The #1 book on good interviewing "The Mom Test", sells 1,000 copies every week.

This shows how HARD it is to gather robust data in interviews.

The Customer Forces Canvas by @ashmaurya is a lean structure to follow the tips from the The Mom Test [without reading it]. When you ask customers a direct question they will lie to your face.

Not intentionally, but due to how our brain works.

The easy fix is to capture concrete stories.

Concrete stories let your customers tell you about their actual behavior.

But how to ask for the right story?

There is a myriad of great books for product managers. But after reading we forget about 90% of its content.

Because we want to speed up our way to success and avoid the mistakes the author already made.

I've been using @readwise for 330 days in a row so I don't forget 👇 Readwise quizzes you every day on the highlights you've made in books (e.g. in Kindle).

So I don't forget the content after 3 days.

Plus: It sometimes reminds me of helpful advice at the exact right moment to apply it.

Managing by output, not outcomes, is unfair!

This is why - and how product people can explain it to their stakeholders 👇 Many product teams work in a so-called "feature factory".

They are tasked with implementing features.

Delivering the features (output) is in the team's control, which gives us a sense of planability.

But this sense is false.

And it's unfair!

Business outcomes are too vague to steer the action of your team.

This makes good product outcomes the one key thing to empower your team to build great products.

But finding a leading product outcome is a difficult process. 👇 Quick recap: Lagging vs. leading indicators

Lagging indicators show your past performance.
E.g.: Revenue. It is a consequence of you developing a good product.

Leading indicators are measures that suggest that you are progressing towards your lagging indicator.

The velocity of a scrum team is the most abused metric!

Conservative management loves to use it to manage dev teams.

But they are lying to themselves.

When velocity increases but business is declining, they are NOT HAPPY.

Let's dig a little deeper into how absurd it is. 👇 Meet Emmy.

She's responsible for developing a video streaming service. Think Netflix or Amazon Prime.

She wants to entertain people with video.

So she starts building:
• A player
• A search
• A catalog

A very simple product.

This is called *uninformed simplicity*

In 2010 the Ex-VP Product of Netflix @gibsonbiddle joined the start-up Chegg, where the leadership fought all the time.

Turns out they were lacking a good strategy.

This is the exercise he gave them tp foster a discussion about where to focus 👇 The exercise seems easy.

Prioritize these three factors:

• Monetization: Lifetime Value (LTV) and gross margin [except retention].
• Engagement: Retention as a proxy for quality
• Growth: Year-over-year growth rate

So how did it go?

Roadmaps are waterfall, not agile!

So why bother creating them? Or trying to fix them with approaches like theme-based roadmaps?

I studied @ttorres, @jackiebo and @simplybastow to find out why they are still extremely important 👇 1️⃣ A roadmap is a prototype for our strategy

Janna Bastow coined this quote and I love it.

Like a prototype, the roadmap doesn't represent exactly what we will execute.

But it reveals underlying assumptions and inconsistencies in our thinking.

So others can give us feedback.

Every customer you speak with has the problem you want to solve. ✅

But they aren't aware of it 😬 When your customers aren't aware of the problem, you have 3 options:

• Educate the customers about this problem
• Choose a problem the customer segment is aware of
• Choose a customer segment, which is aware of the problem

But educating them is hard!

Conducting interviews is the most important thing you can do to build empathy and discover problems worth solving.

Here's the thing: If you don't have a product yet, you don't have a customer base to draw from.

Here are 4 approaches we tried to recruit them anyway. 1️⃣ Early Customers are a Who-Where pair

This is from the #1 bestseller about interviewing "The Mom Test".

Rob argues, that in the early stages you should know where you can find the customers (to interview them).

So slice it until you have a preferably physical location.

Over the last year, we’ve conducted 50 interviews with product owners and product managers.

We wanted to help them gather their own data, so they can prioritize what to build next in a data-informed fashion.

But they were lacking a requirement for that decision. 👇 "If you don't know where are you going, any road will get you there" ~ Lewis Carroll

To decide what to build next, we have to know, what we want to achieve.

This can take place on different levels.

How can we measure the customer lifetime of our customers?

The easy approach: Wait X months and measure.

The problem: It is such a lagging indicator.

The good thing is you can calculate it. Waiting forever to measure the customer's lifetime is a pain. But there are 3 cases when it’s simply not possible:

1. For a new product, which just launched
2. For a product, which has just improved much
3. For a customer segment, which needs have changed

So it’s not possible.

