Running Lean: Iterate from Plan A to a Plan That Works (Lean (O'Reilly))

by Ash Maurya

If I had to summarize these changes in one phrase, it would be this one: “the rentership of the means of production”

Today, anyone with a credit card can rent all of these capabilities and more. What is significant about this development is that it enables more startup experiments than ever before. And make no mistake, a startup is an experiment. Today’s companies can build anything they can imagine. So the question we are called on to answer is no longer primarily, “can it be built?”, but rather, “should it be built?”

Running Lean is a systematic process for iterating from Plan A to a plan that works, before running out of resources.

The key takeaway from Customer Development can best be summed up as: Get out of the building. — Steve Blank Most of the answers lie outside the building — not on your computer, or in the lab. You have to get out and directly engage customers.

Principles guide what you do. Tactics show you how.

It is important to accept that your initial vision is built largely on untested assumptions (or hypotheses). Running Lean helps you systematically test and refine that initial vision.

Figure 1-1. Lean Canvas

Customers don’t care about your solution. They care about their problems.

Your job isn’t just building the best solution, but owning the entire business model and making all the pieces fit.

A startup goes through three distinct stages, as shown in Figure 1-3. Figure 1-3. Three stages of a startup

While ideas are cheap, acting on them is quite expensive.

Before product/market fit, the focus of the startup centers on learning and pivots. After product/market fit, the focus shifts toward growth and optimizations. (See Figure 1-4.) Figure 1-4. Before and after product/market fit

The best way to differentiate pivots from optimizations is that pivots are about finding a plan that works, while optimizations are about accelerating that plan.

but some times are certainly better than others to consider external funding (see Figure 1-5). Figure 1-5. Ideal time to raise funding

While an experiment helps you validate or invalidate a specific business model hypothesis, an iteration strings multiple experiments together toward achieving a specific goal, such as getting to product/market fit.

Figure 1-7 shows the basic iteration meta-pattern we’ll use throughout this book. Figure 1-7. Iteration meta-pattern

A book, like large software, is never finished — only released.

Bind a solution to your problem as late as possible.

As a scalable channel, direct sales only make sense in businesses where the aggregate lifetime value of the customers exceeds the total compensation of your direct sales people, such as in certain B2B and enterprise products.

While a salesperson can probably outsell you on the execution of a sales plan, she can’t create that plan. You have to first sell your product yourself, before letting others do it.

I believe that if you intend to charge for your product, you should charge from day one.

Getting paid is the first form of validation. Getting a customer to give you money is one of the hardest actions you can ask them to take and is an early form of product validation. Although there is a lot of science around pricing, pricing is more art than science.

Every business has a few key numbers that can be used to measure how well it is performing. These numbers are key for both measuring progress and identifying hot spots in your customer lifecycle. A model I use heavily is Dave McClure’s Pirate Metrics,[9] shown in Figure 3-7. Figure 3-7. CloudFire: Pirate Metrics

Acquisition describes the point when you turn an unaware visitor into an interested prospect.

Activation describes the point when the interested customer has his first gratifying user experience.

We know that startups are highly uncertain, but uncertainty and risk aren’t the same thing. We can be uncertain about a lot of things that aren’t risky.

Uncertainty: The lack of complete certainty, that is, the existence of more than one possibility. Risk: A state of uncertainty where some of the possibilities involve a loss, catastrophe, or other undesirable outcome.

My tool of choice is an incremental build of the Lean Canvas delivered on an iPad (or paper). I start with a blank canvas and incrementally reveal parts of the business model as I walk through it.

Devote 20% of your time to setup, 80% to conversation.

Be wary of the “advisor paradox.”

Recruit visionary advisors. Much like early adopters want to help when you nail their problems, visionary advisors will want to help when you present them with interesting problems that trigger their strengths and passion. You’ll know if there’s a fit based on their answers and body language. If so, consider bringing them on as formal advisors.

The one thing you should never outsource is learning about customers.

Validate Qualitatively, Verify Quantitatively

If you have a lot of uncertainty now, you don’t need much data to reduce uncertainty significantly. When you have a lot of certainty already, then you need a lot of data to reduce uncertainty significantly. — Douglas Hubbard

Your initial goal is to get a strong signal (positive or negative) that typically doesn’t require a large sample size. You might be able to do this with as few as five customer interviews.[12] A strong negative signal indicates that your bold hypothesis most likely won’t work and lets you quickly refine or abandon it. However, a strong positive signal doesn’t necessarily mean your hypothesis will scale up to statistical significance; nevertheless, it gives you permission to move forward on the hypothesis until it can be verified later through quantitative data. Validating hypotheses in this way — first qualitatively, then quantitatively — is a key principle we’ll see applied in various stages throughout this book.

A business should be run like an aquarium, where everybody can see what’s going on. — Jack Stack, The Great Game of Business (Currency/Doubleday)

The first significant milestone of a startup is achieving product/market fit, which isn’t just about building the “right” product but building a scalable business model that works.

The best initial learning comes from “open-ended” questions.

Are Surveys Good for Anything? While surveys are bad at supporting initial learning, they can be quite effective at verifying what you learn from customer interviews. I discussed the principle of two-phase validations earlier — first qualitative, then quantitative. The customer interview is a form of qualitative validation that is quite effective in uncovering strong signals for or against hypotheses using a “reasonably” small sample size. Once you have preliminary validation on your hypotheses, you can then use what you have learned to craft a survey and verify your findings quantitatively. The goal is no longer learning, but demonstrating scalability (or statistical significance) of the results.

Before you can pitch the “right” solution, you have to understand the “right” customer problem.

pet peeves

Content precedes design. Design in the absence of content is not design, it’s decoration. — Jeffrey Zeldman, A List Apart (Happy Cog Studios)

your pricing not only is part of your product, but it also defines the customer segment you attract.

Oren Klaff discusses this framing technique in his book, Pitch Anything (McGraw-Hill). He describes how, in most pitches, the presenter plays the role of a jester entertaining in a royal courtyard (of customers). Rather than trying to impress, position yourself to be the prize.

AIDA is a marketing acronym for Attention, Interest, Desire, and Action, and a useful framework for structuring Solution interviews.

Finding the right price is more art than science. Usually the right price is one the customer accepts, but with a little resistance.

Figure 9-1. Traditional product development cycle

Your MVP should be like a great reduction sauce — concentrated, intense, and flavorful.

The job of your unique value proposition (UVP) is to make a compelling promise. The job of the MVP is to deliver on that promise.