IA pour la Segmentation et l'Analyse Client

Ce qu'elle couvre

Ce programme de niveau praticien apprend aux équipes analytics et marketing à concevoir et déployer des systèmes de segmentation client alimentés par l'IA, de la classification comportementale à la prédiction de la valeur vie client et aux modèles de propension. Les participants travaillent sur des labs pratiques couvrant des pipelines de données réels, l'ingénierie de features et l'évaluation de modèles. Le programme fait le lien entre la data science et l'activation marketing, en s'assurant que les résultats sont directement exploitables dans les outils CRM, médias payants et campagnes lifecycle. La formation combine des sessions en présentiel avec des projets guidés sur des données fournies par les participants ou synthétiques.

À l'issue, vous saurez

• Build and evaluate a behavioural segmentation model using clustering techniques on transactional data
• Train and validate a customer LTV prediction model and interpret its business implications
• Construct a propensity-to-churn or propensity-to-convert model and define actionable score thresholds
• Design a journey analytics framework that maps activation patterns to revenue outcomes
• Export model outputs into a CRM or marketing platform and define segment-specific campaign logic

Sujets abordés

• Behavioural segmentation: RFM, K-means, hierarchical clustering
• Customer lifetime value (LTV) prediction with regression and survival models
• Propensity modelling for conversion, churn, and upsell
• Customer journey analytics and activation pattern identification
• Feature engineering from transactional and event data
• Model evaluation, fairness checks, and business KPI alignment
• Connecting model outputs to CRM and marketing automation tools
• A/B testing and incrementality measurement for segment activation

Modalité

Typically delivered as a 4-week blended programme with two half-day live instructor sessions per week and asynchronous lab work between sessions. Can be compressed into a 5-day on-site bootcamp format. Hands-on ratio is approximately 60% labs to 40% instruction. Participants are encouraged to bring their own anonymised customer datasets; synthetic e-commerce and SaaS datasets are provided as fallback. Tools used include Python (scikit-learn, pandas, lifetimes), SQL, and optionally Databricks or BigQuery.

Ce qui fait que ça marche

• Involving CRM and campaign managers in the programme so model outputs are co-designed with end users
• Establishing a model refresh cadence and ownership before the programme ends
• Linking every model to a specific business decision or campaign trigger, not just an analytical output
• Running a live A/B test during or immediately after the programme to demonstrate incremental impact

Erreurs fréquentes

• Treating segmentation as a one-time project rather than a live, refreshing model integrated into business processes
• Over-relying on RFM alone without incorporating behavioural or predictive signals that drive stronger targeting
• Building models in isolation from marketing and CRM teams, leading to outputs that are never activated
• Ignoring data leakage in propensity models, producing inflated accuracy metrics that fail in production

Fournisseurs à considérer

• DataScientestdatascientest.com →
• Le Wagonwww.lewagon.com →
• Jedha Bootcampwww.jedha.co →
• DataCampwww.datacamp.com →

Sources

• Scikit-learn: Machine Learning in Python — Clustering and Classification documentation →
• Lifetimes library: measuring customer lifetime value in Python →