Customers
OA
Oliver Ali
activeoliver.ali53@example.com · +447110483930
- Source
- airtable
- Last contacted
- 20 Mar 2026
- Mortgage renewal
- 02 Sept 2026
- Loan value
- £677,000
- Region
- North West
- Added
- 01 Oct 2023
renewal-soon self-employed newsletter
Activity
-
Email opened — "It's been a while"
20 Mar 2026
-
Enrolled in Dormant Reactivation
01 Oct 2023
-
Imported from airtable
01 Oct 2023
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 79
Churn risk 83
What's driving likelihood to respond
Engagement history 70
Recency of contact 64
Source quality 88
Profile & loan fit 65
Renewal timing 78
What's driving churn risk
Time since engagement 91
Contact frequency 72
Channel responsiveness 80
Relationship tenure 83
Best send time
Sun 5:00pm
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.