I’ve put together a free 30-day email course for consultants, mentors,  educators, and product managers leading early-stage product teams.

It assembles many of the ideas across this newsletter and my books into 10 coaching lean meta-skills I’ve repeatedly used to drive breakthrough transformation with teams.

More details here.

Cheers,

Ash

P.S.

I…

Read more

I’m excited to push the CI Framework further next year with you — starting with the customer interview ai-assist features I previewed last week.

You can still catch the replay here.
(I talk about CustomerForces.ai at the 47-minute mark)

But first, I’m looking forward to some much-needed “free” …

Read more

I’ll preview what we have in store for the coming year and answer any questions on coaching lean certification and the LEANSTACK platform.

Some highlights: 

• CustomerForces.ai - new ai powered problem discovery features

• Entrepreneur/Team IQ scoring

• Funnel level metrics - visualize y…

Read more

Challenging strong founder convictions as a coach requires a different approach.

Lecturing seldom works because the Innovator’s Bias is immune to lecture. Press them hard enough, and they s…

Read more

The last Level 1 certification workshop kicks off next week, Nov 7-10th. Then, I won’t run another until Feb 2024.

If you’d like to use the discount, here are the steps:

1. Select the workshop you’d like to attend here

2. Go through the…

Read more

If you’ve been considering leveling up your coaching lean skills and pursuing certification, I just published dates for workshops through April 2024.

These workshops are hands-on and teach you battle-tested techniques for leading teams throug…

Read more

On the surface, recurring revenue models seem superior to non-recurring (one-time) models. While this is mostly true, it isn’t always true.

Let’s dive in.

Subscribe now

Traction Roadmapping - Direct Recurring Business Models

The…

Read more

First, she’s an extreme outlier. Next, you can probably guess that most of her net worth didn’t come from books alone — especially when you factor in that she reportedly received royalties of approximately 15% for each boo…

Read more

Often, they don’t have a good answer.

You see, the first few customers came in from friends, others from adviser referrals, and the rest were seemingly random.

The problem with random is that random is not repeatable.

Not only is repeatability a property of systems, it is also a necessary precondition for growth.

In the next series of posts, I’m going to dive into metrics, traction, and systems thinking, specifically the Customer Factory model and how to use it in different scenarios and stages of a product.

The Customer Factory model is a simple metaphor built on the premise that all businesses share a universal goal: To make happy customers.

It works by

• taking in unaware visitors as the input on the left,

• creating, delivering, and capturing value from these visitors inside the customer factory and

• creating happy customers on the right.

The rate at which a business model creates happy customers = Traction

When working with a team that has customers, the first task is baselining their traction and hitting the Groundhog Day Effect.

Here are the steps:

1. Turn revenue numbers into a rate (MRR/ARR)

Traction is a measure of the rate of growth, so an aggregate or single revenue number without a time period isn’t all that useful. The first step is understanding how much revenue the product has made in a given time period — monthly is a good start.

Use this to baseline their current MRR (monthly recurring revenue) or ARR (annual recurring revenue).

Note: MRR/ARR isn’t restricted to subscription-based businesses. All businesses need to establish repeatability and generate recurring monthly and yearly revenue.

2. Turn MRR/ARR into a customer throughput rate

The next step is understanding where this revenue comes from, i.e., which customer segment. Finding multiple customer segments at multiple price points is pretty typical at this step. This, too, isn’t repeatable.

While it’s perfectly normal (and even a good idea) for a startup to test multiple customer segments at the outset, they must pick a single primary early adopter segment (beachhead market) to focus on reasonably quickly. This is because it’s hard to optimize multiple business models in parallel.

The ideal is pursuing a 1 product - 1 customer segment - 1 business model rule until product/market fit.

The deliverable from this step is understanding the early adopter archetype, which informs GTM (go-to-market) strategy:

3. Build a Customer Factory dashboard

The final step is creating a weekly Customer Factory dashboard. My preferred way of doing this is creating a slide deck with 52 slides (one for each week) and manually inputting the numbers every Sunday (or Monday):

When the team starts measuring their metrics this way, something uncanny happens. They encounter the Groundhog Day effect.

What is the Groundhog Day Effect?

Once the team starts measuring their Customer Factory as batches (cohorts), system repeatability quickly kicks in, and the metrics start flatlining.

