Marketing & Content

Campaign performance commentary

Convert raw campaign data into narrative summaries for weekly reports and post-campaign reviews. AI drafts; analyst reviews and adds context.

AugmentationPattern-matchingTime saving

50–70%

reduction in reporting time

Opportunity assessment

Business Impact
1

Negligible commercial impact. Saves time at the margins but won't move the needle on revenue or profit.

Feasibility
5

Very easy to implement. Ready-to-use tools exist. Can be up and running in days or weeks with minimal technical resource.

Data Readiness
4

Light data requirements. Uses straightforward inputs — documents, product descriptions, customer records — that are usually accessible with minimal prep.

Risk Exposure
5

Very low risk. Fully internal use. A person checks everything before it goes further. Worst case is a minor internal inconvenience.

Change Complexity
5

Very easy to absorb. Runs quietly in the background or gives people a helpful new input. Makes everyday work easier with almost no friction.

Tooling required

Standard LLM

Things to consider

  • Structured data export (CSV, JSON) feeds better AI commentary than copy-pasted tables — standardise the data format the AI receives.

  • Define what 'good commentary' looks like: which metrics matter, what language to use, what context to include. Give examples in the prompt.

  • The analyst still adds the 'so what' — the AI produces the description, the human adds the strategic interpretation and recommendation.

  • The most time is saved on the data-to-narrative step, not on strategic framing. Be clear with stakeholders that AI writes the description of what happened; the analyst writes the recommendation.

  • Experiment starter: For the next three weekly campaign reports, have the analyst generate the first-draft commentary from a structured data export using an LLM. Compare time spent against the previous three weeks and gather feedback on whether output quality changed.

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