67 Appendix C — Analytical Method Coverage Map

68 Appendix C — Analytical Method Coverage Map

This appendix provides a comprehensive chapter-by-chapter reference of all 56 chapters, including the key methods, datasets, and cross-references in each. Use this as a quick lookup guide when searching for a specific analytical technique or Nigerian/African data application.

68.1 Complete Chapter Coverage Table

Chapter	Title	Part	Key Methods Taught	Key Nigerian/African Dataset	Cross-References
1	Introduction to Business Analytics in Africa	I	Overview, business drivers, analytics maturity model	Mobile money transactions	Ch. 2
2	Fundamentals: KPIs, Metrics, and Dashboarding	I	KPI design, business metrics frameworks, dashboard design, monitoring systems	Household expenditure survey	Ch. 3, 13
3	Customer and Business Value: Profitability, Margins, CLV	I	Customer profitability, activity-based costing, customer lifetime value, margin analysis	Bank loan portfolio, retail network	Ch. 34, 36
4	Data Collection, Integration, and Pipeline Architecture	I	Data engineering, ETL, data quality, metadata, data lakes, real-time pipelines	Mobile money transactions, call centre logs	Appendix B
5	Statistical Foundations: Hypothesis Testing, Significance, Power	I	t-tests, chi-squared, ANOVA, Kruskal-Wallis, statistical power, sample size, type I/II error	Bank loan portfolio, household survey	Ch. 6, Appendix D
6	Machine Learning Fundamentals: Algorithms, Evaluation, Validation	I	Supervised/unsupervised learning, train-test split, cross-validation, confusion matrix, AUC-ROC, F1 score, precision-recall	Bank loan portfolio, customer survey	Ch. 7, 27-29
7	Advanced Machine Learning: Ensembles, Neural Networks, Regularisation	I	Decision trees, random forests, gradient boosting (XGBoost, LightGBM), neural networks, dropout, L1/L2 regularisation	Bank loan, insurance claims	Ch. 28, 29, 44
8	Probabilistic Methods: Bayesian Inference, Simulation, and Optimisation	I	Bayes’ theorem, prior/posterior, Monte Carlo simulation, linear programming, integer programming, sensitivity analysis	Energy consumption, pricing data	Ch. 25, 26
9	Time Series Analysis: ARIMA, Seasonality, Trend, and Forecasting	I	Stationarity, ACF/PACF, ARIMA(p,d,q), SARIMA, exponential smoothing, trend decomposition	Agricultural commodities, mobile money, energy	Ch. 31, 32, 37
10	Natural Language Processing and Image Analytics	I	Tokenisation, stemming, TF-IDF, LDA topic modelling, word embeddings, word2vec, CNNs, image classification	Customer reviews, call centre data	Ch. 19, 20, 35, 39
11	Network Analysis and Geospatial Analytics	I	Graph theory, centrality measures, community detection, shortest path, location analysis, clustering, Voronoi diagrams	Retail store network, agricultural supply chains	Ch. 22, 40, 44
12	Managing Analytics Projects: Agile, Stakeholder Buy-in, Governance	II	Project management, stakeholder management, risk assessment, team structures, communication strategies	Case studies from Ch. 1, 14	Ch. 13-15
13	Building Analytics Capability: Organisational Structures, Talent	II	Centre of excellence, analytics functions, hiring, training, knowledge management, communities of practice	Employee records	Ch. 46, Appendix B
14	A/B Testing Design: Randomisation, Causal Inference, Experimental Design	II	Randomisation, blocking, matching, propensity score, causal graphs, CATE (Conditional Average Treatment Effect)	Mobile money (hypothetical experimentation)	Ch. 15, 37
15	Running and Analysing A/B Tests: Power Analysis, Sequential Testing	II	Sample size calculation, minimum detectable effect (MDE), sequential testing, multi-arm testing, Bayesian AB testing	Simulated ecommerce experiments	Ch. 14, 33
16	Exploratory Data Analysis and Correlation Techniques	II	Univariate/bivariate/multivariate analysis, correlation (Pearson, Spearman, Kendall), confounding, Simpson’s paradox	Bank loan portfolio, household survey	Ch. 5, 6, 17
17	Market Basket Analysis and Association Rules	II	Apriori algorithm, Eclat, support/confidence/lift, market basket, cross-selling, frequent itemsets	Ecommerce clickstream	Ch. 21, 43
18	Dimensionality Reduction: PCA, Factor Analysis, Feature Engineering	II	Principal component analysis, variance explained, feature selection, LASSO, Elastic Net, VIF	Bank loan, agricultural data	Ch. 6, 27
19	Text Mining and Sentiment Analysis	II	Bag-of-words, TF-IDF, LDA, sentiment classification, emotion detection, opinion mining	Customer reviews, call centre transcripts	Ch. 10, 35, 39
20	Computer Vision and Image Analytics	II	Image preprocessing, CNNs, transfer learning, object detection, quality control applications	Call centre visual data, manufacturing defects	Ch. 10, 41
21	Building Recommendation Systems	II	Content-based filtering, collaborative filtering, matrix factorisation, neural collaborative filtering, cold-start problem	Ecommerce clickstream	Ch. 17, 43
22	Social Network Analytics and Influencer Identification	II	Network centrality, PageRank, community detection, homophily, tie strength, viral coefficient	Retail network, social media data	Ch. 11, 35, 39
23	Classification and Logistic Regression	II	Logistic regression, odds ratios, model interpretation, probability threshold, decision boundaries	Bank loan default, insurance claims	Ch. 6, 27, 29
24	Regression Techniques: Linear, Multiple, and Nonlinear	II	Linear regression, multiple regression, interaction terms, polynomial regression, weighted regression	Bank loan, sales performance	Ch. 6, 33, 37
25	Optimisation Techniques: Linear and Integer Programming	II	Objective functions, constraints, branch-and-bound, sensitivity analysis, knapsack problem, travelling salesman	Inventory, pricing, allocation	Ch. 32, 42, 44
26	Monte Carlo Simulation and Risk Quantification	II	Sampling, probability distributions, value-at-risk (VaR), scenario analysis, tornado diagrams	Financial portfolios, pricing scenarios	Ch. 8, 48
