Repeat-Purchase & Churn for Shopify
A Shopify-tuned deployment of the canonical Churn Prediction & Retention Engine — handles both transactional repeat-purchase prediction and subscription-aware churn (Recharge / Bold / Shopify Subscriptions integration), wires reactivation triggers into Klaviyo flows.
- Engagement
- 6–10 week deployment · self-serve to custom packaging
- Built for
- DTC operators · Subscription-product brands · Retention teams
DTC repeat-purchase patterns are tracked but rarely predicted — the store knows the aggregate repeat-rate but doesn't know which customers are about to lapse or which subscribers are heading for churn.
What this is
The canonical Churn Prediction & Retention Engine, tuned for Shopify's repeat-purchase and subscription patterns. See the canonical product page in the Operations Algorithms Suite for the modeling backbone, risk-driver attribution, and retention-action library. The Shopify-tuned tuning consists of:
- Recharge / Bold / Shopify Subscriptions integration for subscription-aware churn modeling.
- Shopify Customer and Orders APIs for the transactional repeat-purchase signal.
- Klaviyo segment sync for retention-flow integration.
- Shopify Audiences sync for marketing-campaign targeting.
What you get
- The churn model trained on your store's data.
- Subscription-aware predictions for stores running subscription apps.
- Klaviyo and Shopify Audiences sync.
- Risk-driver attribution per predicted customer.
- Quarterly model refresh.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 6–10 week deployment · self-serve to custom packaging
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Also in · Operations Algorithms Suite
Churn Prediction & Retention Engine
A classification layer per customer — churn probability with confidence bands, risk-driver attribution (why the model thinks this customer is at risk), and retention-action recommendations tied to the predicted risk drivers.
- Same suite · Shopify Ops Intelligence Suite
Customer Clustering for Shopify
A Shopify-tuned deployment of the canonical Customer Clustering & LTV Engine — uses Shopify Customer and Orders data plus metafields, syncs results to Klaviyo and Shopify Audiences for activation.
What buyers ask about this one.
How does it handle Recharge subscriptions?
Recharge integration is canonical — subscription events (initial, renewal, skip, cancel) feed into the model as labeled signal. The churn prediction differentiates 'about to skip cycle' from 'about to cancel,' which require different retention actions.
What about Shopify Subscriptions (the native one)?
Supported. The data shape is similar to Recharge though some signal nuances differ. The model adapts.
What's the retention-action integration?
Predicted-risk customers feed into Klaviyo segments (or Shopify Audiences) where the retention team has flows configured. The engine doesn't replace the marketing-flow infrastructure — it informs it with risk signal.
How is this different from the OAS canonical?
Same modeling backbone, Shopify-and-subscription-app data integration on top. Cross-link to the canonical for the modeling detail.
Pricing?
Tiered per buyer profile. Discovery call covers the right tier.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026