I named the Groundhog Day Effect after the movie with the same name. In the movie, the protagonist, played by Bill Murray, is stuck in a loop where his life repeats daily until he has a breakthrough insight that breaks the loop. If you haven’t watched the original, there are numerous plot remakes that I’m sure you can relate to.

On the one hand, this flatlining of metrics can be depressing. Because once steady traffic sets in, despite best efforts, this graph flatlines. No one wants to walk into a meeting with a flatline graph.

On the other hand, this can be highly empowering. Once you have a stable benchmark of a product, it permits you to aggressively experiment with bold new ideas to effect a spike in the flatline. If you create a spike, the next course of action is to run more of whatever you did to make the line stick at the higher level.

Your goal is to do everything possible to repeatedly and systematically drive all of these lines up and to the right guilt-free of vanity.

Resources

Download the Customer Factory Dashboard template

Read more

• a private community (Coaching Lean Academy) for mentors, consultants, and educators using the Continuous Innovation Framework,

• invitation to clinics, discussions, and forums where you can ask questions,

• …

Read more

Wrap-up Talking Points

One Business Model Variant Per Canvas

The first session's goal is to get the team to outline their idea as a business model.

Read more

If you enjoy Coaching Lean, it would mean the world to me if you invited friends to subscribe and read with us. If you refer friends, you will receive benefits that give you special access to Coaching Lean.

How to participate

1. Share Coaching Lean. When you use the refe…

Read more

To me, feasibility comes down to the 3Ts: Team, Traction, and Timeline.

While I got a general snapshot of traction during the backstory, it’s still…

Read more

There are two key inputs for stress-testing viability:

• a goal, and

• the pricing model.

I usually find that neither is …

Read more

Because entrepreneurs are naturally predisposed to fall in love with their solutions, and they manage to convince themselves that if they build an “awesome” enough solution, people will come — the Field of Dreams.

They frame their differentiation in terms of the novelty of their solution, but if this difference doesn’t matter to customers, the product fails to grab attention.

This kind of solution-centric framing happens to most entrepreneurs and happens unconsciously, which is why I coined it The Innovator’s Bias.

When they have decided to build a hammer, everything starts looking like a nail.

The best way to help your teams sidestep this bias is to help them craft a Why Now Elevator Pitch that builds upon the Innovator’s Gift mental model.

Read more

This is the key step in getting a team to focus on “right action, right time” — the few key actions that help drive the biggest impact in their business model.

But while starting with what’s riskiest is a simple enough concept, too many advisers/coaches rush this step with costly ramifications.

Incorrect prioritization of risk is a recipe for wasting time, money, and effort on suboptimal actions. This depletes the already limited runway for finding product/market fit.

You can’t simply guess or “map and rank” a bunch of risks on a 2x2 matrix to surface the riskiest assumptions because:

• Guessing at risk is highly subjective and prone to individual bias.

• Voting on risks is prone to specialization bias — what’s risky to a developer differs from what’s risky to a marketer.

• Even seasoned advisers often disagree on what’s riskiest.

Who do you listen to? The most vocal or most experienced aren’t guaranteed to be correct because startups are in the business of creating something new and a bit crazy.

Is there a better way?

The answer is yes... And it requires using a systems-based approach that doesn’t require any guessing — specifically applying the Theory of Constraints (TOC) to business models.

Read more

2. Structure

With the backstory of the idea out of the way, I then focus my attention on the overall business model structure. T…

Read more

Read more

1. Start by baselining the team and their business model

2. Use a traction-driven approach to identify what’s riskiest in the business model

3. Trigger the team to change course (if needed) 

Over the next several posts, I’ll dive into these steps in more detail. Before doing that, it would help to share…

Read more

As I boarded the 30-seater plane in subzero temperatures headed for Sydney, Nova Scotia, I developed butterflies in my stomach. Not because I was afraid of flying but because I was scheduled to deliver a full-day workshop on Running Lean to a room full of biotech researchers the next day.

What could I, a software entrepreneur, have to teach these researchers? Software and deep science are worlds apart.

The reason I found myself on that plane was that I had just published my first book of the same title, and when the opportunity to branch out to a wider audience presented itself, I had intellectually jumped on it. Now I wasn’t so sure.

The next day, I decided to focus on just surviving until the first break, then checking in with the organizers to re-assess if/how to carry forward.