27	Lead Scoring and Sales Qualification	III	Propensity modelling, explicit scoring, implicit scoring, rank ordering, prioritisation matrices	Sales performance data	Ch. 6, 33
28	Clustering and Segmentation	III	K-means, hierarchical clustering, DBSCAN, Gaussian mixture models, silhouette analysis, optimal k selection	Bank loan, customer records, all customer data	Ch. 6, 34, 43
29	Classification for Churn, Fraud, and Propensity Prediction	III	Decision trees, random forests, gradient boosting, cost-sensitive learning, imbalanced classes, SHAP values	Insurance claims, employee data, churn data	Ch. 7, 23, 36, 41
30	Survival Analysis and Hazard Modelling	III	Kaplan-Meier curves, Cox proportional hazards, censoring, time-to-event, retention curves, RFM	Customer lifetime value, employee tenure	Ch. 3, 34, 47
31	Demand Forecasting: Time Series and Causal Methods	III	ARIMA/SARIMA, Prophet, exogenous variables, cross-validation for time series, accuracy metrics (MAE, RMSE)	Agricultural commodities, inventory	Ch. 9, 45
32	Inventory Analytics and Optimisation	III	Reorder points, safety stock, ABC analysis, economic order quantity (EOQ), demand-supply balancing	Warehouse inventory, energy consumption	Ch. 25, 45
33	Sales Forecasting and Pipeline Analytics	III	Sales funnel, pipeline stage progression, quota allocation, rep-level forecasting, win-loss analysis	Sales performance data	Ch. 27, 37
34	Customer Profitability and Lifetime Value	III	Segment profitability, contribution margin, customer acquisition cost (CAC), customer lifetime value (CLV), payback period	Bank loan, household survey, customer records	Ch. 3, 30, 36, 47
35	Customer Satisfaction and Experience Analytics	III	NPS (Net Promoter Score), CSAT, CES, satisfaction drivers, gap analysis, journey mapping	Guest reviews, survey data, call centre	Ch. 2, 36
36	Customer Churn Prediction and Retention Analytics	III	Survival analysis, propensity models, retention curves, cohort analysis, intervention targeting, churn drivers	Insurance claims, employee records, customer lifetime	Ch. 29, 30, 47
37	Marketing Mix Modelling (MMM) and Attribution	IV	Adstock, diminishing returns, media elasticity, contribution analysis, multi-touch attribution	Digital ad spend, sales data	Ch. 9, 24, 33
38	Web Analytics and Funnel Optimisation	IV	User journey, funnel analysis, conversion rate optimisation (CRO), attribution models, cohort analysis	Ecommerce clickstream	Ch. 4, 22
39	Social Media and Text Analytics	IV	Engagement metrics, sentiment tracking, influencer analysis, viral potential, topic modelling	Customer surveys, social media data, reviews	Ch. 10, 19, 22
40	Geospatial Analytics and Location Optimisation	IV	Store location analysis, spatial clustering, trade area analysis, drive-time analysis, cannibalisation	Retail network, delivery routes	Ch. 11, 44
41	Quality Analytics and Six Sigma	IV	Statistical process control (SPC), control charts (p-chart, x-bar-R), process capability (Cp, Cpk), DMAIC, root cause analysis	Manufacturing defects	Ch. 5, 8
42	Pricing Analytics and Optimisation	IV	Price elasticity, willingness-to-pay, value-based pricing, dynamic pricing, price discrimination, revenue optimisation	Bank loan interest, hotel pricing	Ch. 3, 8, 25
43	Product Recommendation and Cross-Selling	IV	Collaborative filtering, content-based filtering, market basket, sequential patterns, uplift modelling	Ecommerce, retail network	Ch. 17, 21, 28
44	Supply Chain Analytics and Optimisation	IV	Demand planning, inventory optimisation, routing optimisation, supplier performance, risk quantification	Warehouse inventory, routes, energy	Ch. 25, 31, 32, 45
45	Inventory Planning and Demand-Supply Optimisation	IV	Demand planning, safety stock, service level, seasonal adjustments, promotion lift forecasting	Warehouse inventory, agricultural data	Ch. 9, 31, 32, 44
46	Employee Analytics and Performance Management	IV	Performance scoring, tenure analytics, skill mapping, compensation benchmarking, development tracking	Employee records	Ch. 2, 47
47	Workforce Attrition Prediction and Retention	IV	Survival models, propensity scoring, retention drivers, exit risk scoring, intervention effectiveness	Employee records, HR systems	Ch. 30, 36, 46
48	Financial Risk Analytics: Credit, Market, Operational	IV	Credit risk models, probability of default (PD), loss given default (LGD), value-at-risk (VaR), stress testing, correlation	Credit bureau, stock returns, insurance	Ch. 8, 26, 29
49	Financial Performance and Cash Flow Analytics	IV	Cash flow forecasting, working capital, liquidity analysis, variance analysis, budget forecasting	Financial data, energy consumption	Ch. 3, 24
50	Strategic Analytics and Executive Dashboards	IV	KPI strategy, balanced scorecard, strategic planning analytics, scenario analysis, drill-down dashboards	All datasets aggregated	Ch. 2, 13
51	Ethics, Fairness, and Responsible AI in Analytics	IV	Bias detection, fairness metrics, algorithmic transparency, SHAP/LIME, explainability, regulatory compliance	Case studies across all applications	All chapters
52	Advanced Topics: Causal Inference and Policy Evaluation	IV	Causal DAGs, instrumental variables, regression discontinuity, difference-in-differences, policy impact	A/B testing data	Ch. 14, 15
53	Real-Time Analytics and Streaming Data	IV	Stream processing, online learning, real-time dashboards, alerting systems, lambda/kappa architectures	Mobile money, energy consumption	Ch. 4, 50
54	Deploying Analytics: Models, APIs, Monitoring	IV	Model serving, REST APIs, containerisation (Docker), model versioning, monitoring, retraining schedules	All predictive models	Ch. 6, 7
55	Case Study 1: E-commerce Revenue Optimisation	IV	End-to-end case study combining demand forecasting, pricing, recommendations, A/B testing	Ecommerce clickstream, aggregated sales	Multiple chapters
56	Case Study 2: Customer Value Maximisation in Banking	IV	End-to-end case study combining segmentation, CLV, churn prediction, cross-sell recommendations	Bank loan portfolio, customer survey	Multiple chapters

