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Lean Analytics

Cover of Lean Analytics by Alistair Croll... Published by O'Reilly Media, Inc.
  1. Lean Analytics
  2. Dedication
  3. Preface
    1. Who this book is for
    2. How the book works
    3. The building blocks
      1. Customer Development
      2. Lean Startup
    4. About the authors
    5. Thanks and acknowledgements
  4. I. Stop lying to yourself
    1. 1. We’re all liars
      1. The Lean Startup movement
      2. Poking a hole in your reality distortion field
      3. Case study: AirBnB Photography—growth within growth
    2. 2. How to keep score
      1. What makes a good metric?
      2. Qualitative versus quantitative
      3. Vanity metrics versus real metrics
      4. Pattern: Eight vanity metrics to watch out for
      5. Exploration versus reporting
      6. Case study: Circle of Moms explores its way to success
      7. Leading metrics vs. lagging metrics
      8. Moving targets
      9. Case Study: HighScore House defines an “active user”
      10. Segments, cohorts, A/B testing, and multivariate analysis
      11. The Lean Analytics cycle
      12. Exercise: Evaluating the metrics you track
    3. 3. Deciding what to do with your life
      1. The Lean Canvas
      2. What should you work on?
      3. Exercise: Create a Lean Canvas
    4. 4. Data-driven vs. Data-informed
      1. Pattern: How to think like a data scientist
      2. Lean Startup and Big Vision
  5. II. Finding the right metric for right now
    1. 5. Analytics Frameworks
      1. Dave McClure’s Pirate Metrics
      2. Eric Ries’s Engines of Growth
      3. Ash Maurya’s Lean Canvas
      4. Sean Ellis’s Startup Pyramid of Growth
      5. The Long Funnel
      6. Introducing the Lean Analytics Stages and Gates
    2. 6. The Discipline of One Metric That Matters
      1. Case Study: SEOmoz tracks fewer KPIs to increase focus
      2. Four reasons to use the One Metric That Matters
      3. Case study: Solare focuses on a few key metrics
      4. Drawing lines in the sand
      5. The squeeze toy
      6. Exercise: Define your OMTM
    3. 7. What business are you in?
      1. About those people
      2. The Business Model Flipbook
      3. Six business models
      4. Exercise: Pick your business model
    4. 8. Model one: E-commerce
      1. Pattern: What mode of e-commerce are you?
      2. A practical example
      3. Conversion rate
      4. Purchases per year
      5. Shopping cart size
      6. Abandonment
      7. Cost of customer acquisition
      8. Revenue per customer
      9. Case study: WineExpress increases revenue by 41% per visitor
      10. Keywords and search terms
      11. Recommendation acceptance rate
      12. Virality
      13. Mailing list click-through rates
      14. Offline and online combinations
      15. Visualizing the e-commerce business
      16. Wrinkles: Traditional e-commerce vs. subscription e-commerce
      17. Key takeaways
    5. 9. Model two: Software-as-a-Service (SaaS)
      1. Case study: Backupify’s Customer Lifecycle Learning
      2. Measuring engagement
      3. Churn
      4. Visualizing the SaaS business
      5. Case study: ClearFit abandons monthly subscriptions for 10x growth
      6. Wrinkles: Freemium, tiers and other pricing models
      7. Key takeaways
    6. 10. Model three: Free mobile app
      1. Installation volume
      2. Average Revenue per User
      3. Percentage of users that pay
      4. Churn
      5. Visualizing the mobile app business
      6. Wrinkles: In-app monetization vs. advertising
      7. Key takeaways
    7. 11. Model four: Media site
      1. Audience and churn
      2. Inventory
      3. Pattern: Performance and the sessions-to-clicks ratio
      4. Ad rates
      5. Content/advertising trade-off
      6. Visualizing the media business
      7. Wrinkles: hidden affiliates, background noise, ad blockers, and paywalls
      8. Key takeaways
    8. 12. Model five: User-generated content
      1. Visitor engagement
      2. Content creation & interaction
      3. Engagement funnel changes
      4. Value of created content
      5. Content sharing and virality
      6. Notification effectiveness
      7. Visualizing a UCG business
      8. Wrinkles: Passive content creation
      9. Key takeaways
    9. 13. Model six: Two-sided marketplace
      1. Case study: What Duproprio watches
      2. Rate at which you’re adding buyers and sellers
      3. Rate of inventory growth
      4. Buyer searches
      5. Conversion rates and segmentation
      6. Buyer and seller ratings
      7. Visualizing a two-sided marketplace
      8. Wrinkles: Chicken and Egg; Fraud; keeping the transaction; auctions
      9. Key takeaways
    10. 14. What stage are you at?
      1. Exercise: Pick the stage that you’re at
    11. 15. Stage one: Empathy
      1. Metrics for the Empathy Stage
      2. This is the best idea I’ve ever had! (or how to discover problems worth solving)
      3. Finding a problem to fix (or how to validate a problem)
      4. Pattern: Signs you’ve found a problem worth tackling
      5. Pattern: Running Lean and how to do a good interview
      6. Pattern: How to avoid leading the witness
      7. Convergent and divergent problem interviews
      8. How do I know if the problem is really painful enough?
      9. Case study: Cloud9 IDE interviews existing customers
      10. How are people solving the problem now?
      11. Are there enough people that care about this problem? (Understanding the market)
      12. What will it take to make them aware of the problem?
      13. A ‘Day in the Life’ of Your Customer
      14. Pattern: Finding people to talk to
      15. Getting answers at scale
      16. Case Study: LikeBright Mechanical Turks its way into TechStars
