Example Case

How does an actual draft of an estimation look like I do them?
An actual example of a big opportunity from a semi-invented B2B SaaS platform with a product-led growth distribution model:

Aside from the numbers which I changed, this is exactly what it looks like in the real world.

It serves as a hypothesis that is quite visual and is a great discussion basis for the team before we dive into validation/invalidation.

Colour coding helps to see which assumptions are with high confidence (green, comparable features exist already, real data) or low confidence (red, completely made-up assumptions based on gut feeling)

Recurring themes for use cases or feature additions:

• Direct impact on your existing customer base.

  • Retention -> LTV

  • Cost of Switching -> LTV

  • External Top of funnel impact -> How many additional users does this drive per month to the platform (SEO + paid)

• Virality impact -> Loop impact

• Churn prevention -> How many existing customers wouldn't churn that do churn today if the feature existed

The next step would be to rip the case apart by challenging assumptions and looking for opportunities how to validate with little effort. In general the more to the LEFT / (Start of a process) an assumption is the more likely you want to drive down uncertainty.

The goal to me is usually not accuracy but to spot extremely limited cases by thinking them through.

Understanding Confidence & Impact

2 factors matter when building a business case:

• How confident are you of each assumption?

• How big is the potential impact of each assumption?

Confidence

We can infer confidence by comparing it to existing data points - like similar features. If those don't exist we can look at qualitative indicators.

Before those have been connected to quantitative exploration (experiments etc.) they are not in the high confidence bracket.

Another important indicator is *who* you ask. The big difference between customers and users is that we have proven in the first group that they want to pay for our solution.

Users have proven shown interest. Potential users who have never heard about us have no proof attached to them. This affects confidence.

Impact

For impact, we can have a simple look at the context of the solution.

Let's assume we want to see the impact of a batch processing feature (processing multiple files at the same time):

If there is a feature that can process 1 document it can also process 10 documents one by one. Or 100. Theoretically at least.

For each group of users the impact is different:

• 1 Document/day users: No impact. They don't need it

• 10 Documents /day users: Medium Impact, this makes their life easier. Instead of running the tool 10 times

• 100 Documents/day users: High Impact, these users can't do it at all.

For confidence, it's worth trying to find simple methods to move up. For impact, it depends on the stage of your company.

Summary

Early-stage companies should aim for High Impact solutions. Whereas typically scaling organizations are better at defending their current growth by optimizing.

The next posts will dive into more specifics and when you simply have to stop going into more detail. There is something like spending too much time assuming.