68.2 Quick Lookup: Find a Topic

68.2.1 By Analytical Technique

A/B Testing & Experimentation: Ch. 14, 15, 52 Anomaly Detection: Ch. 29, 41, 53 Churn Prediction: Ch. 29, 30, 36, 47 Classification: Ch. 6, 23, 27, 29 Clustering & Segmentation: Ch. 6, 28, 40 Demand Forecasting: Ch. 9, 31, 45 Deep Learning: Ch. 7, 20, 54 Ensemble Methods: Ch. 7, 29, 44 Forecasting & Time Series: Ch. 9, 31, 37, 45 Fraud Detection: Ch. 29, 48 Inventory Optimisation: Ch. 25, 32, 44, 45 Marketing Attribution: Ch. 37, 38, 39 Natural Language Processing: Ch. 10, 19, 35, 39 Network Analysis: Ch. 11, 22, 39, 44 Optimisation: Ch. 8, 25, 40, 42, 44, 45 Pricing Analytics: Ch. 3, 8, 42 Recommendation Systems: Ch. 17, 21, 43 Regression Analysis: Ch. 5, 6, 24, 31, 37 Risk Analytics: Ch. 8, 26, 29, 48, 49 Survival Analysis: Ch. 30, 36, 47 Text Analytics & Sentiment: Ch. 10, 19, 35, 39

