Marketing Decision Intelligence: A Practical Framework
Learn a practical marketing decision framework connecting evidence and insights with priorities, accountable action, experiments, and evaluation.
Marketing Decisions Built on Data and Context
Data does not automatically become a decision. This cluster separates findings, insights, and recommendations, then explains how to prioritise and test them while final control stays with people.
4 articles
A route from evidence to marketing decisions
Four guides connecting the decision framework with causal diagnosis, hypotheses, and experiments.
- 01
the marketing decision intelligence framework — Start with evidence, alternatives, priorities, accountable action, and evaluation.
- 02
separating symptoms, triggers, and causes — Validate measurement, timelines, segments, and evidence that supports or challenges a diagnosis.
- 03
turning findings into hypotheses — Define the audience, mechanism, outcome, guardrail, and distinguishing evidence.
- 04
designing experiments for business decisions — Connect controls, treatments, metrics, stopping rules, and actions after the result.
Start here
Topic map
Causal diagnosis
Separate symptoms, triggers, measurement failures, and contributing causes before selecting a remedy.
- Root Cause Analysis for Digital Marketing Problems
Learn how to separate symptoms, triggers, and contributing causes before deciding how to address a digital marketing problem.
Marketing hypotheses
Turn findings into specific explanations or predictions that evidence can examine.
- How to Build Hypotheses from Marketing Data
Learn how to turn a marketing finding into a specific, testable hypothesis that can support a real decision.
Experiments and evaluation
Test recommendations through planned comparisons and explicit decision rules.
- Designing Experiments to Test Marketing Recommendations
Learn how to connect a marketing hypothesis with controls, metrics, guardrails, and a business decision.
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