Customers
SB
Sophie Brown
activesophie.brown19@example.com · +447871714311
- Source
- airtable
- Last contacted
- 05 May 2026
- Mortgage renewal
- 21 Nov 2026
- Loan value
- £597,000
- Region
- North West
- Added
- 03 Oct 2025
renewal-soon no-reply-90d
Activity
-
Email opened — "It's been a while"
05 May 2026
-
Enrolled in Dormant Reactivation
03 Oct 2025
-
Imported from airtable
03 Oct 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 93
Churn risk 67
What's driving likelihood to respond
Engagement history 100
Recency of contact 78
Source quality 79
Profile & loan fit 100
Renewal timing 79
What's driving churn risk
Time since engagement 78
Contact frequency 58
Channel responsiveness 77
Relationship tenure 65
Best send time
Thu 7:00pm
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.