      17. Pattern: Creating an answers-at-scale campaign
      18. Build it before you build it (or how to validate the solution)
      19. Case study: Localmind hacks Twitter
      20. Before you launch the MVP
      21. Deciding what goes into the MVP
      22. Measuring the MVP
      23. Case Study: Static Pixels eliminates a step in their order process
      24. A summary of the Empathy Stage
      25. Exercise: Should you move to the next stage?
    12. 16. Stage two: Stickiness
      1. Iterating the MVP
      2. Case study: qidiq changes how it adds users
      3. Premature virality
      4. The goal is retention
      5. Pattern: 7 Questions to Ask Yourself Before Building a Feature
      6. Case study: How Rally builds new features with a Lean approach
      7. How to handle user feedback
      8. The Minimum Viable Vision
      9. The Problem-Solution Canvas
      10. Case study: VNN uses the Problem-Solution Canvas to solve business problems
      11. A summary of the Stickiness Stage
      12. Exercise #1: Should you move to the next stage?
      13. Exercise #2: Have you identified your biggest problems?
    13. 17. Stage three: Virality and word of mouth
      1. The three ways things spread
      2. Metrics for the viral phase
      3. Beyond the viral coefficient
      4. Case studyComment [BY4]: This is a new case study, I’ve sent it to Jonathan Wegener for review / approval.Timehop experiments with content sharing to achieve virality
      5. Instrumenting the viral pattern
      6. Growth hacking
      7. A summary of the Virality Stage
      8. Exercise: Should you move on to the revenue stage?
    14. 18. Stage four: Revenue
      1. Metrics for the Revenue Stage
      2. The penny machine
      3. Finding your revenue groove
      4. Customer Lifetime Value > Customer Acquisition Cost
      5. Case study: Parse.ly and the pivot to revenue
      6. Market/Product Fit
      7. The breakeven lines in the sand
      8. Revenue Stage Summary
    15. 19. Stage five: Scale
      1. The hole in the middle
      2. Metrics for the Scale Stage
      3. Is my business model right?
      4. Case study: Buffer goes from Stickiness to Scale (through Revenue)
      5. Pattern: The three-threes model
      6. Finding discipline as you scale
      7. A summary of the Scale Stage
    16. 20. Model + Stage drives the metric you track
  6. III. Lines in the sand
    1. 21. Am I good enough?
      1. Case study: WP Engine discovers the 2% Cancellation Rate
      2. Average isn’t good enough
      3. What’s good enough?
      4. Growth rate
      5. Number of engaged visitors
      6. Pricing metrics
      7. Case study: Socialight discovers the underlying metrics of pricing
      8. Cost of customer acquisition
      9. Virality
      10. Mailing list effectiveness
      11. Uptime and reliability
      12. Site engagement
      13. Web performance
      14. Exercise: Make your own lines in the sand
    2. 22. E-commerce: lines in the sand
      1. Conversion rate
      2. Shopping cart abandonment
      3. Search effectiveness
    3. 23. SaaS: lines in the sand
      1. Paid enrollment
      2. Freemium versus paid
      3. Upselling and growing revenue
      4. Churn
      5. Case study: OfficeDrop’s key metric – paid churn
    4. 24. Free mobile app: lines in the sand
      1. Mobile downloads
      2. Mobile download size
      3. Mobile customer acquisition cost
      4. Case study: Sincerely learns the challenges of mobile customer acquisition
      5. Application launch rate
      6. Percent active mobile users/players
      7. Percentage of Mobile users who pay
      8. Average revenue per daily active user
      9. Monthly average revenue per mobile user
      10. Average revenue per paying user
      11. Mobile app ratings click-through
      12. Mobile Customer Lifetime Value
    5. 25. Media site: lines in the sand
      1. Click-through rates
      2. Sessions-to-clicks ratio
      3. Referrers
      4. Engaged time
      5. Pattern: What onsite engagement can tell you about goals & behaviors
      6. Sharing with others
      7. Case study: JFL Gags cracks up YouTube
    6. 26. User-generated content: lines in the sand
      1. Content upload success
      2. Time on site per day
      3. Case study: reddit part one—from links to a community
      4. Engagement funnel changes
      5. Case study: reddit part two—there’s gold in those users
      6. Spam and bad content
    7. 27. Two-sided marketplaces: lines in the sand
      1. Transaction size
      2. Case study: What Etsy watches
      3. Top ten lists
    8. 28. What to do when you don’t have a baseline
  7. IV. Putting Lean Analytics to work
    1. 29. Selling to businesses: Enterprise markets
      1. Why are enterprise customers different?
      2. The enterprise startup lifecycle
      3. Case study: How Coradiant found a market
      4. So what metrics matter?
      5. The bottom line: Startups are startups
    2. 30. Lean from within: Intrapreneurs
      1. Span of control and the railroads
      2. Pattern: Skunk Works for intrapreneurs
      3. Changing—or innovating to resist change?
      4. Stars, dogs, cows, and question marks
      5. Case study: Swiffer gives up on chemistry
      6. Case study: Doritos chooses a flavor
      7. Working with an executive sponsor
      8. Case study: EMI embraces data to understand its customers
      9. The stages of intrapreneur analytics
    3. 31. Conclusion: Beyond startups
      1. How to instill a culture of data in your company
      2. Ask good questions
    4. A.
      1. References and further reading
  8. About the Authors
  9. Copyright
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Chapter 1. We’re all liars

