Retail
Treat every customer as a segment of one
Score every customer individually, not by segment, and act on loyalty, basket and churn signals as they happen.
Fast by design
Full network data reconciled in hours, not days.
Data stays put
On-premise or private cloud. Transaction and customer data never leave your infrastructure.
Built for scale
Processes billions of records a day on a standard server.
Our approach
Built for what retail actually needs.
Anatella | Data prep engine
Connect to any sources
Point-of-sale, loyalty, e-commerce and supplier data, from any system, reconciled into one view.
Processes at store-and-SKU scale
Hundreds of thousands of points of sale and SKUs modeled individually, not averaged into a national figure.
One pipeline, one server
No cluster, no data lake project. A single server handles ingestion through scoring.
Modeler | Predictive Engine
Automatic variable selection
One model per store, per neighborhood or per customer, generated and scored in parallel.
Algorithms competing mathematically
Several models benchmarked against each other, the best retained on statistical merit.
Built-in Drift Report
Flags a forecast or churn model losing accuracy as seasons or buying patterns shift.
SOLUTIONS
What that looks like in production.
+10% store visits from personalized coupons
Delhaize replaced generic campaigns with coupons matched to each shopper’s own purchase history.
A shopping cart that fills itself in one click
Carrefour’s Magic List builds the basket from each customer’s own buying habits and stock levels.
92% forecast accuracy across 300,000 stores
Bavaria runs 30,000 neighborhood models instead of one average for the whole network.
1,013 variables flag customers before they leave
Delhaize spots disengagement across 37 product categories, early enough to act.
Problems solved
What retailers fix with TIMi.
One model, thousands of stores
A single national forecast misses what’s happening on each shelf, in each neighborhood, every day.
Offers that feel generic
Discounts and promotions built for a segment rarely fit the person standing at the register.
One customer, several profiles
Loyalty, point-of-sale and e-commerce data sit in separate systems, never reconciled into one view.
Assortment decided on gut feel
By the time a trend is confirmed in a report, the shelf space or inventory call has already been made.
FAQ
Common questions.
What is TIMi used for in telecom?
Telecom operators use TIMi to certify network and revenue data, detect fraud before it’s booked, model subscriber churn, and support real-time sales. Deployments include Snype (national network data certification), MTN (churn analysis across 21.4 million subscribers) and VOO (fraud detection and inbound sales).
Can TIMi run on-premise for a telecom operator?
Yes. TIMi runs on-premise or in a private cloud, on standard server hardware, with no cluster required. Subscriber, billing and network data stay on the operator’s own infrastructure.
How fast can TIMi process national-scale telecom data?
For Snype, TIMi processes 150 billion monthly transactions across 7 operators and over 200 data sources into certified traffic, revenue and fraud figures in 5 to 7 hours.
How much does TIMi improve churn detection accuracy?
For MTN, TIMi raised churn detection accuracy from 45% to 57%, analyzing 12.1 billion records across 21.4 million subscribers in under 5 days, using Social Network Analysis and 657 variables.
Can TIMi detect fraud before it affects revenue?
Yes. For VOO, TIMi flags suspicious subscriber behavior from the first month after activation, cutting recovery costs by 50% and preserving revenue on 36% of at-risk accounts.
Does TIMi integrate with existing billing, CRM or network systems?
Yes. TIMi connects natively to existing billing, CRM and network systems, and feeds predictions into them directly, without requiring a migration.
SEE FOR YOURSELF
Don’t take our word for it.
Run TIMi on your own data with a free trial or request a live demo.
