Not "you might also like".
The right product, and the reason why.

Matr lets your CRM and marketing teams build, interrogate, and pilot the recommendation logic that powers your campaigns and your customer experience.

Réserver une démo
LE PROBLÈME AUJOURD'HUI

Recommendations are everywhere. Yet most are blind.

01

Noise everywhere

Your e-commerce platform has a recommendation widget. Your CRM has personalized blocks. Your email tool suggests products. They all promise relevance. Most deliver noise.

02

Rules in disguise

The widget shows last week's top sellers, dressed up as personalization. The "you might also like" block runs on a rule from 2022 nobody remembers.

03

A black box

When a recommendation works, you can't reproduce it. When one fails, you can't fix it. The logic is buried in a tool nobody can interrogate.

LES DONNÉES NÉCESSAIRES
Recommendation isn't a silo.

Ce que c'est

Recommendation × Churn

Suggest the right reactivation product to each at-risk customer

Recommendation × LTV

Adapt recommendation strategy to customer value, not just behavior

Recommendation × Segmentation

Build distinct recommendation logics per behavioral segment

Vos règles

Produits à pousser, à exclure, marges minimum. MatR les intègre pendant le cadrage.

Recommandations in minutes
Ask a question. Get a model in production.
01
Describe your goal
02
Train automatically
03
Deploy in one click
04
Automate & monitor
Tell Matr what you want to predict,in plain language.
Matr identifies the right datasets and starts working.
Every step is visible and explainable. No black box.
Matr selects features, tests algorithms, and trains the model.
The model goes live, no setup.
Predictions refresh with your data, via dashboard, API, or direct integration.
Set it once. No MLOps team required.
Matr monitors performance and flags when retraining is needed.
The ML runs in production.
What a day with Matr actually looks like for a CRM team.

Detection

« Trois produits par client pour la campagne du mois. »

You get the list, ranked by probability, with the signals that drove each prediction (drop in usage frequency, unopened recent emails, unsubscribe page visits).

Understanding

"Why is the Premium segment churning faster this quarter?"

Matr analyzes, cross-references variables, returns the main factors. Not a spreadsheet. An explanation, structured, actionable.

Action

"Which at-risk customers are most likely to respond to a 15% retention offer?"

You get the sub-segment to activate first, with projected response rate. The campaign builds itself from there.

Measurement

"What was the impact of last month's campaign on actual churn for the targeted customers?"

The model returns the gap between predicted churn and observed churn, with interpretation. You know what worked. You iterate.

No more translating what you want into technical language. Your team asks questions the way they'd phrase them in a meeting.

VOS BRIQUES MATR
Why this isn't just
another query tool.
Who can use it

Everyone

Natural language

Speed to answer

Seconds

Recommandations par client.

Data volume

Unlimited

Envoi vers l'emailing, le site ou le CRM.

Les questions qu'on nous pose.

  • Faut-il beaucoup de données ?

    Il faut un historique d'achat par client. MatR évalue ce qui est possible pendant le cadrage.

  • Peut-on imposer des règles ?

    Oui. Stock, marge, produits à pousser ou à exclure.

  • Ça marche en B2B ?

    Oui. Les recommandations vont alors surtout aux commerciaux, dans le CRM.

  • Et pour un nouveau client ?

    MatR s'appuie sur les clients qui lui ressemblent et sur les produits les plus achetés.