By Tom Laufer
As I get the chance to meet with people who are working on their 2025 planning, I’m still sometimes surprised to see that very few companies prioritize opportunity sizing in their roadmap. And for those that do, it’s based on the RICE/ICE framework (Reach, Impact, Confidence, and Effort; and Impact, Confidence, and Ease - and there are many others).
This issue is that RICE/ICE approaches are really biased.
Assuming you’re a B2B PLG / PLS company or B2C product company, you can really leverage your data to size the opportunities of your hypotheses.
Opportunity size is a function of Reach multiplied by Impact.
Let’s explore these two parameters:
REACH:
Reach is calculated as the number of users that will be exposed to the new feature/initiative and by estimating, based on previous benchmarks, the percentage of users that will adopt/be affected by this initiative. Typically, Reach is usually both easier to calculate and to move, than Impact.
IMPACT:
This is a bit more tricky. Some people try to calculate Impact based on benchmarks from previous launches and correlations - which, as you know, could be very misleading. At Loops, we leverage causal models to run Sensitivity Analysis (”What If”) in order to calculate the exact impact of changing one KPI (or feature) on the other KPIs.
While this method is not 100% accurate (and intuition still plays a major role), it does give you a directional understanding of whether the opportunity is big enough to warrant putting your time and resources on it.
💡 In many cases, you will actually discover that even if your hypothesis is successful, its maximum impact will not move the needle for the company.
