AI TRAINING
AI for Customer Segmentation and Analytics
Build predictive segmentation models that drive measurable revenue through smarter customer targeting and activation.
What it covers
This practitioner-level programme teaches analytics and marketing teams to design and deploy AI-powered customer segmentation systems, from behavioural clustering to LTV prediction and propensity modelling. Participants work through hands-on labs covering real customer data pipelines, feature engineering, and model evaluation. The programme bridges the gap between data science and marketing activation, ensuring outputs are directly usable in CRM, paid media, and lifecycle campaigns. Delivery combines instructor-led sessions with guided project work on participant-supplied or synthetic datasets.
What you'll be able to do
- 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
Topics covered
- 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
Delivery
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.
What makes it work
- 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
Common mistakes
- 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
When NOT to take this
This programme is not the right fit for teams that have not yet consolidated their customer data into a single source of truth — if customer IDs are inconsistent across systems and event data is unreliable, foundational data engineering work must precede any modelling effort.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.