10:43:00.006-05:00

This is the second of four posts about Launching Technology Ventures (LTV), a new MBA elective course I'm developing at Harvard Business School to explore lean startup management practices. Part I provides an overview of concepts covered in the course. Part III lists a set of tools and techniques that I think any MBA working in a tech venture should master. Part IV lists recommended readings for the course.

[Addendum, Apr. 10, 2011: if you are interested in lean startup concepts, you should also check out the LTV course blog. Students write blog posts instead of taking an exam. They've done terrific work.]

Here’s a preliminary syllabus for the 15 class sessions of LTV, which will start in late January and run through early March.

Dropbox. Accel/Sequoia-backed Dropbox offers a cloud-based service that allows users to synchronize and share files across PCs and smart phones. We’ll use this case to introduce several course concepts, including product-market fit, user-centered product development, customer conversion funnel optimization in a ‘freemium’ SaaS context, and business development challenges for early-stage ventures negotiating with large corporations.
IMVU. Guest: Eric Ries, IMVU co-founder & former CTO and HBS Entrepreneur-in-Residence. We’ll discuss how he and his colleagues pioneered lean startup principles at Menlo Ventures-backed IMVU, which offers an instant messaging service built around 3-D avatars. Eric will also discuss how lean startup concepts have evolved and whether the concepts are applicable beyond info tech startups.
Triangulate. Trinity Ventures/angel-backed Triangulate offers a Facebook-based dating service. We’ll explore how this startup has used lean startup principles to guide product development through several pivots.
Cake Financial. Cake was a website for consumers seeking to improve their investment portfolio performance. Although this TechCrunch40 finalist raised $9 million in venture capital and got press accolades, it never achieved strong product-market fit, and Cake’s assets were sold to E*Trade last year. The case explores whether this outcome was inevitable given market conditions, and what, if anything, the team might have been done differently.
David Skok, General Partner, Matrix Partners. David will share insights on SaaS economics, customer conversion funnel optimization, and how to build a sales and marketing ‘machine’ in an early-stage venture.
Rentjuice. Angel-backed Rentjuice provides web-based tools for rental real estate agents. Building on Skok’s ideas, we’ll explore issues in building direct sales capabilities in a SaaS-based early-stage venure.
Aardvark. Aardvark, backed by August Capital and subsequently acquired by Google, is a social search service. We’ll focus on Aardvark’s use of ‘mechanical Turk’ product development processes. To learn about customers’ needs, the company substituted human operators for algorithms in routing beta users’ search queries to parties in a social network.
Mochi Media. Mochi, backed by Accel and subsequently acquired by Shanda, developed a three-sided platform, connecting Flash game developers, sites that aggregate these games, and advertisers. We’ll use this case to explore the applicability of lean startup principles to platform-based businesses that harness strong network effects, along with the challenges of a ‘middleware’ business model that shares a large fraction of value with platform partners.
Fred Wilson, Managing Partner, Union Square Ventures. Fred will discuss the new investor ecosystem that is evolving to serve capital-efficient startups (e.g., “super angels,” seed-funds, etc.), along with his perspectives on the tradeoffs between lean and “fat” strategies for building startups. He will be interviewed by Jeff Bussgang, Flybridge Capital General Partner & HBS Entrepreneur-in-Residence.
Predictive Biosciences. Flybridge/Highland/NEA-backed Predictive has developed accurate and non-intrusive (‘pee-in-a-cup’) cancer diagnostic tests. We’ll explore the interplay between customer requirements and product development priorities in a life sciences context, and ask whether lean startup principles are applicable .
Aquion Energy. Aquion is developing a low-cost, long-lasting, sodium ion battery suited for grid-scale energy storage. We’ll discuss product and customer development priorities for an early-stage venture confronting the clean tech ‘valley of death,’ i.e., the challenge of raising more equity capital than VCs are typically willing to provide under conditions deemed too risky for debt financing.
Chegg. Kleiner-backed Chegg dominates the emerging textbook rental business. We’ll examine pressures that the company encountered as it grew explosively, and how management is responding to scaling challenges.
foursquare. Union Square/Andreessen Horowitz-backed foursquare offers location-based services. We’ll use this session to explore the applicability of lean startup principles to platform-based businesses that harness strong network effects. In particular, in such businesses, is it possible/advisable to achieve product-market fit before you identify a monetization strategy?
OPOWER. Accel/Kleiner-backed OPOWER helps electric utilities improve efficiency by delivering reports to consumers about their energy use. We’ll focus on how the company introduced formal product management processes.
Course Wrap.

LTV has four modules. The first six company cases (Dropbox through Aardvark) examine challenges confronting early-stage ventures as they search for product-market fit. The last three company cases (Chegg, Foursquare, OPOWER) plus Mochi Media explore issues that tech ventures encounter when scaling, after achieving product-market fit. A third module consists of sessions on Predictive Biosciences and Aquion Energy, in which we’ll test the applicability of lean startup principles beyond info tech in capital-intensive, science-based ventures. The fourth module consists of sessions with two venture capital partners who will address topics of general relevance to early-stage ventures.

The cases won't be available through Havard Business Publishing until the spring. If you are an academic and you want access to a case sooner for use in your course, contact me and I'll try to expedite availability.

