Quick answer
Data mining looks for patterns worth investigating: products bought together, similar customer behaviour or an unexpected change in response. Treat a pattern as a lead to follow. It doesn’t tell you, by itself, what caused it or whether acting on it will help.
Choose a pattern that could change a decision
Data mining looks for useful patterns in data. In marketing, that might mean products often bought together, groups of customers with similar behavior or an unusual change in campaign response. IBM’s overview describes common techniques.
Ask what you’d do if the pattern holds. Products often bought together might justify testing an offer; a customer group might deserve more research. If you can’t name a next step, you have an interesting observation rather than a marketing plan.
| Method | Question | Check before acting |
|---|---|---|
| Association | Which products appear together? | Base rates and existing promotions |
| Clustering | Which customers behave similarly? | Stable groups with useful differences |
| Classification | Which cases may reach an outcome? | Performance on unseen records |
| Anomaly detection | What changed unexpectedly? | Tracking faults and ordinary seasonality |
Read an association in context
Suppose 100 of 1,000 baskets contain product A, and 30 of those also contain B. The share of A baskets containing B is 30%. If B appears in only 10% of all baskets, that association is worth investigating.
It is not proof that promoting B to buyers of A will increase sales. Both products may already be bundled, discounted together or popular during the same season. Check those explanations before designing a new offer.
Protect the analysis from hindsight
Use information available at the time of the proposed decision. Keep a later period aside to see whether the pattern persists. If many combinations are explored, some striking results will occur by chance; a fresh sample helps test whether a finding holds up.
Inspect missing and duplicate records. Campaign-specific collection rules can create apparent customer differences that actually reflect the form people used. Our data-quality guide covers the checks that should come first.
Turn the finding into a test
Write the proposed intervention, the audience eligible for it and the outcome to measure. Where practical, compare it with a suitable control. Include negative effects, such as lower margin or more unsubscribes, alongside the desired response.
Retain the original analysis and the test result. A pattern that fails to improve the decision is still useful evidence: it prevents the team from treating an attractive correlation as a permanent rule.
Treat a behavioural pattern as a hypothesis to verify, then test whether acting on it changes the result. See our guides to B2B intent data and marketing incrementality testing.