68.2.2 By Business Domain

Banking & Credit Risk: Ch. 3, 5, 6, 23, 27, 29, 48, 56 Customer Analytics: Ch. 2, 3, 28, 30, 34, 35, 36, 38, 43, 47, 55, 56 E-commerce & Retail: Ch. 17, 21, 22, 38, 40, 43, 55 Employee Analytics: Ch. 13, 30, 46, 47 Energy & Utilities: Ch. 9, 25, 31, 32, 45 Financial Management: Ch. 3, 49, 50 Insurance: Ch. 29, 41, 48 Manufacturing & Quality: Ch. 8, 25, 41, 44 Marketing & Digital: Ch. 2, 14, 15, 37, 38, 39, 42, 43 Supply Chain & Logistics: Ch. 8, 11, 25, 31, 32, 44, 45

68.2.3 By Nigerian/African Data Used

Bank Loan Portfolio: Ch. 3, 5, 6, 16, 18, 23, 24, 56 Call Centre Logs: Ch. 10, 20, 39, 46 Ecommerce Clickstream: Ch. 17, 20, 38, 43, 55 Employee Records: Ch. 13, 46, 47 Energy Consumption: Ch. 9, 25, 31, 32, 44, 45 Guest Reviews: Ch. 19, 35, 36 Household Survey: Ch. 2, 3, 5, 16, 24 Insurance Claims: Ch. 29, 36, 41, 48 Manufacturing Defects: Ch. 41 Mobile Money Transactions: Ch. 1, 4, 9, 14, 15, 22 Retail Network: Ch. 3, 11, 22, 40, 43 Sales Performance: Ch. 27, 33, 37, 46 Warehouse Inventory: Ch. 25, 31, 32, 44, 45

68.3 Learning Paths

68.3.1 Path 1: From Foundations to Predictive Analytics (Complete Curriculum)

Ch. 1-11 (Foundations)
Ch. 12-25 (Advanced Methods)
Ch. 26-29 (Predictive Analytics)

Total: 29 chapters, ~600 pages, 8-12 weeks

68.3.2 Path 2: Customer-Centric Analytics (Focus)

Ch. 2, 3, 28, 30, 34, 35, 36, 43, 46, 47, 50, 56

Total: 12 chapters, ~250 pages, 3-4 weeks

68.3.3 Path 3: Marketing & Revenue Analytics (Focus)

Ch. 2, 14, 15, 21, 33, 37, 38, 39, 42, 43, 55

Total: 11 chapters, ~220 pages, 3-4 weeks

68.3.4 Path 4: Operations & Supply Chain (Focus)

Ch. 4, 5, 8, 9, 11, 25, 31, 32, 40, 41, 44, 45

Total: 12 chapters, ~240 pages, 3-4 weeks

68.3.5 Path 5: Risk & Finance (Focus)

Ch. 3, 8, 26, 29, 30, 48, 49, 52

Total: 8 chapters, ~160 pages, 2-3 weeks

68.4 Chapter Dependencies

Some chapters build on others. A recommended reading order within each part:

Part I (Foundations): 1 → 2 → 3 → 4 → (5, 6 in parallel) → 7 → 8 → 9 → 10 → 11

Part II (Advanced): 12 → 13 → (14 & 15 in parallel) → (16-25 mostly independent, but 18 builds on 16)

Part III (Predictive): 26 → (27-30 independent) → 31 → 32 → 33 → (34-36 independent)

Part IV (Applications): 37 onwards largely independent; 40-45 benefit from Ch. 25 and Ch. 32; 46-47 benefit from Ch. 30; 55-56 are capstone integrations.

This coverage map is current as of the 2024 edition. Check the book’s website for updates and errata.