In the meantime, I'd be grateful for feedback. Next year, I'd like to add a case about a big tech company (e.g., Google, Amazon) launching a new product. What else is missing? Can you suggest other case studies that would complement these?

Launching Tech Ventures: Part I, Course Overview

2011-01-07T09:46:00.004-05:00

This is the first of four posts about Launching Technology Ventures (LTV), a new MBA elective course I'm developing at Harvard Business School to explore lean startup management practices. Part II will describe LTV's class sessions. Part III will list a set of tools and techniques that I think any MBA working in a tech venture should master. Part IV lists recommended readings for the course.

[Addendum, Apr. 10, 2011: if you are interested in lean startup concepts, you should also check out the LTV course blog. Students write blog posts instead of taking an exam. They've done terrific work.]

Most startups fail — usually due to lack of customer demand, not product development problems. These new ventures burn through their capital, wasting money on engineering and marketing before discovering they have built a product no one wants. Startups are more likely to succeed when they rapidly and iteratively test assumptions about a new venture’s business model based on customer feedback, then quickly refine promising concepts and ruthlessly cull the flops. New ventures that follow this approach are lean startups. “Lean” invokes the image of bootstrapping entrepreneurs, sustained by ramen noodles and a dream. Some lean startups fit that image. However, the term “lean” is derived from Toyota’s management philosophy. Toyota uses short production cycles to reduce inventory and eliminate waste. Lean startups similarly rely on short product development cycles to eliminate waste and gain rapid market feedback.

Lean startup practices are being pursued by firms in Silicon Valley and beyond. These practices have gained special traction in the information technology sector, where rapid “build/measure/learn” cycles are facilitated by the availability of open source engineering tools and Internet marketing channels. However, lean practices can also be applied by startups in other sectors and by large corporations launching new products.

Launching Technology Ventures uses case studies to examine lean startup practices. LTV focuses on the integration of marketing and engineering functions and emphasizes implementation rather than strategy formulation issues. The course does not examine financing options or the composition of founding teams. LTV draws heavily on the ideas of Eric Ries, Steve Blank, Marty Cagan and other practitioners. Ries coined the term “lean startup” when he connected ideas from lean manufacturing and agile software development to Blank’s customer development process.

Course Structure and Key Concepts

LTV is organized into two modules that explore execution challenges before and after a startup achieves product-market fit, i.e., a match between its product solution and market needs.

The first module about challenges prior to achieving product-market fit covers the following core concepts:

The importance of well-structured experiments to confirm or disprove hypotheses about uncertain business model elements, thereby securing what Ries calls validated learning.
The benefits of what Ries calls a minimum viable product (MVP), i.e., the smallest set of product features and business initiatives needed to secure the next round of validated learning. MVPs can be counter-intuitive for managers and entrepreneurs, who often see fully-featured products as being better. However, building more features than necessary risks wasting time on functionality no one wants. It also compromises experimental designs, because it can be difficult to determine why a customer rejected a new version that incorporates many simultaneous changes.
The value of rapidly iterating the MVP based on customer feedback obtained through interviews, focus groups, usability tests, customer support interactions, etc.
Pivoting, i.e., changing a startup’s business model based on validated learning. When they pivot, startups retain some elements of their prior model to avoid waste. Lean startups may make many small pivots or a few big ones.
The need for metrics to gauge whether business model hypotheses have been validated, e.g., viral coefficients, customer retention rates.
The benefits of bootstrapping and avoiding big investments in marketing and infrastructure until business model hypotheses are validated.

In the first module, in addition to covering these core concepts, we’ll consider several issues of practical concern to marketing and product managers in early stage companies, for example:

When should a startup outsource engineering work?
How do firms design products for virality?
In a B2B context, how can an unknown startup with an unproven product identify potential early adopters and craft an effective sales pitch for them?
When does a “do-it-yourself” approach to public relations make sense, and how long should startups wait before they aggressively invest in PR?

The second module addresses challenges when scaling a business after achieving product-market fit. Topics include:

Approaches to customer conversion funnel analysis and optimization, e.g., usability labs, A/B testing.
Metrics for scaling startups, e.g., Net Promoter Score, Lifetime Value of a Customer.
Challenges in scaling a direct sales force; processes for prioritizing sales leads.
Tradeoffs for startups relying on business development partnerships with large companies.
Why, when and how startups should introduce formal product management processes, e.g., project prioritization and tracking systems, product roadmaps.

Boundaries

In addition to the issues described above, LTV will consider whether lean startup principles apply in three special contexts:

Platform-Based Businesses. "Do not scale until you validate your business model" is a core principle for lean startups. But does this make sense for platform-based businesses that harness strong network effects, such as Facebook, YouTube, and Twitter? All these firms scaled aggressively before they had proven business models; they subsequently relied on ecosystem partners to experiment with ways to monetize their big platforms.
Science-Based Businesses. For a class of capital- and/or science-intensive ventures—e.g., biotech, clean tech—the rapid iteration that is central for lean startups may not be a practical option. For such products, development and/or deployment inherently takes a long time due to fundamental uncertainty about engineering approaches or delays in deploying production capacity.
Large Corporations. Big companies have deep pockets and ample resources; that is their principal advantage over nimble startups. So, why should big companies constrain themselves unnaturally by running lean?

I'm eager to get feedback on the concepts covered in my course. What seems off target or missing?