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increase experiment volume with confidence

Stop wasting weeks on experiments that don’t move revenue

teams can’t increase experiment volume because too much product and engineering time is spent on tests that don’t move revenue. qombine predicts impact before you build so you focus only on experiments that drive revenue.

Run a backtest predict impact
qombine simulation output EXPERIMENT remove onboarding step predicted impact before design or build conversion +3.2% activation +1.1% retention -0.4% CONFIDENCE medium SIMULATED SESSIONS 20,000 example prediction output

Decision loop

Without Qombine

idea → build → experiment → learn

With Qombine

idea → simulate → experiment → learn

Decide which experiments are worth running.

Qombine models real user journeys from event data and uses simulation to estimate how product changes affect behavior across your funnel.

validated behavioral model

Convert analytics events into session journeys and transition models. Qombine learns how users move through your product and validates the model against real outcomes before running simulations.

Experiment simulation

Define experiment variants and simulate thousands of sessions to estimate how behavior and conversion rates change.

Decision-ready outputs

See predicted lift, confidence ranges, and funnel impact for each variant before running the real experiment.

analytics events ga4 / amplitude session reconstruction user journeys behavioral model transition probabilities simulation engine synthetic sessions predicted outcomes conversion lift

How Qombine models user behavior

  1. Import event data: Upload analytics events or past experiment logs to reconstruct user journeys.
  2. Learn behavioral transitions from session data: Qombine builds a model of how users move through your funnel.
  3. Simulate experiment variants at scale: Run thousands of simulated sessions to estimate behavioral impact.
  4. Predict outcomes: See expected lift, funnel changes, and confidence ranges for each variant.

Who this is for

Qombine is a simulation layer for product experimentation. Teams use it to test ideas before running experiments on users.

  • Growth teams

    Prioritize experiments with the highest expected impact.

  • Product teams

    Evaluate product changes before investing design or engineering effort.

  • Experimentation teams

    Use simulation to increase experiment velocity.