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product decision engine

decide which product bets are worth building.

Qombine uses your existing product data to model how users may respond, helping your team prioritize before committing weeks of design and engineering time.

test Qombine on your data see the real backtest
qombine simulation output EXPERIMENT remove onboarding step predicted impact before design or build conversion likely increase activation likely increase retention watch closely CONFIDENCE medium SIMULATED SESSIONS 20,000 example prediction output

your roadmap has more ideas than capacity.

Teams spend weeks building features and experiments that sometimes move nothing. Qombine helps teams evaluate ideas before committing significant resources.

product data existing analytics current behavior user journeys proposed change feature or experiment evaluate outcomes likely impact roadmap signal prioritize resources

how it works

  1. learn current behavior: Build a behavioral model from your existing product data.
  2. model the change: Describe the feature, funnel change, or experiment your team is considering.
  3. evaluate before building: Simulate likely outcomes and use them as another signal when prioritizing the roadmap.

where simulation fits

simulation doesn’t replace experimentation. it helps decide what deserves to become an experiment in the first place.

decisions Qombine can help with.

  • new features

  • growth experiments

  • onboarding changes

  • conversion improvements

  • pricing or packaging changes

we tested this against real outcomes.

First, we tested whether Qombine could forecast how an existing e-commerce funnel would behave. Using historical session data, we generated predictions and compared them with what actually happened.

Event Predicted Actual Error
Add to Cart 3.78% 3.76% 0.6%
Purchase 0.82% 0.80% 2.0%
View Item 17.69% 17.85% 0.9%
Add Shipping Info 1.65% 1.60% 2.8%

Based on historical backtests, not hypothetical scenarios
Tested across multiple funnel stages using ~80k real sessions

example decision workflow

from product idea to build decision.

example: remove an optional onboarding step illustrative workflow
  1. 01

    product change

    remove one optional step from onboarding

  2. 02

    modeled user response

    fewer users abandon setup and more reach activation

  3. 03

    likely business outcome

    higher activation potential, with retention risk surfaced early

  4. 04

    build / no-build decision

    move to a scoped experiment, with retention as a guardrail

test Qombine on your data