Customers
EB
Ethan Brown
activeethan.brown44@example.com
- Source
- mailchimp
- Last contacted
- 14 Apr 2026
- Mortgage renewal
- 27 Oct 2026
- Loan value
- £539,000
- Region
- North West
- Added
- 19 Jul 2023
self-employed newsletter webinar-2025
Activity
-
Email opened — "It's been a while"
14 Apr 2026
-
Enrolled in Dormant Reactivation
19 Jul 2023
-
Imported from mailchimp
19 Jul 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 82
Churn risk 28
What's driving likelihood to respond
Engagement history 81
Recency of contact 69
Source quality 80
Profile & loan fit 80
Renewal timing 96
What's driving churn risk
Time since engagement 24
Contact frequency 27
Channel responsiveness 21
Relationship tenure 25
Best send time
Learning…
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.