Customers
MB
Maya Bell
activemaya.bell84@example.com · +447982361582
- Source
- airtable
- Last contacted
- 14 May 2026
- Mortgage renewal
- 04 Mar 2027
- Loan value
- £615,000
- Region
- Midlands
- Added
- 29 Mar 2025
referral no-reply-90d remortgage
Activity
-
Email opened — "It's been a while"
14 May 2026
-
Enrolled in Dormant Reactivation
29 Mar 2025
-
Imported from airtable
29 Mar 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 97
Churn risk 44
What's driving likelihood to respond
Engagement history 89
Recency of contact 100
Source quality 100
Profile & loan fit 84
Renewal timing 100
What's driving churn risk
Time since engagement 42
Contact frequency 39
Channel responsiveness 40
Relationship tenure 41
Best send time
Learning…
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.