How the intelligence models work
What are intelligence models?
LiftSignals trains 12 machine learning models on your specific store's order and customer data. Unlike generic industry benchmarks, these models learn your store's unique patterns — your typical purchase cadence, seasonal rhythms, and customer behaviour.
The 12 models at a glance
| Model | What it predicts | Updates |
|---|---|---|
| Churn Detection | Probability a customer will not return (0–100 score) | Every 2 hours |
| CLV Forecast | Predicted revenue from a customer over the next 12 months | Daily |
| Win-back Timing | The optimal day to contact a lapsed customer | Every 2 hours |
| RFM Segmentation | Customer segment based on Recency, Frequency, Monetary value | Daily |
| Propensity to Buy | Likelihood of purchasing in the next 30 days | Daily |
| Repurchase Prediction | Probability of a customer buying again based on their history | Daily |
| Customer Health Score | Overall engagement health (0–100) | Daily |
| Cohort Retention | Retention curves by acquisition cohort | Weekly |
| Channel Attribution | Which acquisition channels produce the best long-term customers | Weekly |
| LTV Maximizer | Actions most likely to increase a customer's lifetime value | Daily |
| Spend Optimizer | Where to focus retention spend for maximum ROI | Weekly |
| AI Recommendations | Synthesises all 11 models into prioritised daily actions | Every 2 hours |
How models train
On first connection, LiftSignals trains all models on your complete historical data. After that, models retrain automatically when:
- A new sync completes and adds significant new data
- LiftSignals detects a meaningful shift in your store's patterns
- You manually trigger retraining from Model Studio
Model accuracy
Accuracy is displayed in Explore → [Model Name] for each model. The Churn Detection model typically achieves 87–94% precision across our customer base. Accuracy is higher with:
- More order history (12+ months gives the best results)
- More complete data (high email fill rate, complete order dates)
- Consistent purchase patterns (models struggle with very erratic buying behaviour)
New stores: Models need at least 500 customer records and 90 days of order history to produce reliable scores. With less data, scores are provisional — treat them as directional guidance, not hard predictions.
Model Studio
Advanced users can adjust model thresholds, run scenarios, and compare model versions in Model Studio. This is covered in detail in the Model Studio documentation.
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