Expected Value / Bet Sizing

Use it when you've got multiple projects or experiments and need a clear way to compare risk-adjusted payoffs.

What is it?

Expected Value / Bet Sizing is a data-driven prioritization framework from the Lean Startup playbook that turns gut calls into clear, comparable scores.

You quantify each idea by estimating the probability of success, assigning an impact value (like revenue lift or retention gain) and factoring in the cost of investment. The core formula, EV = Probability of Success × Impact − Probability of Failure × Cost, lets you rank features, experiments or campaigns by their risk-adjusted return. Borrowed from finance and tuned for product teams, this method solves the classic resource-allocation problem: how to invest limited time and budget across dozens of hypotheses. Its main components include defining clear success/failure outcomes, calibrating probabilities (based on data or expert judgment), measuring impact metrics (ARR, activation rate, engagement lift) and tallying costs (engineering hours, ad spend, operational overhead).

By blending upside, downside and uncertainty in one unified scale, you sidestep shiny-object syndrome and zero in on the bets that truly move the needle.

Why it matters?

In growth and product, you're juggling scarce resources against an endless backlog. Expected Value / Bet Sizing forces you to quantify risk and reward across every initiative, so you fund the highest-upside experiments and cut the ones that drain budget without promise. That discipline leads to faster learning loops, higher ROI on dev hours and marketing spend, and ultimately a growth engine that scales predictably.

How it works

Growth co-pilot turns your toughest product questions into clear, data-backed recommendations you can act on immediately.

Inventory your bets

List every feature, experiment or marketing test you're considering and document the desired outcome for each.

Define impact metrics

Assign a measurable outcome, revenue, activation lift or retention increase, that reflects each bet's potential payoff.

Estimate probabilities

Use historical data, analog experiments or expert input to gauge your success likelihood and plug it into the formula.

Calculate EV for each idea

Apply EV = Success Probability × Impact − Failure Probability × Cost to get a single score per bet.

Rank and decide

Order your bets by descending EV, set a minimum threshold, and allocate resources to the highest-scoring initiatives first.

Frequently asked questions

Growth co-pilot turns your toughest product questions into clear, data-backed recommendations you can act on immediately.

How do I pick probabilities for new experiments?

No crystal ball needed, start with benchmark data, past test results or expert consensus. Calibrate over time by comparing your estimates against actual outcomes and adjust future bets accordingly.

How do I pick probabilities for new experiments?

No crystal ball needed, start with benchmark data, past test results or expert consensus. Calibrate over time by comparing your estimates against actual outcomes and adjust future bets accordingly.

Can I use non-financial metrics in EV calculations?

Absolutely. Swapping revenue for activation rates, retention lift or engagement scores works just as well. The key is consistency: pick a metric that aligns with your core growth objective and stick to it across bets.

Can I use non-financial metrics in EV calculations?

What's the difference between EV and other scoring models like RICE?

RICE scores ideas on Reach, Impact, Confidence and Effort but lacks a unified currency for reward versus risk. EV converts all elements into a single, risk-adjusted return figure, ideal when you need to compare vastly different bets on one scale.

What's the difference between EV and other scoring models like RICE?

How often should I revisit my EV estimates?

Treat EV as a living document. Recalculate whenever you get meaningful data, after an experiment, a market shift or a major feature launch, to keep your prioritization razor-sharp.

How often should I revisit my EV estimates?

Treat EV as a living document. Recalculate whenever you get meaningful data, after an experiment, a market shift or a major feature launch, to keep your prioritization razor-sharp.

What pitfalls should I watch out for when using EV?

Beware biased probability estimates, overlooked costs (like maintenance or opportunity cost) and ignoring dependencies between bets. Keep your assumptions transparent and review them regularly with the team.

What pitfalls should I watch out for when using EV?

You've sized your bets with EV modeling, now don't shoot in the dark. Run your top-scoring experiments through CrackGrowth's diagnostic to uncover hidden friction and design winning variations before you launch.

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