Customers
AD
Ava Davies
dormantava.davies60@example.com · +447922183776
- Source
- airtable
- Last contacted
- 17 May 2025
- Mortgage renewal
- —
- Loan value
- £541,000
- Region
- North West
- Added
- 23 Mar 2023
Activity
-
Email opened — "It's been a while"
17 May 2025
-
Enrolled in Dormant Reactivation
23 Mar 2023
-
Imported from airtable
23 Mar 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 90
Churn risk 21
What's driving likelihood to respond
Engagement history 98
Recency of contact 85
Source quality 85
Profile & loan fit 98
Renewal timing 93
What's driving churn risk
Time since engagement 29
Contact frequency 32
Channel responsiveness 33
Relationship tenure 30
Best send time
Learning…
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.