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.
50–70%
reduction in reporting time
Use case assessment
Negligible commercial impact. Saves time at the margins but won't move the needle on revenue or profit.
Very easy to implement. Ready-to-use tools exist. Can be up and running in days or weeks with minimal technical resource.
Light data requirements. Uses straightforward inputs (documents, product descriptions, customer records) that are usually accessible with minimal prep.
Very low risk. Fully internal use. A person checks everything before it goes further. Worst case is a minor internal inconvenience.
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
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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