Programme overview
Introduction:
Customer analytics and predictive insights training on churn, CLV and uplift modelling is a 5-day course for CRM, marketing analytics, customer data and customer experience analysis staff, ending with a Churn and CLV Model and Retention Action Plan for a case company. Organisations spend retention budgets on customers who would have stayed anyway, while high-value customers leave unnoticed because model scores are never linked to campaign or service actions. Nominees already query customer data and build reports in spreadsheets or notebooks, and the course runs as a modelling build on case data. CoreConcept Training Center delivers this customer analytics course.
Course Objectives:
- Assemble a single customer view and an analytical base table with clear observation and outcome windows for churn labelling
- Score customers with RFM cells and read acquisition cohort retention matrices to locate where value and loyalty decay
- Estimate historic, predictive and probabilistic customer lifetime value and rank customers by expected future margin
- Build and evaluate churn and propensity-to-buy classifiers using logistic regression and tree models with lift, gains and AUC
- Design uplift tests with randomised holdouts to target persuadable customers and measure incremental retention value
- Convert churn probability and CLV scores into prioritised retention treatments and an action plan with fairness and consent checks
Target Audience:
- Staff responsible for analysing CRM and loyalty data to support retention and cross-sell campaigns
- Staff responsible for marketing analytics, campaign selection lists and response measurement
- Staff responsible for preparing customer datasets, queries and predictive scores for business teams
- Staff responsible for customer experience analysis who link complaints, service contacts and cancellations
- Staff responsible for subscription, membership or account base reporting in service businesses
Course Outline:
Day 1: Customer Data Foundations and Behavioural Value Analysis
- Single Customer View Build from Transaction and Interaction Tables
- Analytical Base Table Design with Observation and Outcome Windows
- RFM Scoring with Quintile Bins and Value Cells
- Contractual Versus Non-Contractual Churn Definition and Labelling
- Current Retention Reporting Audit Against Gross and Net Attrition
Day 2: Cohort Retention and Customer Lifetime Value Models
- Acquisition Cohort Retention Matrix and Survival Curve Reading
- Historic Customer Lifetime Value from Margin and Tenure
- Predictive CLV Using Retention Rate and Discount Factor
- Buy Till You Die BG/NBD and Gamma-Gamma Model Concepts
- CLV Decile Ranking and Value Concentration Curve Interpretation
Day 3: Churn Prediction and Propensity Scoring Models
- Churn Feature Engineering from Usage, Billing and Complaint Signals
- Logistic Regression Churn Scorecard with Odds Ratio Interpretation
- Decision Tree and Random Forest Churn Classifier Training
- ROC Curve, AUC, Lift and Cumulative Gains Evaluation
- Propensity-to-Buy and Next-Best-Offer Scoring Matrix Construction
Day 4: Uplift Modelling, Basket and Journey Analytics and Responsible Scoring
- Uplift Modelling with Persuadables, Sure Things and Sleeping Dogs
- Qini Curve and Randomised Holdout Design for Retention Offers
- Market Basket Association Rules with Support, Confidence and Lift
- Customer Journey Path Analysis Before Cancellation Events
- Model Fairness Checks, Consent Scope and Data Minimisation
Day 5: Modelling Build of the Churn and CLV Retention Action Plan
- Case Company Dataset Preparation and Churn Label Validation
- Churn Probability and CLV Matrix for Retention Prioritisation
- Score-to-Action Rules for Campaign and Service Save Treatments
- Incremental Revenue Estimate from Treatment and Control Results
- Churn and CLV Model with Retention Action Plan Completion
Skills You Will Gain:
- Customer Data Preparation
- Behavioural Value Scoring
- Cohort Retention Analysis
- Lifetime Value Estimation
- Churn Classification Modelling
- Model Performance Evaluation
- Uplift Test Design
- Responsible Customer Scoring
Why Attend This Course:
- Deliver a Churn and CLV Model with Retention Action Plan to the CRM or customer value lead for approval of the next retention cycle
- Decide which at-risk customers merit a save offer, a service call or no contact at all, based on expected value and predicted uplift
- Avoid wasted incentives on customers who would stay anyway and cancellations triggered by contacting sleeping dogs
- Share reusable feature definitions, evaluation charts and score-to-action rules with analysts and campaign teams
Conclusion:
Back at work, the participant hands the Churn and CLV Model with Retention Action Plan to the CRM or customer value lead, who uses it to choose which customers receive save offers, service outreach or no contact in the next retention cycle. Campaign and service teams can apply the same score-to-action rules to selection lists and contact queues. After the first cycle, the unit should review lift against the holdout group, how far predicted churn matched actual cancellations, and whether features, thresholds or treatments need recalibrating.
Frequently Asked Questions (FAQ):
What do participants need before a customer analytics and predictive insights course?
Participants should be able to query customer tables, work confidently in spreadsheets and read basic statistics such as averages and percentages. Prior notebook coding helps but is not required. Bringing an anonymised description of their own customer data fields makes the feature and labelling exercises more relevant.
How does customer analytics and predictive insights differ from a CRM strategy course or a general forecasting course?
It builds customer-level models: RFM, cohorts, lifetime value, churn and propensity classifiers and uplift tests. CRM strategy courses focus on programme design and loyalty economics, while general forecasting courses project aggregate volumes over time rather than scoring individual customers.
Why does uplift modelling matter in customer analytics and predictive insights work?
A churn score shows who is likely to leave, not who will stay because of an offer. Uplift modelling compares treated and control customers to find persuadables and avoid sleeping dogs, so retention spend goes where it changes behaviour and incremental value can be measured.
What do participants take back from the customer analytics and predictive insights course?
Participants return with a Churn and CLV Model with Retention Action Plan built on a case company, including the analytical base table, evaluated churn classifier, lifetime value estimates, a probability-by-value matrix, score-to-action rules and a holdout design for measuring incremental retention.