About This Course
The retail and e-commerce landscape is undergoing a profound data-driven transformation. From personalised product recommendations to dynamic pricing engines and real-time inventory management, the organisations that thrive are those capable of extracting meaningful insight from vast volumes of transactional, behavioural, and operational data. Analytics is no longer a back-office function — it is a core competitive capability that shapes merchandising strategy, customer experience, and supply chain efficiency at every level.
This intensive ten-day programme delivered by TrainingComplex is designed to build end-to-end competency in retail and e-commerce data analytics. Participants begin with the foundations of retail data ecosystems and progress through customer analytics, sales and demand forecasting, digital marketing measurement, pricing intelligence, and advanced predictive modelling. Each stage is grounded in realistic retail datasets and industry tools, ensuring that learning translates directly into workplace impact.
The programme is equally relevant to professionals working in physical retail chains, pure-play e-commerce businesses, and omnichannel operations. Whether you are optimising a loyalty programme, evaluating the ROI of a promotional campaign, or building a demand forecast for a peak season, this course provides the frameworks, tools, and confidence to do so with rigour and precision.
By the end of the programme, participants will have developed a personal analytics toolkit — spanning data wrangling, visualisation, statistical modelling, and storytelling — that positions them as analytically capable contributors within their organisations. TrainingComplex's expert facilitators bring hands-on industry experience in retail analytics, ensuring every concept is illustrated with relevant, current business scenarios.
Who Should Attend
- Retail analysts and business intelligence professionals seeking to deepen their e-commerce analytics expertise
- E-commerce managers and digital merchandisers responsible for sales performance and product strategy
- Marketing and CRM professionals working with customer data in retail environments
- Category managers and buyers who rely on data to make range planning and pricing decisions
- Supply chain and inventory planners in retail or omnichannel businesses
- Data analysts transitioning into or supporting the retail and consumer goods sector
- IT and digital transformation professionals implementing analytics solutions in retail organisations
- Entrepreneurs and founders of e-commerce businesses looking to build data-driven operations
Learning Objectives
- Understand the structure and sources of retail and e-commerce data, including transactional, behavioural, and operational datasets
- Apply data cleaning, integration, and transformation techniques to prepare retail datasets for analysis
- Analyse customer behaviour using segmentation, cohort analysis, and lifetime value modelling
- Build and interpret sales performance dashboards using industry-standard visualisation tools
- Construct demand forecasting and inventory optimisation models appropriate for retail contexts
- Measure and optimise digital marketing performance using attribution modelling and campaign analytics
- Apply pricing analytics and competitive intelligence techniques to inform dynamic pricing decisions
- Develop product and category analytics to support range planning and merchandising strategy
- Design and evaluate A/B tests and experiments to improve conversion rates and customer experience
- Build predictive models for churn, next-purchase probability, and product recommendation
- Interpret and present analytical findings in a compelling, business-focused narrative for non-technical stakeholders
- Align analytics strategy with broader retail business objectives and KPIs
- Apply ethical and data governance principles relevant to retail customer data
Course Modules
Module 1: Foundations of Retail and E-commerce Data
- The retail data ecosystem: transactional, behavioural, and operational data sources
- Understanding point-of-sale, web analytics, and CRM data structures
- Key retail and e-commerce KPIs: conversion rate, basket size, sell-through, and ROAS
- Data maturity in retail organisations: from reporting to predictive analytics
- Overview of analytics tools used in the retail sector: Excel, Power BI, Google Analytics, Python
- Common data quality challenges in retail environments and how to address them
Module 2: Data Collection, Integration, and Preparation
- Connecting and ingesting data from multiple retail sources: ERP, website, loyalty, and social
- Data cleaning techniques: handling missing values, duplicates, and outliers in retail datasets
- Data transformation: creating derived fields such as margin, frequency, and recency
- Introduction to SQL for querying retail databases
- Merging offline and online datasets for omnichannel analysis
- Building a clean, analysis-ready retail dataset as a practical exercise
Module 3: Customer Behaviour Analytics
- RFM analysis: recency, frequency, and monetary value segmentation
- Cohort analysis: understanding retention and repeat purchase patterns over time
- Customer lifetime value (CLV) modelling: definitions, calculation methods, and business applications
- Purchase funnel analysis: mapping and measuring the customer journey from awareness to conversion
- Identifying high-value, at-risk, and lapsed customer segments
- Building a customer segmentation dashboard in Power BI
Module 4: Sales Performance Analytics and Reporting
- Designing a retail sales performance dashboard: metrics, layout, and drill-down capability
- Year-on-year, month-on-month, and like-for-like sales analysis
- Product, category, and channel performance decomposition
- Sales variance analysis: isolating volume, price, and mix effects