--- title: "Appendix C — Analytical Method Coverage Map" --- # Appendix C — Analytical Method Coverage Map This appendix provides a comprehensive chapter-by-chapter reference of all 56 chapters, including the key methods, datasets, and cross-references in each. Use this as a quick lookup guide when searching for a specific analytical technique or Nigerian/African data application. ## Complete Chapter Coverage Table | Chapter | Title | Part | Key Methods Taught | Key Nigerian/African Dataset | Cross-References | |---------|-------|------|-------------------|-------------------------------|-------------------| | 1 | Introduction to Business Analytics in Africa | I | Overview, business drivers, analytics maturity model | Mobile money transactions | Ch. 2 | | 2 | Fundamentals: KPIs, Metrics, and Dashboarding | I | KPI design, business metrics frameworks, dashboard design, monitoring systems | Household expenditure survey | Ch. 3, 13 | | 3 | Customer and Business Value: Profitability, Margins, CLV | I | Customer profitability, activity-based costing, customer lifetime value, margin analysis | Bank loan portfolio, retail network | Ch. 34, 36 | | 4 | Data Collection, Integration, and Pipeline Architecture | I | Data engineering, ETL, data quality, metadata, data lakes, real-time pipelines | Mobile money transactions, call centre logs | Appendix B | | 5 | Statistical Foundations: Hypothesis Testing, Significance, Power | I | t-tests, chi-squared, ANOVA, Kruskal-Wallis, statistical power, sample size, type I/II error | Bank loan portfolio, household survey | Ch. 6, Appendix D | | 6 | Machine Learning Fundamentals: Algorithms, Evaluation, Validation | I | Supervised/unsupervised learning, train-test split, cross-validation, confusion matrix, AUC-ROC, F1 score, precision-recall | Bank loan portfolio, customer survey | Ch. 7, 27-29 | | 7 | Advanced Machine Learning: Ensembles, Neural Networks, Regularisation | I | Decision trees, random forests, gradient boosting (XGBoost, LightGBM), neural networks, dropout, L1/L2 regularisation | Bank loan, insurance claims | Ch. 28, 29, 44 | | 8 | Probabilistic Methods: Bayesian Inference, Simulation, and Optimisation | I | Bayes' theorem, prior/posterior, Monte Carlo simulation, linear programming, integer programming, sensitivity analysis | Energy consumption, pricing data | Ch. 25, 26 | | 9 | Time Series Analysis: ARIMA, Seasonality, Trend, and Forecasting | I | Stationarity, ACF/PACF, ARIMA(p,d,q), SARIMA, exponential smoothing, trend decomposition | Agricultural commodities, mobile money, energy | Ch. 31, 32, 37 | | 10 | Natural Language Processing and Image Analytics | I | Tokenisation, stemming, TF-IDF, LDA topic modelling, word embeddings, word2vec, CNNs, image classification | Customer reviews, call centre data | Ch. 19, 20, 35, 39 | | 11 | Network Analysis and Geospatial Analytics | I | Graph theory, centrality measures, community detection, shortest path, location analysis, clustering, Voronoi diagrams | Retail store network, agricultural supply chains | Ch. 22, 40, 44 | | 12 | Managing Analytics Projects: Agile, Stakeholder Buy-in, Governance | II | Project management, stakeholder management, risk assessment, team structures, communication strategies | Case studies from Ch. 1, 14 | Ch. 13-15 | | 13 | Building Analytics Capability: Organisational Structures, Talent | II | Centre of excellence, analytics functions, hiring, training, knowledge management, communities of practice | Employee records | Ch. 46, Appendix B | | 14 | A/B Testing Design: Randomisation, Causal Inference, Experimental Design | II | Randomisation, blocking, matching, propensity score, causal graphs, CATE (Conditional Average Treatment Effect) | Mobile money (hypothetical experimentation) | Ch. 15, 37 | | 15 | Running and Analysing A/B Tests: Power Analysis, Sequential Testing | II | Sample size calculation, minimum detectable effect (MDE), sequential testing, multi-arm testing, Bayesian AB testing | Simulated ecommerce experiments | Ch. 14, 33 | | 16 | Exploratory Data Analysis and Correlation Techniques | II | Univariate/bivariate/multivariate analysis, correlation (Pearson, Spearman, Kendall), confounding, Simpson's paradox | Bank loan portfolio, household survey | Ch. 5, 6, 17 | | 17 | Market Basket Analysis and Association Rules | II | Apriori algorithm, Eclat, support/confidence/lift, market basket, cross-selling, frequent itemsets | Ecommerce clickstream | Ch. 21, 43 | | 18 | Dimensionality Reduction: PCA, Factor Analysis, Feature Engineering | II | Principal component analysis, variance explained, feature selection, LASSO, Elastic Net, VIF | Bank loan, agricultural data | Ch. 6, 27 | | 19 | Text Mining and Sentiment Analysis | II | Bag-of-words, TF-IDF, LDA, sentiment classification, emotion detection, opinion mining | Customer reviews, call centre transcripts | Ch. 10, 35, 39 | | 20 | Computer Vision and Image Analytics | II | Image preprocessing, CNNs, transfer learning, object detection, quality control applications | Call centre visual data, manufacturing defects | Ch. 10, 41 | | 21 | Building Recommendation Systems | II | Content-based filtering, collaborative filtering, matrix factorisation, neural collaborative filtering, cold-start problem | Ecommerce clickstream | Ch. 17, 43 | | 22 | Social Network Analytics and Influencer Identification | II | Network centrality, PageRank, community detection, homophily, tie strength, viral coefficient | Retail network, social media data | Ch. 11, 35, 39 | | 23 | Classification and Logistic Regression | II | Logistic regression, odds ratios, model interpretation, probability threshold, decision boundaries | Bank loan default, insurance claims | Ch. 6, 27, 29 | | 24 | Regression Techniques: Linear, Multiple, and Nonlinear | II | Linear regression, multiple regression, interaction terms, polynomial regression, weighted regression | Bank loan, sales performance | Ch. 6, 33, 37 | | 25 | Optimisation Techniques: Linear and Integer Programming | II | Objective functions, constraints, branch-and-bound, sensitivity analysis, knapsack problem, travelling salesman | Inventory, pricing, allocation | Ch. 32, 42, 44 | | 26 | Monte Carlo Simulation and Risk Quantification | II | Sampling, probability distributions, value-at-risk (VaR), scenario analysis, tornado diagrams | Financial portfolios, pricing scenarios | Ch. 8, 48 | | 27 | Lead Scoring and Sales Qualification | III | Propensity modelling, explicit scoring, implicit scoring, rank ordering, prioritisation matrices | Sales performance data | Ch. 6, 33 | | 28 | Clustering and Segmentation | III | K-means, hierarchical clustering, DBSCAN, Gaussian mixture models, silhouette analysis, optimal k selection | Bank loan, customer records, all customer data | Ch. 6, 34, 43 | | 29 | Classification for Churn, Fraud, and Propensity Prediction | III | Decision trees, random forests, gradient boosting, cost-sensitive learning, imbalanced classes, SHAP values | Insurance claims, employee data, churn data | Ch. 7, 23, 36, 41 | | 30 | Survival Analysis and Hazard Modelling | III | Kaplan-Meier curves, Cox proportional hazards, censoring, time-to-event, retention curves, RFM | Customer lifetime value, employee tenure | Ch. 3, 34, 47 | | 31 | Demand Forecasting: Time Series and Causal Methods | III | ARIMA/SARIMA, Prophet, exogenous variables, cross-validation for time series, accuracy metrics (MAE, RMSE) | Agricultural commodities, inventory | Ch. 9, 45 | | 32 | Inventory Analytics and Optimisation | III | Reorder points, safety stock, ABC analysis, economic order quantity (EOQ), demand-supply balancing | Warehouse inventory, energy consumption | Ch. 25, 45 | | 33 | Sales Forecasting and Pipeline Analytics | III | Sales funnel, pipeline stage progression, quota allocation, rep-level forecasting, win-loss analysis | Sales performance data | Ch. 27, 37 | | 34 | Customer Profitability and Lifetime Value | III | Segment profitability, contribution margin, customer acquisition cost (CAC), customer lifetime value (CLV), payback period | Bank loan, household survey, customer records | Ch. 3, 30, 36, 47 | | 35 | Customer Satisfaction and Experience