03Convertive DecisioningNot the first rule that matches.The move most likely to work.
Move from static rule priority to ranked in-session decisions. Convertive combines predictive modeling, context signals, and learning loops to select the next best action moment by moment.
Decision playground
Change the shopper. Watch the best move change.
A simplified model for illustration.
The shopper, right now
Eligible actions, ranked by expected lift
…
- 10% off, today only0.54
- Free shipping over $1500.42
- Reviews from buyers like her0.23
- Do nothing0.14
- Size-fit helper0.12
- Bundle: boots + socks, save 15%0.11
Why “10% off, today only”
Exit risk 82% and price sensitivity 80%: a small, time-boxed incentive has the highest expected lift.
The real engine scores with models trained on your store’s outcomes and decides in under 100 ms, event to rendered action.
How it works
From live signals to one best action.
01
Predictive intent and value
Score likely conversion, bounce risk, and potential value while the session is live.
02
Action ranking layer
Choose one best action among competing options to reduce noise and improve conversion quality.
03
Continuous learning loop
Use outcomes to update policy and improve intervention quality over time.
What’s inside
Under the hood.
- 01Propensity-based intervention timing
- 02Offer aggressiveness by risk and value segment
- 03Recommendation relevance weighting by in-session context
- 04Holdout-aware policy evaluation for incremental lift
- 05Journey-safe suppression and fallback logic
- 06Adaptive strategy updates from observed outcomes
Questions
AI Engine, answered.
What makes Convertive's AI decisioning different from rule-based personalization?
Rule-based systems fire triggers based on static conditions — "if cart value > $100, show this offer." Convertive's AI decisioning ranks competing actions by predicted outcome for each individual shopper using live session context, value signals, and intent scores. It selects the action most likely to convert, not just the first rule that matches.
How does the next-best-action engine handle multiple competing interventions?
When multiple journeys or actions are eligible for a shopper, the ranking layer selects one winner based on predicted conversion lift, suppression rules, and channel guardrails. This prevents offer collisions and message fatigue that degrade conversion quality.
Does the AI model learn from our store's specific data?
Yes. The decisioning engine incorporates your store's behavioral patterns, conversion signals, and historical outcomes into its scoring. The continuous learning loop updates the policy model as new session outcomes are observed, improving action quality over time.
How do I measure if the AI decisioning is actually lifting conversions?
Convertive Reporting includes holdout-aware lift measurement that isolates the incremental impact of AI-selected interventions versus control groups. You can see decision quality by segment, channel, and intervention type from the Reporting dashboard.
See AI Engine on your own traffic.
A 15-minute demo, using your store and your shoppers. Days to go live, not months.