I delivered my usual 3-step overview of the Running Lean process:

1. Document your Plan A using a Lean Canvas

2. Identify what’s riskiest, not what’s easiest

3. Systematically test your plan using small and fast experiments

I followed with a few rapid-fire case studies and then called a break.

During the break, several researchers approached me to share how excited they were to see such a simple (and scientific) approach to starting a business — one that matched their worldview perfectly.

We continued with the workshop. The researchers captured their big ideas on a Lean Canvas, and I found myself still able to advise them on risks, experiments, and validation strategy — despite knowing how their solution worked.

Emboldened by this experience, I sought out many more uncomfortable situations where I put myself in domains outside my immediate expertise. I was similarly able to steer and deliver numerous transformative aha moments to attendees by taking a principles-driven versus tactics-driven approach.

This led to my first epiphany:

While tactics show you how, principles tell you why.
Tactics are specific, principles are universal.

A couple of months later, I was delivering another full-day workshop at a well-known startup accelerator. The organizers had inserted a speed-mentoring session during lunch where teams sought advice from a dozen seasoned entrepreneurs and domain experts.

When we resumed the workshop, I asked the teams about the advice they had received. While they were all appreciative, most were paralyzed by what to do next. They all had filled up several pages of very tactical how-to advice from their sessions. Their problem now was prioritizing which advice to follow — especially since much of the advice they had received directly contradicted each other. Who should they follow? Who had the biggest exit or the entrepreneur that made the most sense?

When pressed to choose, I found that the founders often cherry-picked the advice that was easiest to implement and aligned with their way of thinking. The problem with that is what’s riskiest usually isn’t what’s easiest. Choosing to tackle what’s easiest (versus riskiest) wastes needless time, money, and effort on the wrong actions at the wrong time.

I witnessed this same adviser paradox play out at several other accelerators after that. Of course, none of these advisers were intentionally trying to confuse or mislead these founders. They were all well-intentioned experts taking time out of their busy schedules to help these startups.

Yet, they were causing more harm than good.

This led to my next epiphany:

Early-stage uncertainty + Conflicting advice = Aimless wandering

Following the rollout of my book, I started delivering more regular workshops, which put me in positions where I got to see and advise many more teams. It didn’t take long for a common pattern to emerge.

Immediately after teams would present their ideas captured on a Lean Canvas to me, they would ask me what I thought of their idea. While I understood why they were asking, I found that question amusing. If I could tell good ideas from bad ideas just by reviewing a Lean Canvas, I wouldn’t be advising them but running my crystal ball VC firm.

I told them that it wasn’t my job to tell them if their idea was good or bad because nobody can see through the fog of uncertainty that clouds the early stages of an idea. What I could show them instead were fast(er) ways to test whether their idea was good or bad by helping them focus on the right risks and ignoring the rest.

That’s also when I realized that advising and coaching are two very different things. I had arrived at another epiphany:

At the earliest stages of a product, entrepreneurs need coaches not advisers.

I define an advisor as anyone with an impressive enough track record, like a seasoned entrepreneur or a domain expert. A coach, on the other hand, is someone trained to steer a team through the entrepreneurial journey and hold them externally accountable. They do this by constantly optimizing for right action, right time.

To be fair, there is a place for advisers at the early stage — to help startups tactically implement the right action once they’ve been identified, but not before. An analogy here might help.

Think of coaches as primary care physicians (doctors) and advisers as specialists. When you have bad headaches, you don’t schedule an appointment with a neurosurgeon. You start with your primary care physician. They diagnose you and, if warranted, refer you to a specialist.

This same approach works well during the early stages of a product. Instead of pairing teams with dozens of advisers, I find it much more effective to assign teams to a dedicated coach whose job it is to help the team first systematically identify right action, right time, and, if warranted, refer them to an adviser.

This not only drives more focus and accountability but also leads to more repeatable and scalable outcomes.

As I began to coach even more teams, I started developing patterns, diagnostics, and tools to help increase my efficiency and reach. I began to wonder if these coaching meta-skills could be taught to others and started running Coaching Lean workshops.

In the next section, I’ll outline seven key meta-skills we teach.

Meta-skill #1: Coaches focus on asking the right questions versus being right.

The true job of a startup is finding a working business model before running out of resources. The key to doing this effectively is correctly diagnosing the riskiest assumptions in a business model.

