ROI Made Simple: How to Evaluate the Impact of Any Feature
Calculating the elusive ROI of a feature
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As product managers, one of the most common questions we face is: “What’s the ROI of this feature?” It’s a fair question—stakeholders need to know whether the resources invested in a feature will drive meaningful business outcomes. But for many PMs, especially those early in their careers, calculating ROI can feel like venturing into uncharted territory.
The good news? You don’t need perfect data or advanced analytics to get started. ROI modeling is less about math and more about thinking critically—understanding what your feature impacts, modeling its outcomes, and linking them to business value. Here’s how to approach it with confidence and clarity.
In this article, I focus on feature forward calculations, in order to explain how further calculations are made.
Step 1: Understand What the Feature Impacts
Every feature exists to solve a problem or drive a specific outcome. To model ROI, you first need to define what the feature impacts—and do so clearly. For example, if you’re building a feature to improve user engagement, ask yourself:
What does “engagement” mean in the context of your product?
How will this feature drive a measurable change in that engagement?
Engagement can mean different things depending on your product. It might be:
Session Frequency: How often users open the app.
Session Duration: How long they stay per session.
Weekly Active Users (WAU): The number of users engaging with the app in a given week.
Let’s say your app is a habit-tracking app, and you’re introducing a daily reminder notification feature.
Feature Goal: Increase user engagement by reminding users to log their habits daily.
Likely Impacted Metrics:
Session Frequency: The number of times users open the app per week.
Daily Active Users (DAU): The percentage of users engaging with the app daily.
Here’s why:
Session Frequency: The reminders encourage users to interact with the app more often, increasing their number of sessions each week.
DAU: Users are more likely to log their habits on the day they receive a notification, boosting daily activity.
By focusing on these metrics, you define the primary ways the feature impacts user behavior. This clarity will guide your ROI modeling, as you’ll now assess how increased session frequency or DAU affects retention and, ultimately, revenue.
Step 2: Model the Impact, Step by Step
Once you’ve identified what the feature impacts, the next step is to map out the relationship between the feature, your chosen metrics, and broader business outcomes. Here’s a simple framework:
Define the Baseline:
Start with the current state of the metric you’re trying to improve. For example:Users currently open the app 2 times per week (baseline session frequency).
Estimate the Impact of the Feature:
Use past data, user insights, or team expertise to estimate how the feature will change this metric. Example:Adding personalized reminders will increase session frequency from 2 times per week to 3 times per week.
Link the Metric to Retention:
Higher engagement often improves retention. If historical data shows that users with 3+ sessions per week retain 10% better than those with fewer sessions, you can estimate how the feature impacts retention.Adjust the Retention Curve:
Update your retention curve to reflect this impact. Example:Current retention: 40% at Week 8.
New retention with feature: 50% at Week 8.
Step 3: Translate Retention into Revenue
Retention improvements directly affect revenue, especially in subscription or freemium models. To calculate this, you can:
Keep Installs and ARPU Constant:
Assume that your user acquisition (installs) and average revenue per user (ARPU) stay the same.Baseline Revenue:
Multiply installs by retention and ARPU to calculate the baseline. Example:Installs: 10,000/month.
Retention: 40%.
ARPU: $5/month.
Baseline revenue: 10,000×40%×$5=$20,000
New Revenue with Feature Impact:
Update the retention rate and calculate again:Retention: 50%.
New revenue: 10,000×50%×$5=$25,000
Calculate the ROI:
Subtract the feature cost from the revenue increase, then divide by the cost. Example:Feature cost: $2,000.
ROI: (25,000−20,000−2,000)/2,000=150%.
Actionable Takeaways for PMs
Start with a Hypothesis:
Clearly define how the feature impacts key metrics before building it. Example: “If we increase session frequency, retention will improve by X%.”Use Historical Data:
Look at past user behavior to validate assumptions. Example: “Users with 3+ sessions/week have 10% higher retention than others.”Simplify Metrics:
Stick to metrics you can measure and connect directly to business outcomes. Example: “Retention improvements drive higher revenue.”Iterate as You Learn:
Your first model doesn’t need to be perfect. Treat it as a draft and refine it as more data becomes available.Communicate Clearly:
Share the logic behind your ROI calculations with stakeholders, even if the numbers are rough. Focus on the story, not just the math.
Common Pitfalls and How to Avoid Them
Overcomplicating the Model:
Don’t try to account for every variable upfront. Start with the basics: baseline, projected impact, and business value.Ignoring Data Validation:
Always sanity-check your assumptions. Example: If you assume a 10% retention lift, ensure historical data supports it.Focusing Only on Revenue:
ROI isn’t always about dollars. For early-stage features, improving retention or engagement might be the bigger win.
Finally: A Mindset Shift
Modeling ROI isn’t about perfect math—it’s about thinking critically and connecting the dots between a feature and its business value. As PMs, our job is to make thoughtful decisions, align stakeholders, and prioritize features that truly move the needle.
The next time someone asks you, “What’s the ROI of this feature?” don’t panic. Define the goal, map the logic, and frame it as a clear, actionable story. It’s less about getting it “right” and more about showing the impact your work can have.