You know who churned.
Now know who's about to.

Matr gives your team the ability to anticipate churn by asking questions of their own data, getting explained answers, and triggering the right retention actions.

Réserver une démo
LE PROBLÈME AUJOURD'HUI

You can see churn. You'd like to see it coming.

01

You observe

Every month, your team opens the retention dashboard. Churn is calculated, lost customers identified. You comment, analyze, plan actions for next month.

02

You wait

Three weeks later, a churn propensity dashboard arrives. Valuable, but it describes a world that has already moved: the at-risk customers aren't quite the right ones anymore.

03

You're late

The problem isn't the absence of prediction. It's the absence of prediction at the right moment, on your timeline, directly accessible to your team.

LES DONNÉES NÉCESSAIRES
Churn isn't a silo.

Ce que c'est

Churn × LTV

Focus retention on high-value customers, not just all at-risk customers

Churn × Segmentation

Identify the behavioral segments most exposed to churn, tailor the message

Churn × Recommendation

Suggest the right reactivation product to each at-risk customer

Votre définition du départ

Un client sans commande depuis combien de temps ? Un contrat non renouvelé ? MatR la fixe avec vous pendant le cadrage.

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 clients sont passés en risque élevé 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.

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

case study

Blissim cut churn by 20%

without hiring a data scientist

The Challenge

Blissim was losing €200K/month to churn. No data scientist on staff. No time to build a model from scratch.

The Solution

Matr deployed a churn prediction model on BigQuery in one day. The model scores every customer, identifies high-risk profiles, and feeds retention campaigns.

The Impact

  • +20% customer retention
  • +40K/month recovered revenue
  • Model deployed and in production
Lire le cas client complet
VOS BRIQUES MATR
Why this isn't just
another query tool.
Who can use it

Everyone

Natural language

Speed to answer

Seconds

Score de risque de départ, avec ses raisons.

Data volume

Unlimited

Envoi vers le CRM, tâches, alertes Slack ou Teams.

Les questions qu'on nous pose.

  • Faut-il un data scientist ?

    Non. MatR construit et met à jour le modèle. Vous posez la question en français.

  • Et si nous n'avons pas d'abonnement ?

    Le churn se définit aussi sur des commandes : un client qui ne commande plus. MatR fixe la définition avec vous.

  • Pourquoi tel client est-il à risque ?

    Chaque score est expliqué par les signaux qui l'ont fait monter.

  • Combien de temps pour avoir les premiers scores ?

    Quelques semaines après le cadrage, selon vos sources.