Hi there -
Here is this week’s “1 principle, 2 strategies, and 3 actionable tactics” for running lean…
1 Universal Principle
“Chase good explanations.”
In a previous issue, I revealed the key activity scientists and entrepreneurs rely on for breakthrough insights.
No, it wasn’t running experiments but chasing good explanations.
That issue focused on chasing good explanations before running an experiment to avoid wasting resources on bad ideas.
Today’s issue will focus on how to pursue good explanations after running an experiment to avoid wasting resources on bad or flawed insights that can blind you.
2 Underlying Strategies at Play
I. Explaining success leads to repeatability.
It’s easy to misattribute the success of an experiment to the wrong thing, only to be disappointed when you can’t repeat the success.
If you’re lucky, you catch it quickly. Unfortunately, I’ve seen teams spend months implementing a new strategy based on a flawed insight that they painfully discover even later.
II. Explaining failure leads to breakthrough.
This is harder because our natural tendency is to sugarcoat or avoid failure. But I’d argue that it’s the more important skill of the two because startups tread in more failure than success.
Doing anything new is met with unexpected outcomes. Explaining these unexpected outcomes leads to root causes, which leads to breakthroughs.
In the next section, let’s explore some practical steps for implementing these concepts.
3 Actionable Tactics
I. Build a company-wide metrics dashboard.
The success or failure of an experiment is relative to your existing business model’s performance. So, before running a single experiment, you should invest some time baseling your existing metrics.
Baselining metrics is a classic Goldilocks problem:
- Measure too little, and you’re flying blind,
- Measure too much, and you’re drowning in numbers.
My solution is to rely on a two-level traction-oriented metrics dashboard that summarizes the macro while allowing you to zoom in on the micro.
Here’s an example of such a company-wide metrics dashboard:
Metrics is a big topic that I cover in a lot more detail in my book and courses, but for a quick primer, check out this video:
II. Use metrics to identify anomalies.
Contrary to common belief, metrics aren't for arriving at good explanations but for identifying anomalies.
Metrics are factual. They sometimes can but typically don’t offer good explanations on their own. Metrics tell you what happened, not why.
They are really effective at identifying anomalies or deviations from the baseline, which leads to the crucial next step.
III. Chase anomalies to arrive at plausible explanations.
It’s easy to guess an explanation for good or bad results, but it's much harder to formulate a good explanation that is grounded in causality.
Causality is the litmus test for a good explanation or good insight that leads to repeatable results.
I find that arriving at a good explanation after an experiment almost always requires a combination of quantitive and qualitative learning.
In other words, metrics tell you something good or bad just happened. But talking to customers is how you infer meaning.
This is why I also rank customer interviewing as another top skill or key activity to develop as a founder, which can be a topic for another day :)
See you next week.
Ash
Author of Running Lean and creator of Lean Canvas
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P.S.
Whenever you're ready, there are 2 ways I can help you:
1 - Get my Just Start email course: If you're new to this framework, you'll learn the key mindsets for building the next generation of products that matter.
2 - Take the 30-day Business Model Design Challenge: If you're an aspiring or early-stage founder looking to launch a new idea in 2025, join my next Business Model Design Challenge, where you'll learn how to design and stress-test your idea without wasting resources.
