AI can help summarize a marketing report, propose questions, or explain a calculation. The quality of the discussion still depends on the data and the question. A plausible explanation for a change in performance is a hypothesis until it has been tested against the evidence.
Start with a decision: whether to investigate a landing page, review a campaign, or improve lead qualification. “Analyze all our marketing” is too broad. “Which step in this inquiry journey changed during the last comparable period?” creates a more useful starting point.
Agree on definitions first
Before sharing a report, define the metrics. What counts as a lead? Are duplicate submissions removed? Is revenue gross or net of refunds? Which timezone and attribution window are being used? Two reports can disagree without either being broken if they describe different things.
Create a short data dictionary and attach it to the analysis brief. Include the date range, source, known tracking changes, and missing fields. Remove unnecessary personal information and use an approved environment for business data.
Ask for observations and hypotheses separately
A useful instruction is: “List the changes visible in this table, show the calculation behind each observation, and separate possible explanations from confirmed facts.” Require the analysis to identify missing information that would help distinguish between explanations.
For a hypothetical campaign, fewer inquiries might reflect less traffic, a lower form completion rate, tracking failure, or a shift in traffic quality. A summary that names one cause without investigating the alternatives is not enough for a budget decision.
Validate the arithmetic
- Recalculate important totals and percentages independently.
- Check whether denominators match across comparisons.
- Look for missing dates, duplicates, or partial periods.
- Separate new tracking behavior from actual customer behavior.
- Inspect a small sample of underlying records where appropriate.
Be especially careful with small samples. A large percentage change can come from a handful of events. Report the underlying counts alongside the rate so the decision maker can judge the scale.
Translate the finding into a test
If the evidence suggests a problem on a landing page, define what you will change and what outcome you expect to observe. Keep other factors as stable as practical. Record the start date, review period, and conditions that would make the comparison unreliable.
A useful analysis ends with a bounded next step, not a confident story. Sometimes that step is fixing measurement before changing the campaign.
Keep a decision log
Save the question, source report, verified calculations, assumptions, and action taken. When the next report arrives, compare what happened with what was expected. This makes AI assistance part of an accountable learning process.
Explore AGM’s growth and analytics capabilities to connect measurement with a clear business objective.
Prepared with AI assistance for AGM Insights. Examples are illustrative and are not reported client results.
