Customers
CH
Chloe Harris
dormantchloe.harris72@example.com
- Source
- airtable
- Last contacted
- 30 Nov 2024
- Mortgage renewal
- 29 Jun 2026
- Loan value
- £659,000
- Region
- London
- Added
- 18 Nov 2025
webinar-2025
Activity
-
Email opened — "It's been a while"
30 Nov 2024
-
Enrolled in Dormant Reactivation
18 Nov 2025
-
Imported from airtable
18 Nov 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 85
Churn risk 81
What's driving likelihood to respond
Engagement history 79
Recency of contact 83
Source quality 95
Profile & loan fit 91
Renewal timing 88
What's driving churn risk
Time since engagement 94
Contact frequency 76
Channel responsiveness 94
Relationship tenure 78
Best send time
Thu 7:00pm
Recommended next step
High propensity — prioritise for the warm introduction sequence with a broker follow-up.