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April 3 - May 28, 2022
Don’t sell what you can make; make what you can sell.
Web analysts and data scientists may also find this book useful, because it shows how to move beyond traditional “funnel visualizations” and connect their work to more meaningful business discussions.
A startup is an organization formed to search for a scalable and repeatable business model.
One of Lean Startup’s core concepts is build→measure→learn — the process by which you do everything, from establishing a vision to building product features to developing channels and marketing strategies, as shown in Figure P-1
Building the minimum product necessary is part of what Eric calls innovation accounting,
Instincts are experiments. Data is proof.
Management guru and author Peter Drucker famously observed, “If you can’t measure it, you can’t manage it.”[
“Without productivity objectives, a business does not have direction. Without productivity measurements, it does not have control.”
In a startup, the purpose of analytics is to find your way to the right product and market before the money runs out.
A good metric is comparative. Being able to compare a metric to other time periods, groups of users, or competitors helps you understand which way things are moving. “Increased conversion from last week” is more meaningful than “2% conversion.”
A good metric is understandable.
Salespeople are coin-operated, so they did what they always do: they followed the money.
One other thing you’ll notice about metrics is that they often come in pairs. Conversion rate (the percentage of people who buy something) is tied to time-to-purchase (how long it takes someone to buy something). Together, they tell you a lot about your cash flow.
Leading metrics are better because you still have time to act on them — the horse hasn’t left the barn yet.
Quantitative data abhors emotion; qualitative data marinates in it.
If you have a piece of data on which you cannot act, it’s a vanity metric.
The real metric of interest — the actionable one — is “percent of users who are active.” This is a critical metric because it tells us about the level of engagement your users have with your product. When you change something about the product, this metric should change, and if you change it in a good way, it should go up. That means you can experiment, learn, and iterate with it.
The hidden genious of Donald Rumsfeld
The “unknown unknowns” are most relevant to startups: exploring to discover something new that will help you disrupt a market.
In the early stages of your startup, the unknown unknowns matter most, because they can become your secret weapons.
Mike discovered an “unknown unknown” that led to a big, scary, gutsy bet (drop the generalized Circle of Friends to focus on a specific niche) that was a gamble — but one that was based on data. There’s a “critical mass” of engagement necessary for any community to take off. Mild success may not give you escape velocity. As a result, it’s better to have fervent engagement with a smaller, more easily addressable target market. Virality requires focus.
In the early days of your startup, you won’t have enough data to know how a current metric relates to one down the road, so measure lagging metrics at first. Lagging metrics are still useful and can provide a solid baseline of performance. For leading indicators to work, you need to be able to do cohort analysis and compare groups of customers over periods of time.
In an enterprise software company, quarterly new product bookings are a lagging metric of sales success. By contrast, new qualified leads are a leading indicator, because they let you predict sales success ahead of time.
correlation is good. Causality is great. Sometimes, you may have to settle for the former — but you should always be trying to discover the latter.
When picking a goal early on, you’re drawing a line in the sand — not carving it in stone. You’re chasing a moving target, because you really don’t know how to define success.
Strategic consultant, blogger, and designer Bud Caddell has three clear criteria for deciding what to spend your time on: something that you’re good at, that you want to do, and that you can make money doing.
Do you have a network of friends and contacts who can give you an unfair advantage that improves your odds? Do you have the talent to do the things that matter really well? Never start a company on a level playing field — that’s where everyone else is standing.
Humans do inspiration; machines do validation.
Math is good at optimizing a known system; humans are good at finding a new one.
Ultimately, quantitative data is great for testing hypotheses, but it’s lousy for generating new ones unless combined with human introspection.
We sometimes remind early-stage founders that, in many ways, they aren’t building a product. They’re building a tool to learn what product to build.
McClure categorizes the metrics a startup needs to watch into acquisition, activation, retention, revenue, and referral — AARRR.[
Value comes not only from a transaction (revenue) but also from their role as marketers (referral) and content creators (retention).
viral coefficient — the number of new users that each user brings on.
The third engine of growth is payment. It’s usually premature to turn this engine on before you know that your product is sticky and viral.
But getting paid, on its own, isn’t an engine of growth. It’s just a way to put money in the bank. Revenue helps growth only when you funnel some of the money generated from revenue back into acquisition. Then you have a machine that you can tune to grow the business over time.
The question this poses a of course, is how do you know if you’ve achieved product/market fit? Sean devised a simple survey that you can send customers (available at survey.io) to determine if you’re ready for accelerated growth. The most important question in the survey is “How would you feel if you could no longer use this product or service?”
The five stages we identified are Empathy, Stickiness, Virality, Revenue, and Scale.
Eric Ries talks about three engines that drive company growth: the sticky engine, the viral engine, and the paid engine. But he cautions that while all successful companies will ultimately use all three engines, it’s better to focus on one engine at a time. For example, you might make your product sticky for its core users, then use that to grow virally, and then use the user base to grow revenue. That’s focus. In the world of analytics and data, this means picking a single metric that’s incredibly important for the step you’re currently working through in your startup. We call this the One
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But don’t let your ability to track so many things distract you. Capture everything, but focus on what’s important.
Whatever your current OMTM, expect it to change. And expect that change to reveal the next piece of data you need to build a better business faster.
You need to step back, ignore all the details, and just think about the really big components. When you reduce things to their basic building blocks in this way, you come up with only a few fundamental business models on the Web.
] And second, that growth is achieved by one of Eric Ries’s fundamental Engines of Growth: an increase in stickiness, virality, or revenue.
Sergio Zyman, Coca-Cola’s CMO, said marketing is about selling more stuff to more people more often for more money more efficiently.[
As a startup, you have a wide range of payment and incentive models from which to choose: freemium, free trial, pay up-front, discount, ad-funded, and so on.
The team at Startup Compass, a startup dedicated to helping companies make better business decisions with data, identifies 12 revenue models: advertising, consulting, data, lead generation, licensing fee, listing fee, ownership/hardware, rental, sponsorship, subscription, transaction fee, and virtual goods. Venture capitalist Fred Wilson has a document listing a vast number of web and mobile revenue models, many of which are variants on six basic ones we’ll list later in the book.[
This means on-site funnels are somewhat outdated; keywords are more important.