Let’s face it: you’re delusional.

We’re all delusional; some more than others. Entrepreneurs are the most delusional of all.

Entrepreneurs are particularly good at lying to themselves. Lying may even be a prerequisite for succeeding as an entrepreneur—after all, you need to convince others that something is true in the absence of good, hard evidence. You need believers to take a leap of faith with you. As an entrepreneur, you need to live in a semi-delusional state just to survive the inevitable rollercoaster ride of running your startup.

Small lies are essential. They create your reality distortion field. They are a necessary part of being an entrepreneur. But if you start believing your own hype, you won’t survive. You’ll go too far into the bubble you’ve created, and you won’t come out until you hit the wall—hard—and that bubble bursts.

You need to lie to yourself, but not to the point where you’re jeopardizing your business.

That’s where data comes in.

Your delusions, no matter how convincing, will wither under the harsh light of data. Analytics is the necessary counterweight to lying, the yin to the yang of hyperbole. Moreover, data-driven learning is the cornerstone of success in startups. It’s how you learn what’s working and iterate toward the right product and market before the money runs out.

We’re not suggesting that gut instinct is a bad thing. Instincts are inspiration, and you’ll need to listen to your gut and rely on it throughout the startup journey. But don’t disembowel yourself. Guts matter; you’ve just got to test them. Instincts are experiments. Data is proof.

The Lean Startup movement

Innovation is hard work—harder than people realize. It’s true whether you’re a lone startup trying to disrupt an industry, or a rogue employee challenging the status quo, tilting at corporate windmills and steering around bureaucratic roadblocks. We get it. Entrepreneurship is crazy, bordering on absurd.

Lean Startup provides a framework by which you can more rigorously go about the business of creating something new. Lean Startup delivers a heavy dose of intellectual honesty. Follow the Lean model, and it becomes increasingly hard to lie, especially to oneself.

There’s a reason the Lean Startup movement has taken off now. We’re in the midst of a fundamental shift in how companies are built. It’s vanishingly cheap to create the first version of something. Clouds are free. Social media is free. Competitive research is free. Even billing and transactions are free[3]. We live in a digital world, and the bits don’t cost anything.

That means you can build something, measure its effect, and learn from it to build something better the next time. You can iterate quickly, deciding early on if you should double down on your idea or fold and move onto the next one. And that’s where analytics come in. Learning doesn’t happen accidentally. It’s an integral part of the Lean process.

