Data Science for Marketing Professionals Training Course

Course overview

Marketing teams generate campaign, web, CRM and customer-service data every day, yet many decisions still rely on channel-level reports, last-click metrics and assumptions about audience behaviour. This course helps marketing professionals turn scattered data into evidence for budget allocation, targeting, retention and creative decisions. Participants learn to frame commercially useful questions, distinguish correlation from causation, assess data quality, and communicate findings in terms that marketing leaders can act on.

The programme covers the marketing analytics workflow from data collection and preparation through exploratory analysis, segmentation, forecasting and campaign measurement. Participants work with customer and campaign datasets using Python in Jupyter notebooks, SQL-style queries, GA4 concepts and Power BI dashboards. They calculate conversion, retention, customer lifetime value, acquisition cost and incremental lift; build customer segments; interpret A/B tests; and select appropriate visualisations for executive reporting.

Instructor-led demonstrations are followed by guided analysis labs built around realistic ecommerce and B2B marketing scenarios. Participants clean a campaign dataset, join customer and transaction records, create an audience-segmentation model, evaluate an experiment and develop a dashboard-based recommendation. They leave with a reusable marketing analytics workbook, annotated notebook templates, a measurement-plan template and a final data-backed campaign optimisation proposal that can be adapted for their own organisation.

The course is designed for marketers who need to use data confidently without becoming full-time data scientists. It is particularly valuable for professionals responsible for digital performance, CRM, customer insight, campaign planning, marketing operations or marketing investment decisions.

Course objectives

By the end of this course, participants will be able to:

• Frame marketing questions as measurable hypotheses with defined decision criteria
• Audit campaign and customer datasets for missing values, duplicates, bias and tracking gaps
• Query and combine marketing data using SQL-style filtering, aggregation and joins
• Calculate CAC, conversion rate, retention, customer lifetime value and incremental ROI
• Build customer segments using RFM analysis and clustering interpretation
• Evaluate A/B test results using confidence intervals, significance and practical lift
• Create an executive marketing-performance dashboard in Microsoft Power BI
• Produce a data-backed campaign optimisation proposal with actions, assumptions and measurement metrics

Benefits of attending

Target competencies

Who should attend

• Digital Marketing Managers — who must allocate paid-media and channel budgets using performance evidence
• Marketing Analysts — who need stronger methods for preparing data, testing hypotheses and explaining results
• CRM and Lifecycle Marketing Specialists — who use customer behaviour data to improve retention and personalisation
• Marketing Operations Managers — who oversee campaign data flows, reporting standards and measurement governance
• Brand and Campaign Managers — who need to assess audience, creative and campaign results beyond surface metrics
• Product Marketing Managers — who use adoption, funnel and customer data to support go-to-market decisions

Requirements and prerequisites

This is a foundation-to-intermediate course and does not require prior data science, statistics or programming experience. Participants should be comfortable working with spreadsheets, reading marketing reports and discussing measures such as impressions, clicks, leads, conversions and revenue. Familiarity with Excel formulas, Google Analytics or a CRM is helpful but not essential. Learners should bring a laptop able to access a modern web browser and install or use provided course software. The course introduces Python and SQL-style querying from the ground up; advanced coding, calculus, machine-learning experience and prior Power BI expertise are not required.

Training methodology

The five-day course alternates short instructor-led explanations with hands-on analysis in supplied marketing datasets. Participants use guided Jupyter notebooks to inspect, clean and analyse customer, transaction and campaign records, then translate results into Power BI visualisations and management recommendations. Case discussions examine attribution disputes, poor tracking design and misleading test results. Small groups critique each other’s dashboard choices and experiment plans. The final session includes an application-planning workshop in which each participant maps one live marketing decision, its required data, metrics, analysis steps and stakeholder output.

Course outline

Day 1: Marketing data foundations and decision framing

• Marketing analytics workflow from business question to action
• North-star metrics, supporting KPIs and metric hierarchies
• Funnel definitions for awareness, acquisition, conversion and retention
• Data sources across GA4, CRM, advertising platforms and ecommerce systems
• Data types, granularity and the unit-of-analysis problem
• Data quality checks for missing values, duplicates and inconsistent identifiers
• Correlation, causation and common marketing measurement biases

Workshop: Participants create a measurement brief for a campaign optimisation decision, defining the hypothesis, audience, metrics, data sources and success threshold.

Day 2: Preparing and exploring marketing data

• Jupyter Notebook workflow and Python dataframe basics
• Importing CSV marketing exports with pandas
• Cleaning dates, channel names, currencies and categorical fields
• Handling missing values, duplicate contacts and invalid campaign records
• SQL-style filtering, grouping and aggregation for campaign analysis
• Joining customer, transaction and campaign tables using keys
• Exploratory visualisation with distributions, cohorts and conversion funnels

Workshop: Participants clean and join a multi-channel campaign dataset, then produce a documented analysis-ready table and initial funnel findings.

Day 3: Customer insight, segmentation and value

• Customer lifecycle stages and behavioural event data
• RFM scoring for recency, frequency and monetary value
• Cohort retention analysis and repeat-purchase curves
• Customer acquisition cost and payback-period calculation
• Customer lifetime value models and assumption testing
• Clustering concepts for audience segmentation
• Segment profiling using descriptive statistics and persona evidence

Workshop: Participants build an RFM segmentation, profile priority customer groups and recommend one differentiated retention or acquisition action per segment.

Day 4: Campaign measurement and experimentation

• Attribution models and the limitations of last-click reporting
• Control groups, holdouts and incrementality measurement
• A/B test hypotheses, primary metrics and guardrail metrics
• Sample size, statistical power and minimum detectable effect
• Confidence intervals, p-values and practical significance
• Interpreting conversion lift and revenue impact
• Common experiment failures including contamination, novelty effects and peeking

Workshop: Participants evaluate an email and landing-page A/B test, calculate the decision metrics and write a recommendation that distinguishes statistical from commercial significance.

Day 5: Dashboard storytelling and marketing action plans

• Power BI data model design for marketing performance reporting
• Measures for conversion, CAC, ROAS, retention and lifetime value
• Dashboard visual selection for executives and channel owners
• Filters, drill-through and audience-segment comparisons
• Data storytelling with claims, evidence, caveats and recommended actions
• Marketing measurement governance and KPI definition sheets
• Ninety-day analytics application planning

Workshop: Participants build a Power BI marketing dashboard and present a campaign optimisation proposal with priorities, expected impact, measurement plan and implementation risks.

Tools & standards covered

Python, Jupyter Notebook, Microsoft Power BI, Google Analytics 4

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions