Marketing Analytics With AI

AI can describe a funnel convincingly while dividing the wrong numbers or inventing a cause. Good marketing analytics starts with definitions, not a narrative.

Before asking a model what happened, establish the population, time period, unit of analysis, and business question. Then check its arithmetic and distinguish an observation from an explanation.

Define each stage

Imagine a fictional product campaign with 1,000 eligible visitors, 100 trial accounts associated with those visitors, and 20 of those trial accounts completing an activation action within an agreed window.

These are synthetic numbers for a worked example. They are not benchmarks or results from a real business.

For this example, visitor-to-trial rate is 100 divided by 1,000, or 10%. Trial-to-activation rate is 20 divided by 100, or 20%. Visitor-to-activation rate is 20 divided by 1,000, or 2%.

Those calculations only make sense if the stages describe compatible groups and the linkage is valid.

Do not swap users, sessions, events, and accounts

One person can start multiple sessions, and one session can contain multiple events. An account may have several users.

Google Analytics distinguishes metrics with different scopes, including user and session key event rates. A custom account-activation rate is another definition and should not silently inherit a user-based denominator.

Write the definition beside the result. If you cannot connect an account to the appropriate acquisition group, report the limitation rather than presenting a false full-funnel rate.

Ask AI for observations before causes

A useful prompt is: Using only this table and these definitions, list observed differences, show each calculation, and separate possible explanations from established facts. Identify missing information needed to evaluate the explanations.

Review the output against the input. A statement that activation is 20% should name the trial-account denominator. A statement that a particular headline caused the rate needs evidence the table does not contain.

Keep data access appropriate. Summaries do not require uploading unnecessary customer identifiers into an unapproved tool.

Compare segments without confusing their composition

Suppose two fictional channels have different activation rates. That difference might reflect audience, device mix, timing, message, product fit, or measurement.

Check counts as well as rates. A percentage based on a small number of accounts can vary substantially. Confirm that both groups have had the same opportunity to activate.

Do not describe an observational channel comparison as a randomized experiment. It may identify a useful question without establishing a causal answer.

Recalculate aggregates from the correct totals

If you combine groups, calculate the overall rate from the matching totals. Do not average percentages when the group sizes differ.