About This Course
In today's hyper-competitive marketplace, organisations that harness customer data effectively consistently outperform those that rely on intuition alone. Marketing and sales teams are sitting on vast repositories of behavioural, transactional, and demographic data — yet many lack the analytical capability to convert that data into clear, strategic action. This course is designed to close that gap.
Advanced Customer Analytics for Marketing and Sales Teams Training is a rigorous, application-focused five-day programme that takes participants beyond basic reporting and dashboards into the world of predictive modelling, customer lifetime value analysis, churn prediction, and personalisation at scale. Each module is grounded in real business scenarios drawn from B2B and B2C environments, giving participants immediately transferable skills.
Delivered by experienced analytics practitioners, the programme combines conceptual depth with hands-on practice. Participants work with real datasets, use industry-standard tools, and leave with frameworks they can deploy within their organisations from day one. Whether your goal is to reduce churn, optimise campaign spend, improve lead scoring, or sharpen your customer segmentation strategy, this training provides the analytical muscle to deliver results.
TrainingComplex has designed this course for professionals who already have foundational familiarity with marketing or sales data and are ready to move to a significantly higher level of analytical sophistication. By the end of the programme, participants will be equipped to make data-driven decisions with confidence, communicate insights persuasively to senior stakeholders, and champion a culture of analytics within their teams.
Who Should Attend
- Marketing analysts and senior marketing executives seeking to deepen their data-driven decision-making capabilities
- Sales operations managers and sales analysts responsible for pipeline reporting and forecasting
- CRM managers and customer success leaders working with large customer databases
- Digital marketing specialists involved in campaign optimisation and audience targeting
- Brand and product managers who rely on customer insight to guide positioning and launch strategies
- Business intelligence professionals supporting commercial teams
- Chief Marketing Officers and Sales Directors seeking to embed analytics culture within their functions
Learning Objectives
- Understand the customer analytics ecosystem and the role of data across the full marketing and sales funnel
- Design and implement advanced customer segmentation models including RFM, behavioural, and psychographic approaches
- Build and interpret predictive models for churn, propensity to buy, and next-best-action recommendations
- Calculate, interpret, and strategically apply customer lifetime value (CLV) metrics to inform acquisition and retention investment
- Apply attribution modelling techniques to assess the true impact of marketing channels and touchpoints
- Use cohort analysis and customer journey mapping to uncover patterns in engagement and conversion
- Develop lead scoring frameworks that align marketing qualification criteria with sales conversion realities
- Interpret A/B test results and design statistically sound experiments to optimise campaigns and sales interventions
- Translate complex analytical findings into clear, compelling narratives and visualisations for executive audiences
- Build a roadmap for embedding advanced customer analytics into ongoing marketing and sales operations
Course Modules
Module 1: The Customer Analytics Landscape: Strategy and Foundations
- The role of analytics across the marketing and sales funnel
- Key data types: transactional, behavioural, demographic, and attitudinal
- Building a customer data infrastructure: sources, integration, and governance
- Defining the right KPIs for marketing and sales analytics maturity
- Common analytics pitfalls that undermine commercial decision-making
- Setting up an analytics-driven team culture and operating model
Module 2: Customer Segmentation: From Basic Demographics to Advanced Models
- Limitations of traditional demographic segmentation in modern markets
- RFM analysis: recency, frequency, and monetary value modelling in depth
- Behavioural segmentation using digital engagement and purchase pattern data
- Psychographic and needs-based segmentation approaches
- Validating and stress-testing segmentation models for commercial relevance
- Applying segmentation outputs to campaign design and sales territory planning
Module 3: Customer Journey Mapping and Cohort Analysis
