Customer Analytics Course

Lesson 1 • Customer Data Landscape

Surveys first-, second-, and third-party data sources and explains how each is collected and used. Grounds learners in the raw material that powers all subsequent analysis.

Lesson 2 • What Customer Analytics Means

Defines customer analytics, distinguishes it from general business intelligence, and maps its role in the customer lifecycle. Establishes shared vocabulary for the entire course.

Lesson 3 • Metrics that Matter to Customers

Introduces foundational customer KPIs such as acquisition rate, retention rate, and satisfaction scores. Connects each metric to a specific business outcome learners will analyse later.

Lesson 4 • Types of Analytical Approaches

Contrasts descriptive, diagnostic, predictive, and prescriptive analytics with real customer examples. Helps learners select the right analytical mode for a given business question.

Lesson 1 • Customer Data Platforms and Storage

Compares data warehouses, data lakes, and customer data platforms for storing unified customer profiles. Learners match storage architecture to organisational scale and use case.

Lesson 2 • Building a Customer Data Dictionary

Guides learners through documenting field definitions, data types, and ownership for all customer attributes. A well-maintained dictionary reduces ambiguity and accelerates analysis.

Lesson 3 • Data Quality and Cleansing

Addresses completeness, accuracy, consistency, and timeliness as the four pillars of data quality. Teaches practical cleansing techniques that prevent downstream analytical errors.

Lesson 4 • Data Collection Methods and Channels

Examines surveys, transactional logs, web tracking, and CRM inputs as primary collection methods. Learners evaluate trade-offs in coverage, cost, and data quality across channels.

Lesson 1 • Cohort and Trend Analysis

Introduces cohort grouping by acquisition period and trend decomposition over time. Learners identify behavioural shifts that inform retention and product strategies.

Lesson 2 • Exploratory Analysis Workflow

Structures a repeatable EDA process from data loading through insight documentation. Learners apply the full workflow to a sample customer dataset as a capstone exercise.

Lesson 3 • Descriptive Statistics for Customer Data

Covers mean, median, mode, variance, and distribution shape as applied to customer metrics. Provides the statistical baseline needed for all subsequent analytical techniques.

Lesson 4 • Data Visualisation Fundamentals

Teaches chart selection, colour use, and layout principles for communicating customer insights visually. Effective visualisation is positioned as a core analytical skill, not just a presentation tool.

Lesson 1 • Clustering Methods for Segmentation

Covers k-means, hierarchical, and density-based clustering algorithms applied to customer feature sets. Learners select and tune algorithms based on data structure and business goals.

Lesson 2 • Validating and Deploying Segments

Applies statistical validation, business review, and A/B testing to confirm segment usefulness before deployment. Learners operationalise segments in CRM and marketing automation systems.

Lesson 3 • Behavioural and Psychographic Segmentation

Extends segmentation beyond demographics to usage patterns, attitudes, and lifestyle indicators. Learners combine behavioural data with survey inputs to build richer segment profiles.

Lesson 4 • RFM Analysis

Teaches recency, frequency, and monetary scoring to rank and group customers by purchase behaviour. RFM is positioned as an accessible entry point before more complex clustering methods.

Lesson 1 • CLV Concepts and Business Impact

Defines CLV, distinguishes historical from predictive CLV, and links it to profitability and resource allocation. Establishes why CLV is the central metric in customer-centric strategy.

Lesson 2 • Simple CLV Calculation Methods

Walks through average revenue, margin-based, and discounted cash flow approaches to CLV calculation. Learners apply each formula to realistic customer datasets and compare results.

Lesson 3 • Machine Learning Approaches to CLV

Applies regression, gradient boosting, and neural network models to predict CLV from customer features. Learners compare ML accuracy against probabilistic baselines and assess trade-offs.

Lesson 4 • Probabilistic CLV Models

Introduces BG/NBD and Pareto/NBD models for predicting future transactions and customer survival. Learners understand model assumptions and fit them to non-contractual purchase data.

Lesson 1 • Designing Retention Interventions

Links churn probability scores to targeted offers, outreach timing, and channel selection for retention campaigns. Learners build a decision tree that routes at-risk customers to the right intervention.

Lesson 2 • Building a Churn Prediction Model

Covers feature selection, class imbalance handling, and model training using logistic regression and tree-based classifiers. Learners produce a scored customer list ranked by churn probability.

Lesson 3 • Measuring Retention Programme Effectiveness

Uses controlled experiments and difference-in-differences analysis to isolate the causal impact of retention programmes. Learners calculate incremental revenue saved and programme ROI.

Lesson 4 • Understanding Customer Churn

Defines voluntary vs. involuntary churn, calculates churn rate, and identifies leading behavioural indicators. Connects churn measurement to the CLV and retention metrics introduced earlier.

Lesson 1 • Data-Driven Attribution Methods

Covers Shapley value, Markov chain, and logistic regression attribution as data-driven alternatives to rules. Learners evaluate when data-driven methods outperform rule-based approaches.

Lesson 2 • Touchpoint Analysis and Influence

Measures the frequency, recency, and conversion influence of each touchpoint across digital and offline channels. Learners rank touchpoints by their contribution to desired customer outcomes.

Lesson 3 • Optimising Spend with Attribution Insights

Translates attribution outputs into budget reallocation recommendations and channel mix optimisation. Learners build a spend optimisation scenario using attribution data from a realistic case.

Lesson 4 • Mapping the Customer Journey

Introduces journey mapping as a data-driven process using event logs, session data, and survey inputs. Learners construct a quantitative journey map that reveals drop-off and acceleration points.

Lesson 1 • Customer Analytics Roadmap Development

Guides learners through prioritising use cases, sequencing investments, and defining milestones for a multi-year analytics roadmap. The completed roadmap serves as the course capstone deliverable.

Lesson 2 • Aligning Analytics with Business Strategy

Connects customer analytics outputs to corporate objectives such as growth, profitability, and market share. Learners translate business questions into analytical problem statements with measurable success criteria.

Lesson 3 • Experimentation and Causal Inference

Covers A/B testing design, sample size calculation, and quasi-experimental methods for causal customer insights. Learners distinguish correlation from causation and design valid business experiments.

Lesson 4 • Building an Analytics Centre of Excellence

Defines the roles, governance, and operating model of a customer analytics centre of excellence. Learners design a team structure that balances centralised expertise with business-unit agility.