The right next purchase, for the right customer, at the right moment.

Matr lets your CRM and marketing teams build, interrogate, and pilot the upsell and cross-sell logic that grows each customer's value, without the blanket pushes that erode trust.

Réserver une démo
LE PROBLÈME AUJOURD'HUI

Every tool promises more revenue per customer. Most just push more.

01

Friction everywhere

Your e-commerce platform bundles products. Your CRM runs an "upgrade" campaign. Your email tool suggests add-ons. They all promise expansion. Most deliver friction.

02

One message for all

The cross-sell bundle is a static rule applied to everyone, whether it fits or not. The upsell campaign blasts "upgrade now" to the entire base, including the people who just downgraded.

03

Pushes that cost you

Blanket pushes don't just underperform, they cost you. Push premium to someone who isn't ready and you train them to ignore you.

LES DONNÉES NÉCESSAIRES
Expansion isn't a silo.

Ce que c'est

Upsell × Churn

Don't push more on a customer about to leave, win them back first

Upsell × LTV

Concentrate expansion effort where future value justifies it

Upsell × Recommendation

The right next purchase and the right product to show run on the same engine

Profil client

Taille, secteur, nombre de sites, si vous l'avez.

test
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

« Quels sont mes comptes à potentiel cette semaine ? »

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.

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

Everyone

Natural language

Speed to answer

Seconds

Score d'opportunité et offre recommandée par client.

Data volume

Unlimited

Opportunités créées dans le CRM, suivi des résultats.

Les questions qu'on nous pose.

  • Faut-il un data scientist ?

    Non. MatR construit et met à jour les scores. Vous posez la question en français.

  • Quelle différence avec les recommandations produits ?

    Les recommandations disent quoi proposer. L'upsell dit d'abord à qui, et quand.

  • Nos commerciaux vont-ils l'utiliser ?

    Les opportunités arrivent dans leur CRM, pas dans un outil de plus.

  • Quel CRM ?

    MatR se branche sur les CRM du marché. On le vérifie pendant le cadrage.