Solutions  /  Customer Decision Intelligence
Customer & Revenue

Know what to do next for every customer

Move beyond dashboards and churn scores. Recommend the next best action that maximizes lifetime value, for every customer, every day.

8h → 2h
Billing & payroll QA cut at a home-health agency (Vitallogix)
+$808K
Lost LTV captured via treatment-effect modeling
73%
Recommended actions accepted by teams
+$214
Average uplift per recommended action

Illustrative figures, except the Vitallogix result, which is from a live deployment.

The problem

Dashboards describe. Scores predict. Neither decides.

!Dashboards show the past, not the next move
!Churn scores with no action attached
!Every customer treated the same
!Discounting without ROI
!Campaigns chosen by gut
!No measurement of what the action achieved
What the system does

Turn every signal into the next best action

Churn prediction

Identify who is at risk before they leave.

LTV prediction

Value each customer to focus investment.

Causal inference

Estimate what actually drives behavior.

Uplift modeling

Target only customers an action will move.

Recommender systems

Rank the right product or offer per customer.

Reinforcement learning

Learn sequential strategies that compound value.

Optimization

Choose actions under budget and constraints.

Experimentation

Measure realized uplift against a holdout.

Decisions supported
Who to retain
What offer to make
Which product to recommend
Which customer to call
How much to discount
Which campaign to prioritize
Which customer-success action to take
Best fit for
Banks
Insurers
Telecoms
Subscription businesses
Enterprise SaaS companies

Customer Decision Intelligence FAQ

How is this different from a churn model?

A churn model tells you who might leave. Customer Decision Intelligence tells you what to do about it: the specific action that maximizes lifetime value, and whether it is worth taking at all.

What makes the recommendations trustworthy?

Actions are grounded in causal and uplift modeling and measured against holdouts, so you act on what actually moves outcomes, not correlations.

Where does it fit in our stack?

It sits on top of your existing data and CRM, scoring customers and writing recommended actions back into the tools your teams already use.

Can it handle budget constraints?

Yes. Optimization chooses the best set of actions within whatever budget, contact, or eligibility constraints you set.

See how customer decision intelligence works

Start with a roadmap that quantifies LTV upside from next-best-action decisioning.

Book an AI Opportunity Call →