Marketing & Content
Paid media performance analysis
Analyse spend, impression, click, and conversion data across paid channels to identify budget allocation opportunities and creative performance patterns. Summarises into a structured weekly report.
10–20%
improvement in return on ad spend through better allocation
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.
Very low risk. Fully internal use. A person checks everything before it goes further. Worst case is a minor internal inconvenience.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
Multi-channel data aggregation is the main technical challenge — data from Meta, Google, TikTok, and affiliates sits in different platforms with different attribution models.
Attribution is inherently contested. The AI can summarise what the data shows within each platform's model; it cannot reconcile competing attribution claims across platforms without a defined methodology.
The analysis is most valuable when focused on a specific decision: 'should we shift budget from Meta to Google this week?' rather than 'tell me how performance is going'.
Last-click attribution, which most ad platforms default to, systematically overstates the contribution of lower-funnel channels and understates upper-funnel activity. Be explicit with the AI about which attribution approach to use and why.
Experiment starter: Export one month of cross-channel paid data into a single structured file. Prompt an LLM to identify the three largest efficiency gaps (channels or campaigns significantly above or below average ROAS) and draft a one-page reallocation rationale. Test the recommendation in the following month and compare outcomes.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — RAG and Knowledge Systems: Unlocking Proprietary DataThis is an decision support opportunity — see the relevant playbook section for how to approach it.More in Marketing & Content
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Campaign performance commentary
Convert raw campaign data into narrative summaries for weekly reports and post-campaign reviews. AI drafts; analyst reviews and adds context.
AI Transformation Playbook
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