Thinking about the risks can shape your products.

When product people want to develop a new product, one of the first steps is a risk analysis.

Then we want to learn about the most critical risks to increase our chances of winning.

This directly shapes the V1 of the product. My favorite way of risk analysis for a new product is starting with a customer journey.

The customer journey aligns your team in terms of the idea and highlights inconsistencies in your thinking. Did you miss any steps?

For each step, you can assess very narrow the risks.

The more specific an assumption/hypothesis the easier and faster it is to test, when validating a product idea,

"People want to watch videos remotely together" is not specific enough to test easily.

But it consists of multiple sub-hypothesis, you can test. The 4 biggest hypotheses for your product are:

• People want it (Desirability).
• People understand how to use it (Usability)
• You'll be able to develop it (Feasibility).
• It pays off (Viability).

This is where all validation starts, but how can we break them down?

Objectives in OKR should not be measurable!

Until this week I thought that this was rather optional.

Like it doesn't have to be measurable, because the key results are.

In a team discussion it turned out, there is harm 👇 One of my teammates prepared a little presentation to kick off our planning.

He said, that objectives must:
• describe a desirable state in the future
• qualitative (not measurable)
• Clear focus on value for customer and business

Until then I've used @johndoerr s definition

I've been helping ~ 15 teams create their assumptions about their business idea and prioritize them.

They all struggled with coming up with a lean experiment to test it.

With this simple step, the assumptions got more specific and therefore way easier to test 👇 I've started with the approach by @davidjbland from his book Testing Business Ideas.

I can highly recommend it, because it teaches you what different kind of experiments are out there.

But the approach for coming up with assumptions was a little bit clumsy for me.

In 2021 we searched for product-market fit for our consulting business.

We started with a lean approach: A Fake-Door-MVP.

But due to lacking experience in sales and marketing, we failed to set a good research focus.

Learn what we missed to get more focus in early discovery 👇 The purpose of discovery is to identify viable opportunities.

In general, there are infinite possible opportunities out there. This is why discovery work needs constraints. One constraint is a product outcome.

Our guiding product outcome was "German SMB build the right things"

If you want to have success, you must set outcomes as your goals.

That's what I was thinking over 2 years ago when I crafted my first set of OKRs.

But I failed miserably because my expectations were too high. One of our lofty objectives was the outcome of closing new deals for our consulting unit.

We didn't have any marketing or sales expertise.

We weren't even sure who our target customer was.

But outcomes are the way to go, right?

Doing sales calls doesn't matter if we close 0.

Our goal: Discovery interviews with German businesses for a new consulting service: Product Discovery Coaching.

Meta, right?

But what is the right approach to do that?

The customer segment of "German businesses" is quite heterogeneous.

Who should we interview first? We needed a theory about which problem we are solving with our discovery coaching:

• Faster business success
• Better products [for the customers]
• ...

We have been poking in the dark.

We were missing a lot for sure, but who can we ask?

Our first Opportunity Solution Tree was a mess!

We started with a solution in mind, which led to interviews with - in hindsight - different customer segments.

By not ensuring a concise logical structure, we made our OST too complicated to read or update. 👇 The solution we had in mind was coaching for product discovery.

As a software service provider, we saw in our projects that many companies create a lot of waste in software dev.

Our target group: German SMBs
Value Proposition: Increase the impact of the dev teams by discovery.

In Continuous Product Discovery you are supposed to map the opportunities you uncover on the Opportunity Solution Tree.

Opportunities are unmet needs, problems, and desires.

IMHO desires make the OST unnecessarily complex and should be avoided. 👇 The Opportunity Solution Tree is a hierarchical structure with the business outcome you want to achieve at the top.

Right under it comes the product outcome. It's your theory of how you can drive the business outcome.

Below is the opportunity space, where the music goes off.

When we started Continuous Discovery Habits for our consulting business, and we struggled so hard to create the Opportunity Solution Tree.

We weren't able to bring the opportunities (unmet needs, pains, and desires) into a hierarchical structure.

The reason? Bad interviews 👇 We tried to conduct story-based interviews.

Asking direct questions like "How often do you go to the gym?" are prone to cognitive biases. The solution is to ask questions about concrete facts in the past.

One way to do this is to let the interviewee tell you whole stories.