Customers
MS
Maya Smith
activemaya.smith20@example.com · +447589703360
- Source
- mailchimp
- Last contacted
- 13 Apr 2026
- Mortgage renewal
- 20 Sept 2026
- Loan value
- £565,000
- Region
- Scotland
- Added
- 18 Sept 2023
referral self-employed webinar-2025
Activity
-
Email opened — "It's been a while"
13 Apr 2026
-
Enrolled in Dormant Reactivation
18 Sept 2023
-
Imported from mailchimp
18 Sept 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 32
What's driving likelihood to respond
Engagement history 74
Recency of contact 92
Source quality 87
Profile & loan fit 95
Renewal timing 90
What's driving churn risk
Time since engagement 46
Contact frequency 45
Channel responsiveness 29
Relationship tenure 40
Best send time
Learning…
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.