Analytics | III | NPS (Net Promoter Score), CSAT, CES, satisfaction drivers, gap analysis, journey mapping | Guest reviews, survey data, call centre | Ch. 2, 36 | | 36 | Customer Churn Prediction and Retention Analytics | III | Survival analysis, propensity models, retention curves, cohort analysis, intervention targeting, churn drivers | Insurance claims, employee records, customer lifetime | Ch. 29, 30, 47 | | 37 | Marketing Mix Modelling (MMM) and Attribution | IV | Adstock, diminishing returns, media elasticity, contribution analysis, multi-touch attribution | Digital ad spend, sales data | Ch. 9, 24, 33 | | 38 | Web Analytics and Funnel Optimisation | IV | User journey, funnel analysis, conversion rate optimisation (CRO), attribution models, cohort analysis | Ecommerce clickstream | Ch. 4, 22 | | 39 | Social Media and Text Analytics | IV | Engagement metrics, sentiment tracking, influencer analysis, viral potential, topic modelling | Customer surveys, social media data, reviews | Ch. 10, 19, 22 | | 40 | Geospatial Analytics and Location Optimisation | IV | Store location analysis, spatial clustering, trade area analysis, drive-time analysis, cannibalisation | Retail network, delivery routes | Ch. 11, 44 | | 41 | Quality Analytics and Six Sigma | IV | Statistical process control (SPC), control charts (p-chart, x-bar-R), process capability (Cp, Cpk), DMAIC, root cause analysis | Manufacturing defects | Ch. 5, 8 | | 42 | Pricing Analytics and Optimisation | IV | Price elasticity, willingness-to-pay, value-based pricing, dynamic pricing, price discrimination, revenue optimisation | Bank loan interest, hotel pricing | Ch. 3, 8, 25 | | 43 | Product Recommendation and Cross-Selling | IV | Collaborative filtering, content-based filtering, market basket, sequential patterns, uplift modelling | Ecommerce, retail network | Ch. 17, 21, 28 | | 44 | Supply Chain Analytics and Optimisation | IV | Demand planning, inventory optimisation, routing optimisation, supplier performance, risk quantification | Warehouse inventory, routes, energy | Ch. 25, 31, 32, 45 | | 45 | Inventory Planning and Demand-Supply Optimisation | IV | Demand planning, safety stock, service level, seasonal adjustments, promotion lift forecasting | Warehouse inventory, agricultural data | Ch. 9, 31, 32, 44 | | 46 | Employee Analytics and Performance Management | IV | Performance scoring, tenure analytics, skill mapping, compensation benchmarking, development tracking | Employee records | Ch. 2, 47 | | 47 | Workforce Attrition Prediction and Retention | IV | Survival models, propensity scoring, retention drivers, exit risk scoring, intervention effectiveness | Employee records, HR systems | Ch. 30, 36, 46 | | 48 | Financial Risk Analytics: Credit, Market, Operational | IV | Credit risk models, probability of default (PD), loss given default (LGD), value-at-risk (VaR), stress testing, correlation | Credit bureau, stock returns, insurance | Ch. 8, 26, 29 | | 49 | Financial Performance and Cash Flow Analytics | IV | Cash flow forecasting, working capital, liquidity analysis, variance analysis, budget forecasting | Financial data, energy consumption | Ch. 3, 24 | | 50 | Strategic Analytics and Executive Dashboards | IV | KPI strategy, balanced scorecard, strategic planning analytics, scenario analysis, drill-down dashboards | All datasets aggregated | Ch. 2, 13 | | 51 | Ethics, Fairness, and Responsible AI in Analytics | IV | Bias detection, fairness metrics, algorithmic transparency, SHAP/LIME, explainability, regulatory compliance | Case studies across all applications | All chapters | | 52 | Advanced Topics: Causal Inference and Policy Evaluation | IV | Causal DAGs, instrumental variables, regression discontinuity, difference-in-differences, policy impact | A/B testing data | Ch. 14, 15 | | 53 | Real-Time Analytics and Streaming Data | IV | Stream processing, online learning, real-time dashboards, alerting systems, lambda/kappa architectures | Mobile money, energy consumption | Ch. 4, 50 | | 54 | Deploying Analytics: Models, APIs, Monitoring | IV | Model serving, REST APIs, containerisation (Docker), model versioning, monitoring, retraining schedules | All predictive models | Ch. 6, 7 | | 55 | Case Study 1: E-commerce Revenue Optimisation | IV | End-to-end case study combining demand forecasting, pricing, recommendations, A/B testing | Ecommerce clickstream, aggregated sales | Multiple chapters | | 56 | Case Study 2: Customer Value Maximisation in Banking | IV | End-to-end case study combining segmentation, CLV, churn prediction, cross-sell recommendations | Bank loan portfolio, customer survey | Multiple chapters | --- ## Quick Lookup: Find a Topic ### By Analytical Technique **A/B Testing & Experimentation**: Ch. 14, 15, 52 **Anomaly Detection**: Ch. 29, 41, 53 **Churn Prediction**: Ch. 29, 30, 36, 47 **Classification**: Ch. 6, 23, 27, 29 **Clustering & Segmentation**: Ch. 6, 28, 40 **Demand Forecasting**: Ch. 9, 31, 45 **Deep Learning**: Ch. 7, 20, 54 **Ensemble Methods**: Ch. 7, 29, 44 **Forecasting & Time Series**: Ch. 9, 31, 37, 45 **Fraud Detection**: Ch. 29, 48 **Inventory Optimisation**: Ch. 25, 32, 44, 45 **Marketing Attribution**: Ch. 37, 38, 39 **Natural Language Processing**: Ch. 10, 19, 35, 39 **Network Analysis**: Ch. 11, 22, 39, 44 **Optimisation**: Ch. 8, 25, 40, 42, 44, 45 **Pricing Analytics**: Ch. 3, 8, 42 **Recommendation Systems**: Ch. 17, 21, 43 **Regression Analysis**: Ch. 5, 6, 24, 31, 37 **Risk Analytics**: Ch. 8, 26, 29, 48, 49 **Survival Analysis**: Ch. 30, 36, 47 **Text Analytics & Sentiment**: Ch. 10, 19, 35, 39 ### By Business Domain **Banking & Credit Risk**: Ch. 3, 5, 6, 23, 27, 29, 48, 56 **Customer Analytics**: Ch. 2, 3, 28, 30, 34, 35, 36, 38, 43, 47, 55, 56 **E-commerce & Retail**: Ch. 17, 21, 22, 38, 40, 43, 55 **Employee Analytics**: Ch. 13, 30, 46, 47 **Energy & Utilities**: Ch. 9, 25, 31, 32, 45 **Financial Management**: Ch. 3, 49, 50 **Insurance**: Ch. 29, 41, 48 **Manufacturing & Quality**: Ch. 8, 25, 41, 44 **Marketing & Digital**: Ch. 2, 14, 15, 37, 38, 39, 42, 43 **Supply Chain & Logistics**: Ch. 8, 11, 25, 31, 32, 44, 45 ### By Nigerian/African Data Used **Bank Loan Portfolio**: Ch. 3, 5, 6, 16, 18, 23, 24, 56 **Call Centre Logs**: Ch. 10, 20, 39, 46 **Ecommerce Clickstream**: Ch. 17, 20, 38, 43, 55 **Employee Records**: Ch. 13, 46, 47 **Energy Consumption**: Ch. 9, 25, 31, 32, 44, 45 **Guest Reviews**: Ch. 19, 35, 36 **Household Survey**: Ch. 2, 3, 5, 16, 24 **Insurance Claims**: Ch. 29, 36, 41, 48 **Manufacturing Defects**: Ch. 41 **Mobile Money Transactions**: Ch. 1, 4, 9, 14, 15, 22 **Retail Network**: Ch. 3, 11, 22, 40, 43 **Sales Performance**: Ch. 27, 33, 37, 46 **Warehouse Inventory**: Ch. 25, 31, 32, 44, 45 --- ## Learning Paths ### Path 1: From Foundations to Predictive Analytics (Complete Curriculum) 1. Ch. 1-11 (Foundations) 2. Ch. 12-25 (Advanced Methods) 3. Ch. 26-29 (Predictive Analytics) **Total**: 29 chapters, ~600 pages, 8-12 weeks ### Path 2: Customer-Centric Analytics (Focus) Ch. 2, 3, 28, 30, 34, 35, 36, 43, 46, 47, 50, 56 **Total**: 12 chapters, ~250 pages, 3-4 weeks ### Path 3: Marketing & Revenue Analytics (Focus) Ch. 2, 14, 15, 21, 33, 37, 38, 39, 42, 43, 55 **Total**: 11 chapters, ~220 pages, 3-4 weeks ### Path 4: Operations & Supply Chain (Focus) Ch. 4, 5, 8, 9, 11, 25, 31, 32, 40, 41, 44, 45 **Total**: 12 chapters, ~240 pages, 3-4 weeks ### Path 5: Risk & Finance (Focus) Ch. 3, 8, 26, 29, 30, 48, 49, 52 **Total**: 8 chapters, ~160 pages, 2-3 weeks --- ## Chapter Dependencies Some chapters build on others. A recommended reading order within each part: **Part I (Foundations)**: 1 → 2 → 3 → 4 → (5, 6 in parallel) → 7 → 8 → 9 → 10 → 11 **Part II (Advanced)**: 12 → 13 → (14 & 15 in parallel) → (16-25 mostly independent, but 18 builds on 16) **Part III (Predictive)**: 26 → (27-30 independent) → 31 → 32 → 33 → (34-36 independent) **Part IV (Applications)**: 37 onwards largely independent; 40-45 benefit from Ch. 25 and Ch. 32; 46-47 benefit from Ch. 30; 55-56 are capstone integrations. --- *This coverage map is current as of the 2024 edition. Check the book's website for updates and errata.*