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Sequenzy computes per-customer predictions from your commerce order history: how much each customer is likely to spend over the next 12 months, how likely they are to churn, and when their next order is expected. Predictions refresh nightly and after each store sync, and they power segments, automations, and the subscriber profile. Predictions work with any commerce order source - Shopify, WooCommerce, or the Commerce API - because they are computed from provider-neutral ecommerce.order_placed events.

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Prediction attributes

Each scored customer gets these subscriber attributes. You can filter on them anywhere attributes work: segments, campaign targeting, and the API. Because predictions are ordinary subscriber attributes, they appear on the subscriber profile, in attribute autocomplete, and in subscriber responses from the API, the CLI (sequenzy subscribers get), and the MCP get_subscriber tool - no special endpoints needed.
Prediction attributes are managed by Sequenzy. You can read and filter on them freely, but manual edits are overwritten by the next nightly refresh.

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How the model works

The model is deliberately explainable rather than a black box:
  • Expected reorder interval blends the customer’s own median gap between orders with your store’s median. A customer with four or more observed gaps is scored mostly on their own cadence; a one-order customer starts from the store’s cadence.
  • Repeat probability starts at your store’s repeat-purchase rate for one-order customers and rises with every repeat order the customer places.
  • Churn risk combines repeat probability with how overdue the customer is: the survival score halves for every expected interval that passes without an order.
  • Predicted spend multiplies a blended average order value (personal history weighted against the store’s predicted AOV) by the expected number of orders over the next year. It counts future spend only, so it can be lower than the customer’s lifetime value - a customer who already spent 500butisunlikelytoreordermayshowapredictedspendof500 but is unlikely to reorder may show a predicted spend of 40.
A brand-new customer can show a high churn risk minutes after their first order. That is the store baseline, not a judgment of that customer: if only 15% of your customers ever place a second order, every first-time buyer starts near 85% until a repeat purchase proves otherwise. The number drops sharply after a reorder, and the at-risk segment and automation events only pick customers up once they are actually past their expected next order date.
Confidence is high for customers with 5+ orders, medium for 2-4, and low for one-order customers - and never exceeds your store-level forecast confidence, which depends on how much order history the store has.

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Eligibility

Predictions require the same baseline as the store-level commerce forecast: at least 20 orders from 10 customers, 30 days of history, 3 repeat customers, and an order in the last 45 days. Until your store qualifies, the Metrics dashboard lists exactly what’s missing.

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Predictive segments

Three predefined segments are available out of the box under Segments → Templates:
  • At-risk customers - churnRisk >= 70 and expectedNextOrderAt before today, so first-time buyers who start at the store’s baseline churn risk are not flagged until they actually miss their expected reorder window
  • Predicted VIPs - predictedLtv >= 200
  • Due to reorder soon - expectedNextOrderAt within the next 7 days
You can build your own with attribute filters, including relative dates - for example expectedNextOrderAt before today targets everyone overdue for their predicted reorder.

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Automation events

The nightly job emits two built-in events you can use as sequence triggers:
  • ecommerce.reorder_window_missed - fires once per order cycle when a customer passes their expected next order date plus a grace period (25% of their interval, at least 3 days) without ordering. Ideal for winback sequences timed to each customer’s actual buying cadence instead of a fixed delay.
  • ecommerce.churn_risk_elevated - fires when a customer’s churn risk crosses 70%. Ideal for at-risk offers before the customer is gone.
Both events carry churnRisk, predictedLtv, orderCount, lastOrderAt, expectedNextOrderAt, and currency properties for use in email content and conditional blocks.
Events only fire for crossings that happened in the last couple of days. Enabling predictions on a store with years of dormant customers never sends a burst of late winback automations.
For product-specific reminders (“your protein powder should be running low”), see replenishment reminders - they complement the customer-level reorder prediction.

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Store-level forecasts

The Metrics dashboard also shows store-level predictions - predicted AOV, 12-month customer value, and expected 90-day revenue with confidence ranges. Those are documented in the analytics API reference and available via GET /api/v1/metrics, sequenzy stats, and the MCP get_stats tool.