- Designing end-to-end customer journey maps tied to data touchpoints
- Identifying drop-off points and friction in acquisition and onboarding flows
- Building cohort analysis tables to track retention and revenue over time
- Interpreting cohort data to distinguish product, channel, and seasonal effects
- Using journey analytics to prioritise investment across the customer lifecycle
- Linking journey insights to CRM workflow and campaign trigger design
Module 4: Predictive Analytics: Churn Modelling and Propensity Scoring
- Introduction to predictive modelling concepts for non-data-scientists
- Identifying leading indicators of customer churn from behavioural data
- Building and interpreting a churn prediction model: variables, outputs, and thresholds
- Propensity-to-buy scoring: using historical data to rank prospects and customers
- Next-best-action frameworks informed by predictive model outputs
- Translating model scores into actionable sales and marketing interventions
Module 5: Customer Lifetime Value: Measurement, Forecasting, and Strategy
- Historical versus predictive CLV: definitions, formulas, and appropriate use cases
- Segmenting customers by lifetime value to prioritise acquisition and retention spend
- Modelling the impact of retention rate improvements on long-term CLV
- Linking CLV to customer acquisition cost for sustainable unit economics
- Using CLV tiers to customise service levels, offers, and communication frequency
- Building a CLV dashboard that marketing and sales leadership can act on
Module 6: Attribution Modelling and Marketing Mix Analysis
- Understanding the attribution problem: why last-click models mislead decisions
- Rule-based attribution models: first-touch, linear, time-decay, and position-based
- Introduction to data-driven attribution and its requirements
- Comparing attribution models across a unified campaign dataset
- Marketing mix modelling: isolating the contribution of offline and online channels
- Using attribution insights to reallocate budget and improve blended ROI
Module 7: Lead Scoring and Sales Pipeline Analytics
- Designing a lead scoring framework aligned to actual sales conversion data
- Combining demographic fit scores with behavioural engagement signals
- Calibrating scoring thresholds to optimise marketing-qualified to sales-qualified lead handoff
- Pipeline velocity analysis: identifying where deals stall and why
- Win/loss analysis: extracting strategic insight from closed deal data
- Building a sales forecasting model that integrates lead score distributions
Module 8: Experimentation: A/B Testing and Statistical Confidence
- Principles of controlled experimentation in marketing and sales contexts
- Sample size calculation and the cost of underpowered tests
- Designing clean A/B and multivariate tests for emails, landing pages, and offers
- Interpreting statistical significance versus practical significance in test results
- Common testing mistakes: peeking, multiple comparisons, and selection bias
- Building an experimentation roadmap that accelerates learning across teams
Module 9: Data Visualisation and Communicating Insights to Stakeholders
- Principles of effective data visualisation for commercial audiences
- Choosing the right chart type for segmentation, trend, and comparison data
- Building executive dashboards that drive action rather than passive review
- Storytelling with data: structuring analytical narratives for sales and marketing briefings
- Handling pushback and scepticism when presenting data-driven recommendations
- Designing self-service analytics reports that empower non-technical team members
Module 10: Building an Analytics-Driven Marketing and Sales Function
- Assessing your organisation's current analytics maturity and identifying gaps
- Defining roles, responsibilities, and tools for a high-performing analytics team
- Creating a data governance framework that supports trust and consistent interpretation
- Embedding analytics into campaign planning, quarterly reviews, and sales forecasting cycles
- Building internal capability: upskilling marketers and salespeople in data fluency
- Developing a 90-day analytics implementation roadmap from course insights to organisational impact
Requirements & Prerequisites
Participants should have at least one to two years of experience working in a marketing, sales, or customer data role. A working familiarity with CRM systems, spreadsheet tools, and basic data interpretation is expected. Prior exposure to analytics platforms or business intelligence tools is helpful but not required, as the programme is designed to build advanced competency from a solid practical foundation.
