Marketing Data Storytelling Training Course

Participants can map objectives, metrics and data sources into a usable measurement structure.

• Marketing objectives translated into acquisition, conversion, retention and value measures
• KPI trees linking revenue, pipeline, customer behaviour and channel activity
• North-star metrics assessed against strategic intent and customer value
• Metric definitions documented with formula, owner, source and reporting cadence
• Data-source mapping across advertising platforms, CRM, web and survey systems
• Measurement gaps prioritised by decision risk and business materiality

Participants can assess whether marketing data is fit for a stated decision.

• Source, scope, granularity and freshness checked before interpreting results
• Duplicate records, missing values and tracking breaks identified systematically
• Campaign naming conventions assessed for reporting consistency and traceability
• Denominator checks applied to rates, shares, averages and conversion metrics
• Period comparisons adjusted for seasonality, promotions and channel-mix changes
• Data lineage recorded from source system through calculation to presentation

Participants can create a decision-focused brief and storyboard for a live marketing question.

• Decision brief drafted with audience, choice, deadline and success criteria
• SCQA structure used to define situation, complication, question and answer
• Initial claim stated before selecting supporting data and visual evidence
• Audience knowledge, objections and information needs captured in a stakeholder profile
• Story spine maps context, insight, implication, recommendation and next step
• Evidence plan identifies required cuts, comparisons and validation checks

Participants can organise marketing findings into a concise executive argument.

• Pyramid Principle structures recommendation, supporting arguments and factual evidence
• Mutually exclusive, collectively exhaustive grouping improves message discipline
• Top-down communication adapted for executive briefings and steering committees
• Bottom-up analysis converted into an answer-first presentation sequence
• Headline messages written as conclusions rather than topic labels
• Recommendation logic tested for gaps, duplication and unsupported assertions

Participants can use funnel and journey data to identify priority customer actions.

• Funnel stages defined from awareness through conversion, onboarding and retention
• Stage conversion rates calculated using consistent populations and time windows
• Journey drop-offs located through event sequences and channel handovers
• Micro-conversions distinguished from commercial outcomes and customer value
• Segment comparisons reveal friction points hidden in aggregate funnel reporting
• Funnel findings converted into prioritised customer-experience interventions

Participants can develop audience stories that reveal meaningful behavioural differences.

• Segmentation bases compared across demographic, behavioural, value and needs data
• Cohort analysis tracks retention, repeat purchase and activation over time
• RFM analysis classifies customers by recency, frequency and monetary value
• Segment size, value and accessibility assessed before recommending action
• Aggregate averages challenged through distribution and subgroup analysis
• Persona claims separated from evidence-based segment characteristics and behaviours

Participants can interpret campaign performance while stating attribution limits clearly.

• Reach, frequency, click-through rate and conversion rate interpreted together
• Last-click attribution compared with first-click, linear and position-based models
• Incrementality distinguished from attributed conversions and platform-reported outcomes
• Media efficiency assessed using CPA, ROAS, marginal return and payback
• Channel interactions examined across paid, owned, earned and sales activity
• Attribution caveats stated without weakening a useful business recommendation

Participants can judge whether observed marketing results support a causal claim.

• Correlation, causation and confounding distinguished in campaign performance discussions
• A/B test design specifies hypothesis, treatment, control and primary metric
• Randomisation reduces selection bias when assigning audiences to treatments
• Statistical significance interpreted alongside effect size and business relevance
• Confidence intervals communicate uncertainty around observed conversion differences
• Quasi-experimental evidence assessed when controlled testing is not feasible

Participants can select and design charts that make marketing evidence easy to interpret.

• Chart choice follows analytical task: comparison, trend, distribution or relationship
• Bar charts communicate ranked categories and discrete performance comparisons
• Line charts show time-series patterns, seasonality and campaign inflection points
• Scatterplots reveal relationships, clusters and notable performance outliers
• Colour, position, length and annotation direct attention to intended evidence
• Truncated axes, dual scales and decorative effects evaluated for distortion

Participants can diagnose dashboard weaknesses and redesign views around decisions.

• Dashboard purpose defined by user decision, cadence and required action
• Information hierarchy separates primary signals from diagnostic supporting metrics
• Cognitive load reduced through grouping, whitespace and purposeful visual emphasis
• Filters and drill-downs aligned with realistic investigation paths and permissions
• Alert thresholds based on variance, materiality and operational response ownership
• Dashboard redesigns replace metric inventories with decision-oriented views

Participants can write concise insights and recommendations grounded in marketing evidence.

• Observation, insight and recommendation distinguished within marketing performance commentary
• Insight statements combine evidence, interpretation, implication and recommended action
• So-what chains connect metric movement to customer and commercial consequences
• Recommendations specify owner, action, timing, expected effect and dependencies
• Assumptions, risks and counterarguments documented beside major proposals
• Executive summaries prioritise decisions rather than analytical process descriptions

Participants can tailor one evidence base to different stakeholder priorities.

• Executive audiences receive strategic choices, financial implications and decision requests
• Channel teams receive diagnostic detail, optimisation priorities and testing actions
• Sales leaders receive pipeline quality, account signals and conversion implications
• Product teams receive customer behaviour evidence, friction points and adoption patterns
• Finance audiences receive assumptions, investment cases and measurement accountability
• Message depth adjusted without changing underlying data definitions or conclusions

Participants can communicate marketing evidence responsibly while protecting customer trust.

• Personal data minimisation applied when preparing customer-level marketing analyses
• Consent status considered before activating audiences or interpreting behavioural records
• Small-cell suppression reduces identification risk in segmented reporting
• Proxy variables examined for unfair targeting and exclusion consequences
• Causal claims constrained by evidence quality, design and known limitations
• Source notes document calculations, exclusions, uncertainty and data ownership

Participants can assemble a coherent presentation from evidence through recommendation.

• Slide sequence follows decision context, evidence, implication and requested action
• One-message-per-slide discipline improves scanning and spoken presentation clarity
• Action titles state conclusions supported by charts, tables and annotations
• Appendix material preserves audit trail without disrupting the main narrative
• Speaker notes prepare responses to likely stakeholder challenge and scrutiny
• Presentation flow tested against time limits and decision-maker attention

Participants can improve a marketing story through structured evidence and stakeholder review.

• Claim-evidence audit checks every conclusion against visible supporting data
• Red-team review tests logic, alternative explanations and missing stakeholder concerns
• Chart critique assesses readability, comparability, annotation and statistical honesty
• Feedback prioritisation separates material revisions from presentational preferences
• Version control records changed assumptions, calculations and recommendation rationale
• Final quality checks confirm definitions, sources, dates and decision request

Participants can convert a data story into a measurable marketing action plan.

• Recommendations translated into objectives, owners, milestones and resource requirements
• Measurement plans specify baseline, target, data source and review cadence
• Test-and-learn backlogs rank hypotheses by impact, confidence and effort
• Budget scenarios compare expected return, risk and capacity constraints
• Decision logs record approved actions, rejected options and evidence basis
• Post-implementation reviews connect planned outcomes with observed business results

Participants can build, review and present a complete evidence-based marketing data story with a clear decision request.

• Select a live marketing decision from campaign, customer or journey evidence
• Build a decision brief, metric framework and evidence-quality assessment
• Analyse funnel, segment, cohort or attribution findings supporting the recommendation
• Design annotated visuals and an answer-first executive presentation sequence
• Review claims through peer challenge, source checks and revision priorities
• Present recommendation, implementation plan, risks and measurement success criteria