Customers
OS
Oliver Smith
dormantoliver.smith33@example.com · +447972895090
- Source
- smartr
- Last contacted
- 13 Jun 2025
- Mortgage renewal
- —
- Loan value
- £658,000
- Region
- Scotland
- Added
- 14 Nov 2025
Activity
-
Email opened — "It's been a while"
13 Jun 2025
-
Enrolled in Dormant Reactivation
14 Nov 2025
-
Imported from smartr
14 Nov 2025
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 74
Churn risk 50
What's driving likelihood to respond
Engagement history 66
Recency of contact 76
Source quality 85
Profile & loan fit 76
Renewal timing 81
What's driving churn risk
Time since engagement 47
Contact frequency 43
Channel responsiveness 49
Relationship tenure 40
Best send time
Learning…
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.