Better Products Start with Opportunity Sizing

Opportunity sizing is based on discipline, not precision. The teams that consistently work on the right things aren’t better at predicting exact outcomes. They’re better at structuring their thinking about potential impact before committing resources.

What We’ll Cover

This guide examines how to size opportunities in ways that actually improve decision-making. We’ll explore the fundamental concepts, the common traps that lead teams astray, and a practical framework you can apply to your own prioritisation discussions.

Understanding the Fundamentals

Core Concepts Explained

Opportunity sizing is the practice of estimating the potential value of a product initiative before building it. But “value” can mean many things, and conflating them creates confusion.

Problem size: How many people have this problem, and how severe is it? A problem that affects millions mildly might matter less than one that affects thousands severely.

Solution fit: How well does your proposed solution address the problem? Perfect problem-solution fit multiplies impact; poor fit diminishes it, no matter how big the problem.

Our unique advantage: Can we solve this better than alternatives? If competitors can easily replicate or already serve this need well, the opportunity shrinks regardless of market size.

Capture rate: What portion of the potential value can we actually realise? Market size means nothing if you can’t reach the market or convert its interest into your outcomes.

Effective opportunity sizing considers all four dimensions. Teams that only look at market size miss the most common reason products fail: building for big markets without solving real problems well enough to win.

Why This Matters for PMs

As a PM, your most important job is ensuring the team works on things that matter. Opportunity sizing is how you make that judgment defensibly.

Without sizing, prioritisation becomes political. Whoever argues most persuasively wins, regardless of actual potential. With sizing—even rough sizing—you shift discussions from opinion to analysis. “This opportunity is larger because…” is a more productive conversation than “I think this is more important.”

Sizing also creates accountability. When you estimate an opportunity at X and it delivers Y, you learn something. Over time, this feedback loop improves your judgment. Teams that skip sizing never develop this calibration.

“The PMs I’ve seen make the best prioritisation decisions weren’t better at predicting outcomes. They were more disciplined about estimating them upfront so they could learn from the gaps.”

Common Pitfalls and How to Avoid Them

Mistakes to Watch For

TAM obsession: Total Addressable Market sounds impressive in pitch decks but means almost nothing for prioritisation. What matters is the slice you can actually capture and the effort required to capture it.

False precision: Opportunity estimates are inherently uncertain. Presenting them with decimal precision (“$4.7M impact”) creates false confidence and invites unhelpful debate about the specific numbers rather than the overall magnitude.

Ignoring effort: A $10M opportunity that requires 10 engineers for two years might be worse than a $2M opportunity that requires two engineers for one month. Size matters, but efficiency matters too.

Selection bias in research: Teams often size opportunities by surveying users who want the feature. This overstates demand. Representative research—including people who don’t care—gives more accurate sizing.

Static thinking: Opportunities change. Market conditions shift, competitors move, and your own product evolves. Sizing done six months ago might be obsolete. Refresh estimates for active decisions.

Conflating certainty and size: Large uncertain opportunities often get prioritised over smaller certain ones because the headline number looks better. Risk-adjusted thinking accounts for probability, not just magnitude.

Prevention Strategies

Use ranges, not points: Express opportunities as ranges that capture uncertainty. “Between 5 and 15% activation improvement” is more honest than “10% activation improvement” and invites better discussion.

State assumptions explicitly: Every opportunity estimate rests on assumptions. Make them visible. When assumptions change, estimates should too.

Compare to known baselines: Anchor new estimates to past results. “This is similar in scope to Project X, which delivered Y” is more credible than standalone projections.

Include effort in the comparison: Compare opportunity size to required investment. Whether you call it ROI, efficiency ratio, or bang-for-buck, the comparison matters more than absolute size.

Build feedback loops: Track what you estimated versus what happened. This calibration, accumulated over time, makes future estimates more reliable.

A Practical Framework

Step-by-Step Approach

Here’s a framework for sizing opportunities that balances rigour with practicality:

1. Define the outcome clearly

What specific change are you trying to create? “Improve retention” is too vague. “Increase 30-day retention for new users by X percentage points” is actionable.

Clear outcomes make sizing concrete. If you can’t define the outcome precisely, you can’t size it meaningfully.

2. Identify the levers

What drives the outcome you’ve defined? For retention, it might be activation quality, feature engagement, or perceived value. Breaking outcomes into component levers helps you estimate more accurately and spot where your solution has the most leverage.

3. Estimate the addressable population

Who could benefit from this improvement? Not everyone in your user base—just those for whom this opportunity is relevant. Being specific here prevents inflated estimates.

4. Estimate the change per person

If your solution works, how much improvement per affected user? This is often the hardest part. Use any available data: user research, competitive benchmarks, analogous past initiatives, or first-principles analysis.

5. Apply realistic capture rates

What portion of the addressable population will actually experience the improvement? Adoption rates, experiment reach, rollout timing—all affect how much potential value you actually realise.

6. Sanity check against constraints

Does your estimate pass basic reasonableness tests? Is it larger than your current performance allows? Does it require more change than similar initiatives have achieved? Extreme estimates usually signal errors in the calculation.

7. Express with appropriate uncertainty

Combine your estimates into a range that reflects confidence level. Communicate not just the estimate but your confidence in it.

Real Examples from Product Teams

A mobile app team was debating two opportunities: improving onboarding completion versus adding a frequently requested feature. The feature request had vocal supporters and felt like a “big” opportunity.

When they sized both systematically, the picture changed. The feature would affect engaged users who were already retained well. Onboarding improvement would affect the much larger population that never activated at all.

The onboarding opportunity was estimated at 3-5x the impact of the feature request, even using conservative assumptions about improvement rates. The team shifted priorities accordingly.

Six months later, the onboarding improvements delivered 4.2x the impact of what the feature request was estimated to deliver. More importantly, the team had learned to trust their sizing process and apply it consistently.

Key Takeaways

Opportunity sizing creates discipline in prioritisation—it’s not about precision, it’s about structured thinking
Consider problem size, solution fit, unique advantage, and capture rate—not just market size
Use ranges that reflect uncertainty rather than false point estimates
Include effort in comparisons—absolute opportunity size means little without knowing the investment required
Build feedback loops that improve calibration over time

Call to Action

Look at your current roadmap or backlog. Pick the top three items and ask: Do we have documented opportunity sizing for these? If you estimated impact, what was the basis?

If the answers are uncomfortable, you’ve found an improvement opportunity of your own. Start sizing your next prioritisation decision systematically, track the result, and build from there.

The teams that make better decisions aren’t smarter—they’re more disciplined about the thinking that precedes decisions.

Have questions or thoughts? Get in touch - I’d love to hear from you!