Customers
DB
David Begum
engageddavid.begum11@example.com · +447761455314
- Source
- airtable
- Last contacted
- 27 Feb 2026
- Mortgage renewal
- —
- Loan value
- £387,000
- Region
- North West
- Added
- 11 Sept 2022
Activity
-
Email opened — "It's been a while"
27 Feb 2026
-
Enrolled in Dormant Reactivation
11 Sept 2022
-
Imported from airtable
11 Sept 2022
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 96
Churn risk 44
What's driving likelihood to respond
Engagement history 100
Recency of contact 86
Source quality 100
Profile & loan fit 85
Renewal timing 91
What's driving churn risk
Time since engagement 52
Contact frequency 53
Channel responsiveness 29
Relationship tenure 35
Best send time
Thu 9:30am
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.