Marketing Data

Build a clearer view of marketing performance. Explore data quality, integration, analytics and visualisation, with practical questions to guide better decisions.

Understand training data, retrieved context and evaluation, with checks for outcome labels, data leakage and reliable marketing predictions.

Marketing data integration: campaigns, CRM and reporting

Connect campaigns and CRM records with clear identifiers, field ownership and reconciliation. Includes a field map and acceptance checks.

Data quality tools: what marketing teams should compare

Compare data quality tools for CRM records using duplicate handling, validation, integration and review controls, with a practical scorecard.

Data warehousing: marketing use cases across industries

Explore marketing warehouse use cases in retail, B2B and subscriptions, with checks for record grain, joins, timing and reliable reporting.

Data mining for marketing: finding patterns worth testing

Find marketing patterns worth testing. Compare mining methods and assess associations, data quality and the difference between correlation and cause.

Where data science helps marketing decisions

Prioritise marketing data-science projects by the decision, available records and evidence needed across acquisition, retention and forecasting.

Data science for marketing: from analysis to action

Take a marketing data-science project from question to action with clear outcomes, reliable records, a baseline and a practical evaluation plan.

Data visualisation for marketers: choosing the right chart

Choose marketing charts that answer a clear question, with consistent denominators, fair comparisons and accessible presentation.

Big data vs small data: choosing marketing evidence

Choose evidence for a marketing decision by the question, sample quality and missing groups, and combine qualitative and quantitative findings.

Marketing analytics: turning reports into decisions

Build a marketing analytics routine around clear metrics, reliable comparisons and actions, with checks for misleading changes in performance.

Understand how marketing data management, analytics and data science fit together, with clear ownership and a practical campaign example.