- Visualisations best suited to retail sales reporting: waterfall charts, heat maps, and trend lines
- Presenting sales performance findings to commercial stakeholders
Module 5: Demand Forecasting and Inventory Analytics
- Principles of demand forecasting in retail: time series, seasonality, and trend decomposition
- Quantitative forecasting methods: moving averages, exponential smoothing, and regression-based models
- Measuring forecast accuracy: MAE, MAPE, and bias
- Inventory analytics: days of supply, stock cover, and ABC-XYZ classification
- Demand-driven replenishment and safety stock optimisation
- Case study: forecasting demand for a seasonal product range across multiple store formats
Module 6: Digital Marketing Analytics and Attribution
- Digital marketing data sources: Google Analytics 4, Meta Ads Manager, email platforms, and affiliate networks
- Key digital marketing metrics: impressions, CTR, CPC, ROAS, and customer acquisition cost
- Multi-touch attribution models: last-click, linear, time-decay, and data-driven approaches
- Measuring campaign effectiveness across paid search, social, and email channels
- Building a digital marketing performance report for an e-commerce business
- Using UTM parameters and tracking frameworks to ensure clean attribution data
Module 7: E-commerce Conversion Rate and UX Analytics
- Understanding the e-commerce conversion funnel: sessions, product views, add-to-cart, and checkout completion
- Using Google Analytics 4 to diagnose conversion rate drop-off points
- Heatmaps, session recordings, and on-site behaviour tools: Hotjar and Microsoft Clarity
- Checkout abandonment analysis: identifying friction points and revenue recovery opportunities
- Designing and interpreting A/B tests for product pages, checkout flows, and promotional banners
- Statistical significance in e-commerce experiments: avoiding false positives and premature conclusions
Module 8: Pricing Analytics and Competitive Intelligence
- Price elasticity of demand: measuring how customers respond to price changes
- Dynamic pricing principles and their application in e-commerce contexts
- Promotional pricing analytics: measuring the true incrementality of discounts and offers
- Competitive price monitoring: scraping, benchmarking, and responding to market price data
- Margin impact analysis: balancing revenue growth with profitability in pricing decisions
- Case study: building a pricing review framework for a multi-category online retailer
Module 9: Product and Merchandising Analytics
- Range planning analytics: assessing product breadth, depth, and overlap
- Product performance scoring: combining sales velocity, margin, and return rate into a composite metric
- Cross-sell and upsell analysis: identifying product affinity and basket-building opportunities
- Market basket analysis using association rules: support, confidence, and lift
- Seasonal and trend-driven range decisions supported by data
- Using analytics to rationalise underperforming SKUs while protecting category breadth
Module 10: Predictive Analytics and Machine Learning in Retail
- Introduction to predictive modelling concepts relevant to retail: classification, regression, and clustering
- Building a churn prediction model: features, training, and interpretation
- Next-best-action and next-purchase-probability modelling
- Product recommendation systems: collaborative filtering and content-based approaches
- Introduction to Python for retail predictive analytics: pandas, scikit-learn, and visualisation libraries
- Evaluating model performance: precision, recall, AUC-ROC, and business impact metrics
Module 11: Loyalty Programme and CRM Analytics
- Measuring the effectiveness of a retail loyalty programme: enrolment, earn, and redemption analytics
- Segmenting loyalty members by engagement level and purchase behaviour
- Personalisation analytics: using customer data to tailor communications and offers
- Email and CRM campaign analytics: open rates, click-through, and revenue attribution
- Linking loyalty data to broader customer lifetime value strategy
- Case study: redesigning a loyalty analytics framework for a regional supermarket chain
Module 12: Data Governance, Ethics, and Retail Data Strategy
- Data privacy regulations relevant to retail customer data: GDPR, CCPA, and Africa-specific frameworks
- Ethical use of customer behavioural data: consent, transparency, and fairness
- Data governance structures in retail organisations: ownership, stewardship, and quality standards
- Building a retail data strategy aligned to commercial objectives
- Creating a single customer view: challenges, solutions, and organisational requirements
- Balancing personalisation with privacy: responsible data-driven retail practices
Module 13: Analytics Storytelling and Stakeholder Communication
- Translating complex analytical findings into clear, decision-ready business narratives
- Structuring an analytics presentation: problem framing, insight, implication, and recommendation
- Designing executive dashboards that drive action rather than information overload
- Handling pushback and scepticism when presenting data-driven recommendations
- Final portfolio project: end-to-end retail analytics case study presentation
- Building an analytics culture: influencing colleagues and leadership to embrace data-driven decision making
Requirements & Prerequisites
Participants should have a working knowledge of Microsoft Excel and a basic familiarity with data concepts such as tables, filters, and summary statistics. Some prior exposure to retail, e-commerce, or consumer goods operations is beneficial and will help contextualise the applied exercises. No prior programming experience is required, though an openness to learning introductory Python or SQL concepts during the course will maximise the learning experience.
