DataQuant

CUSTOMER & REVENUE ANALYTICS

Your customers are showing churn signals right now.

Basket decline. Promo dependency. Reduced purchase frequency. The signals were always there. We build AI-powered customer intelligence systems that surface churn risk 60–90 days before revenue disappears.

€21.5M

Revenue protected before churn happened

34K

At-risk customers identified early

11.9×

ROI delivered in Year 1

How silent churn actually happens.

Most enterprises notice churn after the revenue disappears. We detect it while customers are still recoverable.

01

Healthy Customer

Stable frequency, healthy basket size and category engagement.

02

Basket Decline

Average order value starts decreasing across key categories.

03

Promo Dependency

Customer only purchases when aggressive discounts appear.

04

Reduced Frequency

Buying intervals widen and engagement weakens rapidly.

05

Silent Churn

Silent
Churn

Revenue disappears long before traditional reporting catches it.

Revenue analytics built for retention teams.

Predictive systems designed for enterprise customer, retention and commercial teams operating at scale.

Revenue analytics built for retention teams.

Predictive systems designed for enterprise customer, retention and commercial teams operating at scale.

Churn Prediction Models

90-day-ahead churn probability scoring with intervention prioritization and commercial value ranking.

Customer Lifetime Value

Probabilistic CLV models by cohort, channel and product category.

Behavioral Segmentation

AI-driven customer clusters built from purchasing behavior, frequency and promotion sensitivity.

Commercial Pipeline Intelligence

Deal velocity tracking, pipeline health scoring and revenue forecasting systems.

What enterprise customer intelligence actually looks like.

Real-time retention visibility, churn prediction and customer health monitoring built for modern enterprises.

Churn Risk Trend

LAST 90 DAYS

Jan

Feb

Mar

Apr

May

Retention Score

91%

+12%

Retention Growth

34K

Customers Tracked

How much revenue are you losing to silent churn?

Bring 2–3 customer examples. We’ll tell you if our models would have caught them early.

Predict churn before it happens.