Customers who connect an integration remain subscribed longer. That relationship may be useful, but it does not yet show that persuading more customers to connect one will improve retention. People already committed to the product may be more likely to invest in setup, or larger teams may both use integrations and stay longer.

Correlation describes a statistical relationship between measures. Causation concerns the effect of an intervention or change: what would happen to an outcome if the relevant factor were changed, compared with an appropriate alternative. The second claim needs more than the first pattern.

Investigate the alternative explanations

Confounding occurs when another factor affects both the proposed cause and the outcome. Reverse direction is another possibility, as when commitment encourages feature adoption rather than adoption creating commitment. Selection and missing data can also produce misleading relationships, while searching many comparisons can uncover patterns due to sampling variation.

Time matters too. If customers are classified as integration users based on an action that occurs later, they must remain long enough to take that action. Comparing them with everyone else without accounting for timing can create a misleading retention advantage.

Microsoft Research’s work on causal inference distinguishes correlation-based prediction from reasoning about interventions. A variable can be a useful predictor without being an effective target for change.

Use a design that addresses the causal question

Random assignment in a well-run A/B test makes treatment groups comparable in expectation. It does not guarantee identical groups in a particular sample or protect against faulty implementation, missing outcomes and interference between users.

In the integration example, randomly offering a new setup prompt could estimate the effect of that prompt under the experimental design. It would not automatically estimate the effect of integration adoption itself, because people still decide whether to adopt and the prompt may affect other behaviour.

Where randomisation is impractical, observational causal methods can be useful with explicit assumptions, suitable data and careful design. A simple before-and-after comparison is vulnerable to time trends and other changes. Adding comparison groups can help under the relevant assumptions, but no method name removes the need to examine them.

Use qualitative evidence to sharpen the explanation

User interviews and observation can reveal why integration use might matter: reduced duplicate work, dependence on a shared workflow or a need that existed before adoption. That evidence can improve the causal question and expose alternative explanations, though an account of a plausible mechanism does not alone identify its effect.

Check measurement definitions and temporal order before interpreting a dashboard pattern. Examine whether the association persists in relevant groups without treating adjustment for a few observed characteristics as proof that all confounding has been removed.

Report the relationship in language the design supports. “Integration users retained longer in this dataset” preserves the observation. “Integrations improve retention” adds a causal conclusion. Keeping those sentences distinct helps the team choose a useful next study instead of building a strategy around an untested explanation.