UC-743
Improve Customer Retention and Reactivation to increase output and recover lost value
Lapsing customers found early and offered the thing most likely to bring them back.
TBDBuild Duration
XXXXIndicative ROI
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XXXXValue
The Challenge
The opportunity is to eliminate one blanket offer sent to everyone, discounts given to customers who would have returned anyway, and reactivation attempted long after the customer moved on. The proposed workflow is source-linked, but the current operating baseline and financial value remain illustrative until validated with the client.
How It Works
- Combines order history, browse behaviour, category affinity and service contacts per customer.
- Scores lapse risk and models which offer changes the outcome for that customer.
- Runs the campaign and measures the incremental effect against a holdout group.
What It Removes
- One blanket offer sent to everyone
- Discounts given to customers who would have returned anyway
- Reactivation attempted long after the customer moved on
Input Data RequirementsOrder and basket history, browse and app behaviour, service contacts, campaign and offer history
Output FormatLapse risk score per customer, offer recommendation by segment, incremental revenue against holdout
“Which customers are drifting away while we still have time to act?”