Method Coverage Map

Method Coverage Map

This map shows where each of the 40 key business analytics methods is covered in “AI-Powered Business Analytics.” Each method is mapped to its foundational theory chapter and the applied/practical chapter where it is implemented.

Complete Method Coverage Table

Method Theory Chapter(s) Applied Chapter(s) Brief Description
A/B Testing & Business Experiments Ch. 5 (Statistical Foundations) Ch. 14, Ch. 15 Controlled randomised experiments to compare two variants and measure lift; foundation for causal inference in marketing and product.
Accuracy & Classification Metrics Ch. 6 (Machine Learning Foundations) Ch. 23, Ch. 29 Evaluation of classification model performance; precision, recall, F1 score, and confusion matrices.
Adstock Modelling Ch. 9 (Time Series & Forecasting) Ch. 37 Measurement of lagged effects of marketing spend on sales; geometric and Adstock decay transformations.
Correlation Analysis Ch. 5, Ch. 6 Ch. 16, Ch. 24 Pearson, Spearman, and Kendall correlations; understanding linear and rank-based associations between variables.
Key Predictive Indicators (KPIs) Ch. 2 (Business Analytics Fundamentals) Ch. 13, Ch. 27 Design and tracking of leading indicators that predict business outcomes; dashboarding and alert systems.
Forecasting & Time Series Analysis Ch. 9 (Time Series & Forecasting) Ch. 31, Ch. 32 ARIMA, SARIMA, exponential smoothing, Prophet; predicting future values from historical sequences.
Data Mining Ch. 4 (Data Collection & Preparation) Ch. 17, Ch. 28 Pattern discovery in large datasets; market basket analysis, association rules, and knowledge discovery.
Text Analytics Ch. 10 (Natural Language & Image Analytics) Ch. 19, Ch. 39 Extraction of insights from unstructured text; tokenisation, TF-IDF, topic modelling, LDA.
Sentiment Analysis Ch. 10 Ch. 19, Ch. 35 Classification of sentiment polarity (positive/negative/neutral) in customer feedback, reviews, social media.
Image Analytics & Computer Vision Ch. 10 Ch. 20 Feature extraction from images; object detection, image classification, quality control applications.
Recommendation Engines Ch. 7 (Advanced Machine Learning) Ch. 21, Ch. 43 Collaborative filtering, content-based filtering, matrix factorisation; personalised product recommendations.
Social Network Analytics Ch. 11 (Network & Location Analytics) Ch. 22 Graph analysis, centrality measures (degree, betweenness, closeness, eigenvector), community detection.
Monte Carlo Simulation Ch. 8 (Probabilistic Methods) Ch. 26, Ch. 48 Stochastic sampling for risk quantification, scenario analysis, and decision under uncertainty.
Location-Based Analytics Ch. 11 Ch. 22, Ch. 40 Geospatial analysis, store location optimisation, heatmaps, Voronoi diagrams, distance-based segmentation.
Linear & Integer Optimisation Ch. 8 Ch. 25, Ch. 32, Ch. 44 Linear programming and mixed-integer programming; resource allocation, portfolio optimisation, supply chain.
Decision Trees & Tree Ensembles Ch. 7 Ch. 27, Ch. 29 Tree-based classification and regression; interpretability, feature importance, variable interactions.
Neural Networks & Deep Learning Ch. 7 Ch. 28, Ch. 44 Feedforward networks, backpropagation, regularisation; applications to image, time series, and tabular data.
Speech Analytics Ch. 10 Ch. 20 Automated speech recognition (ASR), speaker diarisation, emotion detection from audio.
Predictive Sales Analytics Ch. 6, Ch. 9 Ch. 33 Sales forecasting, pipeline prediction, quota allocation, win/loss analysis using regression and classification.
Customer Profitability Analytics Ch. 3 (Business Metrics) Ch. 34, Ch. 36 Margin analysis by customer segment; customer contribution to profit; activity-based costing.
Sales Force Analytics Ch. 3, Ch. 11 Ch. 33 Performance tracking, territory analysis, rep productivity, compensation optimisation.
Marketing Mix Modelling (MMM) Ch. 9 Ch. 37 Attribution of sales to marketing variables (price, promotion, media spend, distribution); elasticity estimation.
Web Analytics Ch. 2, Ch. 4 Ch. 38 User behaviour tracking, funnel analysis, conversion rate optimisation, attribution, session analysis.
Social Media Analytics Ch. 4, Ch. 10 Ch. 35, Ch. 39 Engagement metrics, influencer identification, viral potential, trend detection, sentiment tracking.
Demand Forecasting Ch. 9 Ch. 31, Ch. 45 Time series and regression-based prediction of product demand; seasonality and trend decomposition.
Inventory Analytics & Optimisation Ch. 9 Ch. 32, Ch. 45 Stock level optimisation, reorder point calculation, safety stock, ABC analysis, demand-supply balancing.
Pricing Analytics & Optimisation Ch. 3, Ch. 8 Ch. 26, Ch. 42, Ch. 44 Price elasticity estimation, willingness-to-pay analysis, dynamic pricing, price discrimination.
Brand Analytics Ch. 3 Ch. 35, Ch. 36 Brand awareness, consideration, preference tracking; brand health metrics and equity valuation.
Customer Satisfaction Analytics Ch. 2, Ch. 3 Ch. 34, Ch. 35, Ch. 36 NPS, CSAT, CES measurement and prediction; satisfaction drivers and gap analysis.
Customer Lifetime Value (CLV) Ch. 3, Ch. 6 Ch. 34, Ch. 36, Ch. 47 Prediction of cumulative profit from a customer over time; retention and revenue projections.
Customer Segmentation Ch. 6, Ch. 7 Ch. 28, Ch. 34, Ch. 43 Clustering (K-means, hierarchical, DBSCAN); demographic, behavioural, and value-based segments.
Customer Experience (CX) Analytics Ch. 2, Ch. 3 Ch. 35, Ch. 36 Journey mapping, touchpoint analysis, experience metrics, pain point identification.
Customer Churn Prediction Ch. 6, Ch. 7 Ch. 29, Ch. 36, Ch. 47 Classification model to identify at-risk customers; propensity scoring and retention targeting.
Lead Scoring & Qualification Ch. 6, Ch. 7 Ch. 27, Ch. 33 Predictive ranking of sales prospects; explicit and implicit scoring models.
Market Basket Analysis Ch. 6 Ch. 17, Ch. 43 Association rules (Apriori, Eclat); support, confidence, lift; product affinity and cross-selling.
Supply Chain Analytics Ch. 8, Ch. 9, Ch. 11 Ch. 32, Ch. 44, Ch. 45 End-to-end logistics optimisation, supplier performance, demand-supply planning, risk mitigation.
Employee Attrition & Turnover Analytics Ch. 6, Ch. 7 Ch. 29, Ch. 46, Ch. 47 Prediction and analysis of employee departures; retention drivers and intervention targeting.
Employee Performance Analytics Ch. 2, Ch. 3 Ch. 46 Performance scoring, benchmarking, trend analysis, compensation alignment.
Fraud Detection & Prevention Ch. 6, Ch. 7 Ch. 29, Ch. 41 Anomaly detection, isolation forests, classification models; real-time monitoring and rule-based systems.
Quality Analytics & Six Sigma Ch. 5, Ch. 8 Ch. 41 Statistical process control (SPC), control charts, capability analysis, DMAIC methodology.
Financial Risk Analytics Ch. 8, Ch. 9 Ch. 48, Ch. 49 Value-at-Risk (VaR), credit risk, market risk, stress testing, correlation and tail-risk estimation.
Cash Flow & Liquidity Analytics Ch. 3 Ch. 49, Ch. 50 Forecasting cash flows, working capital optimisation, liquidity risk, seasonal cash planning.

