Model accuracy issues

Churn model accuracy is below 80%

Accuracy below 80% is usually explained by one or more of these:

• Insufficient data — The model needs at least 500 customers and 90 days of history. With less, scores are provisional.
• Very low purchase frequency — Stores where customers buy once a year or less have inherently noisy churn signals. Consider the Home & Furniture workspace template which adjusts thresholds for infrequent-purchase stores.
• Missing first_order_date — Without this, the model can't understand customer tenure, which is a strong churn predictor.
• Heavy promotional activity — Stores with frequent large promotions have erratic purchase patterns that are harder to model. Accuracy typically improves over 3–6 months as the model learns your promotional calendar.

Model says a customer is "safe" but they've clearly churned

Check if this customer has an unusual purchase cadence (e.g., they buy every 6 months and the model's average is monthly). The model scores relative to each customer's personal rhythm — a 6-month gap for an annual buyer isn't a churn signal.

If the model is wrong, you can manually override the score from the customer profile. Consistent overrides for similar customer types can be flagged to support — they may indicate a model configuration opportunity in Model Studio.