Customers
OA
Oliver Ahmed
dormantoliver.ahmed49@example.com · +447629997644
- Source
- airtable
- Last contacted
- 28 Sept 2025
- Mortgage renewal
- 26 Sept 2027
- Loan value
- £716,000
- Region
- Scotland
- Added
- 16 Sept 2024
buy-to-let self-employed high-value
Activity
-
Email opened — "It's been a while"
28 Sept 2025
-
Enrolled in Dormant Reactivation
16 Sept 2024
-
Imported from airtable
16 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 93
Churn risk 56
What's driving likelihood to respond
Engagement history 80
Recency of contact 91
Source quality 100
Profile & loan fit 90
Renewal timing 100
What's driving churn risk
Time since engagement 57
Contact frequency 42
Channel responsiveness 45
Relationship tenure 59
Best send time
Learning…
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.