Nigerian and African Datasets by Chapter

The book uses realistic, contextually relevant datasets from Nigeria and other African countries throughout all chapters. Below is a summary organised by chapter range:

Part I: Foundations (Chapters 1-11)

  • Nigerian Bank Loan Portfolio (8,000 accounts): Used in Chapters 5, 6 to teach statistical testing and classification
  • Nigerian Household Expenditure Survey (NBS) (3,000 households): Used in Chapter 3 for KPI and business metrics
  • Mobile Money Transactions (Airtime & Data) (50,000 transactions): Used in Chapters 4, 9 for data pipelines and time series
  • Nigerian Retail Store Data (2,500 transactions across 50 stores): Used in Chapters 11, 22 for network and location analytics

Part II: Advanced Methods (Chapters 12-25)

  • E-commerce Click-stream (Jumia-like marketplace) (100,000 sessions): Chapters 17, 20, 38
  • Customer Survey (FMCG sector) (5,000 respondents): Chapters 19, 35
  • Call Centre Data (Telecom) (10,000 calls): Chapters 20, 39, 46
  • Agricultural Commodity Prices (Nigeria & East Africa) (monthly data 2010-2024): Chapters 9, 31
  • Energy Consumption (PHCN/Distribution) (daily readings, 2,000+ meters): Chapters 9, 32, 44

Part III: Predictive Analytics (Chapters 26-36)

  • Insurance Claims (12,000 claims): Chapter 29 (churn, fraud detection)
  • Manufacturing Defects (monthly by facility, 36 plants): Chapter 41 (quality analytics)
  • Sales Performance (Multi-tier distribution) (monthly by salesperson, 2 years): Chapters 27, 33, 46
  • Credit Bureau Data (limited anonymised CBN dataset) (4,000 borrowers): Chapters 29, 48
  • Hospitality Guest Reviews (8,000 reviews from Nigerian hotels): Chapters 19, 35

Part IV: Optimisation & Applications (Chapters 37-50)

  • Digital Ad Spend & Attribution (monthly campaigns, 24 months): Chapter 37
  • Inventory at Regional Warehouses (daily stock levels, 5 distribution centres): Chapter 32, 45
  • Employee Records (HR) (1,200 employees, 5 years): Chapters 46, 47
  • Supply Chain Routing (500 delivery zones, historical routes): Chapter 44
  • Stock Returns (Nigeria Stock Exchange, select equities) (daily, 2 years): Chapter 48

All datasets are either: 1. Real and publicly available from Nigerian Bureau of Statistics (NBS), Central Bank of Nigeria (CBN), or World Bank 2. Synthetic but realistic: Generated to match true statistical properties of Nigerian business operations 3. Anonymised: Real company data provided under confidentiality agreements

See Appendix B for detailed sourcing, generation scripts, and licence information.

How to Use This Map

  1. Finding a specific method: Use the table above. Search for the method name in the first column.
  2. Learning a method in depth: Start with the Theory Chapter, then move to Applied Chapter(s).
  3. Understanding Nigerian context: Cross-reference with the datasets section to see which African data exemplify each method.
  4. Mapping applications to methods: Use the Brief Description to understand which technique solves your business problem.

Generated for “AI-Powered Business Analytics” by Bongo Adi | All chapters available in the main volume.