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Lean Analytics by Benjamin Yoskovitz, Alistair Croll

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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 most people realize. This is 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 yourself.

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 on to the next one. And that’s where analytics comes 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 succeed in a big way.

But there’s a moment on the startup rollercoaster where the whole thing comes right off the rails. It’s truly failed. 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 rely less on your reality distortion field, and rely more on Lean Analytics.

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 its 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.

What Is a Concierge MVP?

The Minimum Viable Product is the smallest thing you can build that will create the value you’ve promised to your market. But nowhere in that definition does it say how much of that offering has to be real. If you’re considering building a ride-sharing service, for example, you can try to connect drivers and passengers the old-fashioned way: by hand.

This is a concierge approach. It recognizes that sometimes, building a product—even a minimal one—isn’t worth the investment. The risk you’re investigating is, “Will people accept rides from others?” It’s emphatically not, “Can I build software to match drivers and passengers?” A Concierge MVP won’t scale, but it’s fast and easy in the short term.

Now that it’s cheap, even free, to launch a startup, the really scarce resource is attention. A concierge approach in which you run things behind the scenes for the first few customers lets you check whether the need is real; it also helps you understand which things people really use and refine your process before writing a line of code or hiring a single employee.

Initial tests of Airbnb’s MVP showed that professionally photographed listings got two to three times more bookings than the market average. This validated the founders’ first hypothesis. And it turned out that hosts were wildly enthusiastic about receiving 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, shown 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 Airbnb tracked was shoots per month, because it had already proven with its 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.

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 you’d spend doing it yourself—once your business is under way. But free, here, means free from 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 on the cost of the transaction to your consumers.

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

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