Customer Segmentation and Churn Analysis Training Course

Participants can specify and prepare customer-level data suitable for segmentation and churn analysis.

• Customer-level analytical files assembled from CRM, transaction and interaction sources
• Data-quality checks identify duplicates, missing values, outliers and stale records
• Observation, performance and prediction windows defined for retention analysis
• Features engineered from recency, frequency, monetary value and engagement signals
• Time leakage prevented through strict separation of historical and future information
• Data dictionaries document variable definitions, sources, transformations and ownership

Participants can create transparent rule-based segments for customer targeting and relationship management.

• RFM scoring ranks customers by recency, frequency and monetary contribution
• Quintile cut-offs converted into practical audience definitions and contact rules
• Customer lifetime value estimates combine margin, retention probability and acquisition cost
• Value tiers distinguish strategic accounts, growth prospects, core customers and low-value customers
• Lifecycle segmentation separates new, active, declining, dormant and reactivated customers
• Segment migration analysis tracks movement between value and engagement categories

Participants can select variables that represent customer needs, behaviours and commercial potential.

• Segmentation hypotheses connect customer needs to propositions, channels and service design
• Jobs-to-be-Done interviews identify functional, emotional and social purchase motivations
• Behavioural variables capture product adoption, channel preference, usage depth and service demand
• Firmographic criteria classify B2B accounts by industry, size, geography and buying structure
• Attitudinal survey measures converted into usable segmentation variables and scales
• Variable selection balances business relevance, data availability and discriminatory power

Participants can interpret cluster-based segments and judge whether they are commercially usable.

• K-means clustering groups customers using standardised numerical behavioural variables
• Hierarchical clustering compares customer similarity through dendrograms and distance measures
• Choosing cluster numbers uses elbow plots, silhouette scores and business interpretability
• Standardisation prevents high-range variables dominating Euclidean distance calculations
• Cluster profiles compare means, proportions, value measures and behavioural characteristics
• Unstable clusters identified through resampling, time comparisons and operational review

Participants can translate segment outputs into prioritised customer strategies and channel plans.

• Segment scorecards combine size, revenue, margin, growth, risk and strategic fit
• Personas grounded in evidence distinguish useful profiles from unsupported stereotypes
• Targeting priorities set through attractiveness, accessibility and competitive-position criteria
• Differentiated offers match segment needs, willingness to pay and service expectations
• Channel preferences inform contact frequency, message format and response pathways
• Treatment matrices assign segment-specific acquisition, growth, service and retention actions

Participants can define churn consistently and diagnose when, where and among whom it occurs.

• Contractual and non-contractual churn definitions matched to business model realities
• Voluntary, involuntary and partial churn separated for accurate intervention decisions
• Cohort retention tables reveal customer survival patterns by acquisition period
• Churn rates calculated using clear population, period and denominator conventions
• Funnel analysis locates abandonment across onboarding, usage, renewal and support stages
• Driver analysis compares churn patterns by segment, channel, product and tenure

Participants can select, interpret and communicate suitable churn-prediction models for business use.

• Logistic regression estimates churn probability from customer attributes and behaviours
• Decision trees expose understandable churn rules and interaction effects
• Survival analysis estimates time-to-churn and changing retention probabilities
• Training and test samples protect estimates from overfitting and false confidence
• Class imbalance addressed through weighting, sampling and appropriate evaluation metrics
• Feature importance distinguishes influential predictors from operationally useful intervention signals

Participants can evaluate churn models and prioritise treatment using risk, value and cost.

• Confusion matrices distinguish true positives, false positives and missed churners
• ROC curves and AUC compare discrimination across alternative predictive models
• Lift charts identify high-risk groups worth prioritising for limited contact capacity
• Calibration plots test whether predicted probabilities match observed churn rates
• Expected value calculations combine churn risk, margin, save rate and offer cost
• Contact thresholds set from capacity constraints, treatment economics and customer experience

Participants can design controlled retention actions, governance controls and an implementation plan.

• Retention treatments designed around identified drivers, customer context and value
• Randomised control groups measure incremental impact beyond natural customer retention
• A/B test design specifies hypotheses, success metrics, sample sizes and duration
• Uplift logic distinguishes persuadable customers from inevitable churners and loyalists
• GDPR principles guide lawful purpose, data minimisation, transparency and retention periods
• Application plans assign owners, milestones, data needs, decisions and review measures