An ML system that identifies which customers are about to leave, weeks before they do. Your team gets a prioritised list and a reason for every name on it.
Most teams discover a customer has lapsed after the decision is already made. The retention window is gone before it ever opened.
Blanket discounts and mass outreach land on the wrong people. You end up rewarding customers who were never going to leave and missing the ones who were.
Without specific signals, retention teams operate on gut feel. They cannot personalise outreach because they do not know what drove the decision.
No new infrastructure. No real-time feeds. It works from whatever data you already have.
We connect to your existing data exports. Policy records, CRM data, transaction history. Nothing new needs to be built.
Hundreds of behavioural, financial, and engagement signals are computed automatically. The signals that matter get surfaced.
Every customer gets a churn probability. The full book is ranked from highest to lowest risk, automatically.
Your team receives a prioritised list with clear reasons why each customer is flagged, weeks before they reach a decision point.
The model surfaced at-risk policyholders more than 60 days before renewal. Our retention team shifted from reactive calling to precision-led outreach. The lift was immediate and measurable.
Built for insurance. Calibrated for any industry where customer data exists and retention matters.
Motor, health, commercial vehicle, life, crop, and insurtech. Every active policy scored at renewal using signals from your own data.
Predict account closure, credit product lapse, and digital disengagement before balance transfers begin.
Score dormant buyers and subscription drop-offs. Intervene with precision before they buy from someone else.
Identify disengaging members and lapsing plan holders. Get ahead of care discontinuation with targeted outreach.
Flag disengaging learners weeks before non-renewal. Re-engage at scale while there is still time to act.
If you have customer data and a retention problem, the model can be calibrated for your context. Come as you are.
Eigennexus is an independent ML consulting firm. Rehan Qureshi founded it after spending years building production-grade retention models across India and the US, scoring more than ten million policies in the process.
The work here is not theoretical. Every model and pipeline has been built to operate at scale, under real business constraints, with real accountability for the numbers. The flagship engagement is retention intelligence because that is where the evidence is clearest and the return comes fastest.
The firm covers the full ML lifecycle: data engineering, predictive modelling, deep learning, and business intelligence. The engagement model is simple. You share your data, we build on it, and you see results before committing to anything further.
Every engagement starts with a 30-day no-cost pilot on a sample of your data. You see the model performance before you commit to anything.
Start with a conversation. We will ask about your current retention process, work out where the model fits, and offer a 30-day pilot on a sample of your data. No infrastructure changes needed.