Customers
AW
Amelia Wilson
activeamelia.wilson62@example.com
- Source
- smartr
- Last contacted
- 22 Mar 2026
- Mortgage renewal
- —
- Loan value
- £666,000
- Region
- South East
- Added
- 27 Sept 2024
referral
Activity
-
Email opened — "It's been a while"
22 Mar 2026
-
Enrolled in Dormant Reactivation
27 Sept 2024
-
Imported from smartr
27 Sept 2024
AI scoring How these are scored
The model scores each contact 0–100 from their engagement history, recency of last contact, source quality, and profile signals (loan value, renewal timing).
Likelihood to respond — higher is better. Churn risk — higher means more likely to go cold. Scores refresh as new activity arrives.
Likelihood to respond 89
Churn risk 67
What's driving likelihood to respond
Engagement history 95
Recency of contact 83
Source quality 99
Profile & loan fit 82
Renewal timing 100
What's driving churn risk
Time since engagement 64
Contact frequency 81
Channel responsiveness 75
Relationship tenure 76
Best send time
Wed 12:00pm
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.