Too many people (and advisers) simply guess. Sure, our judgment improves with experience, but educated guessing is still guessing, and there’s too much at stake when we simply guess.

Incorrect prioritization of risk is one of the top killers of startups.

The best way to uncover what’s riskiest is to discover it. You do this by asking the right questions to uncover the root causes of problems holding the business model back versus prematurely doling out prescriptive solutions or tactics.

Problems, not solutions, create spaces for innovation.

Meta-skill #2: Coaches use standard tools to baseline teams.

Sticking with the general practitioner analogy, imagine seeing ten doctors about your headaches and getting ten completely different prescriptions. Which would you follow?

The good news is that, barring complicated cases, most doctors generally arrive at the same diagnosis after running a few standard tests. Coaching should be held to the same bar.

While every startup is different in its way, when viewed at a meta-level, they all share the same goal: To build a repeatable and scalable business model. All business models have customers, all customers have problems, etc.

It is, therefore, possible to devise a set of standard diagnostics to baseline teams and systematically identify what’s riskiest in the business model. As I outlined above, this is the single most important step in keeping the startup focused on right action, right time.

In the Continuous Innovation Framework, we use three standard tools to baseline a startup:

1. A Lean Canvas to describe the overall business model story,

2. A Customer Factory to measure key metrics, and

3. A Customer Forces model for understanding customer behavior.

I’ll cover these models and diagnostics in future posts.

Meta-skill #3: Coaches study validation patterns.

In addition to standard diagnostics, a coach uses a pattern language to navigate extreme uncertainty.

When a grandmaster looks at a chess board, they don’t see individual chess pieces but patterns. Similarly, when an experienced coach looks at a Lean Canvas, they don’t see individual assumptions but business model patterns.

Patterns help codify what’s worked before.

Patterns are heavily used in chess, architecture, design, science, and software. The same is true with entrepreneurship.

Meta-skill #4: Coaches use the Socratic Method to discover how teams think.

The biggest challenge with getting teams to follow through on advice often requires getting them to overcome their own biases. This requires one or more mindset changes.

The problem here is that mindset change is immune to lecturing. If you simply ask teams to do something they don’t want to do, like talking to customers or selling before building, they ignore you.

You can lead a horse to water, but you can’t make it drink.

The first step to causing a mindset change is first understanding the team’s or founder’s current mindset. Next, you have to trigger them to want to change by breaking their old way and making a case for a better new way. This requires further discovery and using different tactics based on what you find.

Meta-skill #5: Coaches design for behavior change.

Depending on the team’s coachability and receptivity to change, it is sometimes possible to trigger them to adopt a new way simply through conversation. More often, though, you need to design for behavior change using something I call the Judo strategy.

In Judo, when faced with a larger opponent charging at you, you don’t stand your ground and try to block them but instead, aim to get them off balance.

Similarly, when faced with a ZEBRA team that fails to see why they need to change their validation strategy, it is often more effective not to slow them down but to instead make them go faster.

ZEBRA: Zero evidence but really arrogant.

The key to making this work is designing an experiment that aligns with something the team wants, like fundraising, versus something you think they need, like interviewing customers. If/when the experiment fails, it creates a trigger for mindset change.

I’ll share examples of specific scenarios and experiments in a future post.

Meta-skill #6: Coaches are evidence-driven.

While tempting to rely on your experience and instincts to prescribe experiments, a coach must stay humble and recognize that what may have worked last year may not work today.

Rather than prematurely jumping to prescriptive experiments (tactics), it is best to adopt a lean DIET approach.

lean DIET: Discovery - Insights - Experiments - Traction

In other words, problem discovery is often the best first course of action to identify root causes and true risks, not rushing to test hypotheses. Remember that hypothesis is just a fancy word for guessing.

Meta-skill #7: Coaches invest in team learning.

Even in cases when the problem and solution are obvious to you, a coach should strive to help the teams derive the solution on their own, either through analysis or experiment. This helps instill critical thinking and problem-solving skills — which are key to developing a learning mindset.

Your job is to teach your teams how to fish, not fish for them. Sure, it will sometimes be necessary to demonstrate how to perform a certain skill, but more often, it will be far more effective to have your teams perform the skill to the best of their ability. Then coach them on how to do it better.