Training Methodology
- Hands-on analysis of realistic retail and e-commerce datasets using Excel, Power BI, and introductory Python or SQL in guided lab sessions
- Case study reviews of analytics initiatives from leading retailers and e-commerce platforms, examining both successes and missteps
- Dashboard-building workshops where participants design and present live sales and marketing performance dashboards
- Customer segmentation exercises using real transaction data to identify high-value cohorts and at-risk customers
- Demand forecasting simulations in which participants build and test forecasting models against historical sales patterns
- A/B test design workshops where teams construct experiment plans for conversion rate optimisation scenarios
- Group syndicate challenges that replicate cross-functional retail analytics decision rooms, requiring teams to present data-backed recommendations
- Peer review sessions where participants critique each other's visualisations and analytical narratives for clarity and business relevance
- Personal analytics portfolio project developed progressively across the ten days, culminating in a final presentation to the cohort
- Facilitator-led debriefs after each major exercise to consolidate learning and connect technical outputs to strategic business context
Organisational Benefits
- Improved revenue through data-driven merchandising, pricing, and promotional decisions
- Enhanced customer retention and loyalty driven by deeper understanding of purchase behaviour and lifetime value
- Reduced inventory costs and stockout risk through more accurate demand forecasting and replenishment planning
- Higher digital marketing ROI from rigorous campaign measurement and attribution analysis
- Stronger conversion rates and customer experience through structured experimentation and A/B testing
- Greater organisational confidence in data-backed decision making across commercial and operational teams
- Reduced dependence on gut-feel decisions by embedding analytics into standard planning and review cycles
- Competitive advantage through the ability to respond quickly to market signals and customer behaviour shifts
- Developed internal talent capable of driving analytics initiatives without relying solely on external consultants
- Improved data governance and responsible use of customer data in line with regulatory expectations
Where Is Retail and E-commerce Data Analytics Training Offered?
This course runs in-person across: Nairobi, Riyadh, Dubai, Lagos, Abuja, Cape Town, Zanzibar, Kigali, Nakuru, Mombasa, Kampala, Accra, Kisumu, Naivasha, Addis Ababa, Pretoria, Arusha, Johannesburg, Dar es Salaam, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
12 Oct 2026 – 23 Oct 2026 |
KES 160,000 |
| Live Virtual |
Online |
24 Oct 2026 – 13 Dec 2026 |
KES 160,000 |
| Nairobi, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 230,000 |
| Riyadh, Saudi Arabia |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 9,000 |
| Dubai, United Arab Emirates |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 8,200 |
| Lagos, Nigeria |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,000 |
| Abuja, Nigeria |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,600 |
| Cape Town, South Africa |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 7,600 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,000 |
| Kigali, Rwanda |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 3,800 |
| Nakuru, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Mombasa, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Abuja, Nigeria |
In-Person |
2 Nov 2026 – 13 Nov 2026 |
USD 5,600 |
| Nairobi, Kenya |
In-Person |
2 Nov 2026 – 13 Nov 2026 |
KES 230,000 |
| Kampala, Uganda |
In-Person |
2 Nov 2026 – 13 Nov 2026 |
USD 3,800 |
+228 more dates — view live schedule →
Frequently Asked Questions
Do I need programming experience to attend this course?
No prior programming experience is required. The course introduces Python and SQL concepts progressively, with guided lab sessions designed for participants who are new to coding. A willingness to engage with these tools will significantly enhance your learning, but the programme is structured so that non-programmers can fully participate and benefit.
What software and tools will we use during the training?
Participants will work with Microsoft Excel, Power BI, Google Analytics 4, and introductory Python during the programme. TrainingComplex provides access to all necessary datasets and, where required, temporary software licences or cloud-based environments for the duration of the course. Participants are encouraged to bring a laptop with Excel and Power BI Desktop pre-installed.
Will I receive a certificate upon completion?
Yes. Participants who attend the full ten-day programme and complete the final portfolio presentation will receive a TrainingComplex Certificate of Completion in Retail and E-commerce Data Analytics. This certificate can be shared on professional profiles and used to demonstrate competency to employers.
Is this course suitable for someone working in physical retail rather than e-commerce?
Absolutely. The programme covers analytics applicable to brick-and-mortar retail, pure-play e-commerce, and omnichannel operations. Modules on sales performance, inventory management, pricing, and merchandising analytics are directly relevant to physical store environments, and case studies are drawn from both online and in-store retail contexts.
How is the final project assessed?
Each participant develops an analytics portfolio project progressively throughout the ten days, applying course techniques to a realistic retail dataset. On the final day, participants present their findings and recommendations to the cohort in a simulated stakeholder briefing. Facilitators provide structured feedback, and the completed project serves as a tangible work sample participants can showcase to employers.