Training Methodology
- Hands-on analysis of curated real-world customer datasets drawn from retail, financial services, and subscription-based business models
- Guided segmentation workshops where participants build and test RFM and behavioural models using live data
- Churn prediction case studies that walk through the full analytics workflow from data preparation to model interpretation
- Peer-led attribution modelling exercises comparing last-click, linear, and data-driven attribution approaches across realistic campaign scenarios
- Customer lifetime value calculation labs using Excel and optional introduction to Python or R-based approaches
- A/B testing design clinics where participants plan, size, and interpret controlled experiments relevant to their own campaigns
- Executive communication workshops focused on translating analytical outputs into board-ready dashboards and written insight summaries
- Cross-functional team simulations replicating the handoff of analytics-driven insights from marketing analysts to sales leadership
- Reflective planning sessions at the end of each day to help participants map new concepts to their specific organisational context
- A capstone analytics project completed in small teams, presenting a full customer analytics strategy to a panel of facilitators
Organisational Benefits
- Improved return on marketing investment through more precise audience targeting and channel allocation
- Reduced customer churn as teams identify at-risk segments earlier and respond with tailored retention strategies
- Stronger sales pipeline quality driven by data-validated lead scoring and qualification criteria
- More effective personalisation across campaigns, resulting in higher engagement and conversion rates
- Greater alignment between marketing and sales functions through shared data language and common metrics
- Faster, more confident decision-making supported by predictive insights rather than reactive reporting
- Enhanced competitive positioning through a sustained analytical advantage in understanding customer behaviour
- A measurable uplift in customer lifetime value as acquisition, onboarding, and retention strategies become data-informed
Where Is Advanced Customer Analytics for Marketing and Sales Teams Training Offered?
This course runs in-person across: Kisumu, Johannesburg, Addis Ababa, Pretoria, Arusha, Kampala, Naivasha, Accra, Dar es Salaam, Nairobi, Abuja, Zanzibar, Dubai, Nakuru, Kigali, Riyadh, Lagos, Mombasa, Cape Town, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
19 Oct 2026 – 23 Oct 2026 |
KES 80,000 |
| Kisumu, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Johannesburg, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,500 |
| Addis Ababa, Ethiopia |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,400 |
| Pretoria, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,300 |
| Arusha, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,100 |
| Kampala, Uganda |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 1,900 |
| Naivasha, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Accra, Ghana |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
| Dar es Salaam, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,300 |
| Pretoria, South Africa |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 3,300 |
| Arusha, Tanzania |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,100 |
| Nairobi, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 105,000 |
| Addis Ababa, Ethiopia |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,400 |
| Kisumu, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 120,000 |
+229 more dates — view live schedule →
Frequently Asked Questions
Do I need to be a data scientist or have coding skills to attend this course?
No coding experience is required. This course is designed for marketing and sales professionals who work with data regularly but are not data scientists. The programme focuses on analytical thinking, model interpretation, and commercial application rather than programming, although optional exposure to Python and R concepts is offered for those interested.
What tools and software will be used during the training?
Participants will primarily work with Excel for hands-on exercises, supplemented by demonstrations using common analytics and CRM platforms. Selected sessions will introduce Python or R concepts at a conceptual level. Participants are encouraged to bring their own laptops, though TrainingComplex can provide access to required tools during the sessions.
Will I receive a certificate upon completing this course?
Yes. Participants who attend all five days and complete the capstone group project will receive a TrainingComplex Certificate of Completion in Advanced Customer Analytics for Marketing and Sales Teams. This certificate can be used for professional development records and shared on professional profiles.
Is this course suitable for someone from a B2B sales background rather than consumer marketing?
Absolutely. The course draws on case studies and datasets from both B2B and B2C environments. Concepts such as lead scoring, pipeline analytics, win/loss analysis, and customer lifetime value are explored through a B2B lens as well as a B2C one, ensuring relevance regardless of your industry or go-to-market model.
Can our organisation send multiple team members and receive group pricing?
Yes. TrainingComplex offers group enrolment discounts for organisations sending three or more participants to the same cohort. We also offer in-house delivery of this course for teams of eight or more, which allows the content and datasets to be customised to your specific industry, CRM system, and strategic priorities. Please contact us for a tailored proposal.
