A!theraPartners Retail AI demonstration

Retention, decided before the customer leaves.

RetentionPilot AI reads loyalty, purchase, engagement, and service signals to flag customers at risk of going inactive — and recommends the retention action most likely to bring them back.

Start retention analysis View churn dashboard

Fictional multi-category retailer — 50,000 loyalty members — 500-customer sample

The assistant

From scattered signals to a campaign-ready decision.

Early churn detection

Declining customers are flagged 30 to 90 days before they go silent, read from recency, frequency, engagement, and service signals rather than a single last-order date.

Explained risk, not black-box scores

Every score arrives with the drivers behind it, written in plain language a marketing manager can read, question, and act on without a data team.

Actions matched to reasons

Service recovery for complaints, category reminders for lapsed interest, points for loyalists — and no discount where none is needed, so margin is protected by default.

The journey

How the demo flows.

Every recommendation in this assistant is a proposal for human review, not an automated send. It is built only on data customers have consented to, and no campaign launches without sign-off from the relevant marketing, CRM, and compliance teams.