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NORLA ACADEMY / RESOURCE DETAILS

Data-Driven Campaigns with AI

Norla Editorial3.8 hours$119 USD
$119 USDPay on this website — the lessons open on your account as soon as payment is recorded.
Buy this course · $119

A written sequence for preparing campaign data, comparing results fairly and producing a decision-oriented readout. The exercises keep the underlying definitions and calculations inspectable so AI-assisted explanation remains tied to the evidence rather than substituting for it.

What you will learn

  • Document the grain, source and definitions of campaign data
  • Build a comparison that exposes uncertainty and confounding changes
  • Write a source-linked readout with an actionable next question

Your learning path

Module 1 is free to read · buy the course to unlock all 3
01 / Prepare the reporting contract

Identify what one row represents and how the file was produced. Record the reporting window, time zone, filters and stable identifiers before calculating totals. The same word, such as conversion, may refer to different events in different sources, so the metric definition needs to be written rather than inferred from a column label.

Check required fields, unexpected values and duplicates at the intended grain. Keep unmatched or excluded records visible in a separate table. A clean-looking report that silently drops difficult cases can create more confidence than the data deserves.

Practice: Create a field dictionary and five quality checks for an approved campaign export. Record the action to take if each check fails.

Keep: A campaign data contract and quality checklist.

02 / Compare like with likeUnlocks after purchase
03 / Turn the analysis into a useful decisionUnlocks after purchase
CASE WORKSHOP / APPLY THE LESSON

Explain a campaign difference without overclaiming

Practice brief: A campaign export and an internal order summary disagree for a recent period. The files use different extraction times and treat canceled orders differently. Prepare a reporting contract, a reproducible comparison, and a management readout that distinguishes reconciliation findings from possible explanations of performance.

Work through the case

  1. Document row meaning, date fields, reporting windows, source timestamps, and the treatment of cancellations. Preserve the original measures before constructing a comparison that uses an explicit shared definition.
  2. Create a reconciliation table with matched groups, unresolved differences, and assumptions. Test whether a changed join or an incomplete period could explain a discrepancy before requesting a narrative summary.
  3. Write a concise readout with observations, hypotheses, decision relevance, and a next check. Link each consequential statement to the comparison and state which uncertainties could change the recommendation.

Review your result

  • The comparison does not silently equate attributed conversions with net orders. Any transformation is explained well enough for another analyst to reproduce.
  • The readout separates confirmed differences from causal speculation and makes extraction timing visible wherever it changes interpretation.
  • The proposed next check targets a decision-relevant uncertainty. It does not seek a favorable narrative or force all totals to agree through an unexplained adjustment.
Buy the course to download the workbookReturn to catalogue

NORLA EDITORIAL / FIELD NOTES

Continue exploring.

Working methods, decisions to document, and useful questions to take into your next project.

All field notes