Which user outcomes correlate most with revenue?

Performance Valuation covers both improving outcome delivery and choosing the right outcomes to deliver. Improving outcome delivery will not result in business value if the business doesn't benefit from delivering the outcome.

A key aspect of Performance Valuation, therefore, is quantifying the impact of delivering the different outcomes that the business offering currently supports. Some are bound to be more lucrative (financially) than others. Those are the ones to uncover and invest in.

So how do you figure out which outcomes most correlate with revenue? We add a layer of intelligence to theΒ weekly cohort reports.

Imagine a weekly cohort of users. How many users in this particular cohort are pursuing outcome X, Y, or Z? For example, in our invoice-generating company, how many users per cohort are interested in engaging with the product to A) customize their invoices with client-specific terms of service B) keep all the invoice the company generates organized for audits or C) build invoice templates that they can reuse for similar clients.

The idea is to analyze the performance of these segments within this particular cohort of users and ask questions like:

  • Of the people in this cohort who are pursuing outcome A, how many went on to become healthy customers? How does this compare to outcomes B and C?
  • How many users pursuing outcomes B churned out? When? Is this consistent across cohorts?

The line of questioning sheds a light on which situations are creating the most value for the company. If it turns out that outcome A greatly outperforms outcome B in that it users who are pursuing outcome A generally pay for months longer than those pursuing outcome B, then investing in outcome A is a no-brainer. And if users pursuing outcome C tend to churn out within the first two months at a high degree, it is demonstrably not moving the needle in terms of revenue.

It's possible to get even more granular. If you add in the additional layer on user behavior, you can start to ask questions like:

  • Of the people who are pursuing outcome A how many have used the "terms of service" feature?
  • Did outcome A pursuers who used the "terms of service" feature stick around for longer than the ones who didn't?
  • In terms of customer performance, which features are most valuable to outcome A pursuers?

This helps you start to see the difference between the performance of the people who did a particular activity in-app thatΒ theyΒ care about and the performance of the undifferentiated masses of signups who didn't engage with that activity.

Once you know what really moves the revenue needle, the job goes from being "let's get more people to use the "terms of service" feature" to "let's design the path to outcome A to expose users to the "terms of service" feature as soon as possible." The point is that visibility can help you pinpoint what's really contributing to revenue and what isn't so that yourΒ theories of changeΒ can get more targeted over time.

When you can start drawing a strongly correlative relationship between a user achieving a particular outcome and the vastly improved performance for the members of the cohort who did that thing, then you really have an idea of whether you should design for that outcome to take place more.

From VPP Episode 04 (29.42)