Customers
DW
David Ward
activedavid.ward6@example.com · +447589293941
- Source
- mailchimp
- Last contacted
- 31 May 2026
- Mortgage renewal
- 28 Jul 2026
- Loan value
- £580,000
- Region
- South East
- Added
- 26 Sept 2024
first-time-buyer no-reply-90d
Activity
-
Email opened — "It's been a while"
31 May 2026
-
Enrolled in Dormant Reactivation
26 Sept 2024
-
Imported from mailchimp
26 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 79
Churn risk 74
What's driving likelihood to respond
Engagement history 67
Recency of contact 75
Source quality 82
Profile & loan fit 71
Renewal timing 76
What's driving churn risk
Time since engagement 70
Contact frequency 85
Channel responsiveness 73
Relationship tenure 67
Best send time
Learning…
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.