Management guru and author Peter Drucker famously observed, “If you can’t measure it, you can’t manage it.”[4] Nowhere is this truer than in the Lean model, where successful entrepreneurs build the product, the go-to-market strategy, and the systems by which to learn what customers want—simultaneously.

Poking a hole in your reality distortion field

Most entrepreneurs have been crushed, usually more than once. If you haven’t been solidly trounced on a regular basis, you’re probably doing it wrong, and aren’t taking the risks you need to in order to succeed in a big way.

But there’s a moment on the startup rollercoaster where the whole thing really does come off the rails. It’s truly finished. There’s little more to do than turn off the website and close down the bank account. You’re overwhelmed, the challenges are too great, and it’s over. You’ve failed.

Long before the actual derailment, you knew this was going to happen. It wasn’t working. But at the time, your reality distortion field was strong enough to keep you going on faith and fumes alone. As a result, you hit the wall at a million miles an hour, lying to yourself the whole time.

We’re not arguing against the importance of the reality distortion field—but we do want to poke a few holes in it. Hopefully, as a result, you’ll see the derailment in time to avoid it. We want you to not have to rely on the reality distortion field quite as much, and instead rely on Lean Analytics.

Case study: AirBnB Photography—growth within growth

AirBnB is an incredible success story. In just a few years, the company has become a powerhouse in the travel industry, providing travelers with an alternative to hotels, and providing individuals who have rooms, apartments or homes to rent with a new source of income. In 2012, travelers booked over 5 million nights with AirBnB’s service. But it started small, and its founders—adherents to the Lean Startup mindset—took a very methodical approach to their success.

At SXSW 2012, Joe Zadeh, Product Lead at AirBnB, shared part of the company’s amazing story. He focused on one aspect of their business: professional photography.

It started with a hypothesis: “Hosts with professional photography will get more business. And hosts will sign up for professional photography as a service.” This is where the founders’ gut instincts came in: they had a sense that professional photography would help their business. But rather than implementing it outright, they built a Concierge Minimum Viable Product (MVP) to quickly test their hypothesis.

Initial tests of their MVP showed that professionally photographed listings got two to three times more bookings than the market average. This validated their first hypothesis. And it turned out that hosts were wildly enthusiastic to receive an offer from AirBnB to take those photographs for them.

In mid-to-late 2011, AirBnB had 20 photographers in the field taking pictures for hosts—roughly the same time period where we see the proverbial “hockey stick” of growth in terms of nights booked, as seen in Figure 1-1.

It’s amazing what you can do with 20 photographers and people’s apartments

Figure 1-1. It’s amazing what you can do with 20 photographers and people’s apartments

AirBnB experimented further. It watermarked photos to add authenticity. It got customer service to offer professional photography as a service when renters or potential renters called in. It increased the requirements on photo quality. Each step of the way, the company measured the results and adjusted as necessary. The key metric it tracked was shoots per month, because it had already proven with their Concierge MVP that more professional photographs meant more bookings.

By February 2012, AirBnB was doing nearly 5,000 shoots per month and continuing to accelerate the growth of the professional photography program.

Summary:

  • AirBnB’s team had a hunch that better photos would increase rentals.

  • They tested the idea with a Concierge MVP, putting the least effort possible into a test that would give them valid results.

  • When the experiment showed good results, they built the necessary components and rolled it out to all customers.

Analytics Lessons Learned: Sometimes, growth comes from an aspect of your business you don’t expect. When you think you’ve found a worthwhile idea, decide how to test it quickly, with minimal investment. Define what success looks like beforehand, and know what you’re going to do if your hunch is right.

<End of AirBnB case study>

Lean is a great way to build businesses. And analytics ensures that you’ll collect and analyze data. Both fundamentally transform how you think about starting and growing a company. Both are more than processes—they’re mindsets. Lean, analytical thinking is about asking the right questions, and focusing on the one key metric that will produce the change you’re after.

With this book, we hope to provide you with the guidance, tools, and evidence to embrace data as a core component of your startup’s success. Ultimately, we want to show you how to use data to build a better startup faster.



[3] When we say “free,” we mean, “free from significant upfront investment.” Plenty of cloud and billing services cost money—sometimes more money than doing it yourself—once your business is underway. But free, here, means free of outlay in advance of finding your product/market fit. You can use PayPal, or Google Wallet, or Eventbrite, or dozens of other payment and ticketing systems, and pass the cost of the transaction on to your consumers.

[4] In Tasks, Responsibilities, Practices (1973) Drucker said, “Without productivity objectives, a business does not have direction. Without productivity measurements, it does not have control.”

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