Merchandising & Buying
Promotional effectiveness analysis
Assess which promotional mechanics, depths, and durations delivered the best uplift relative to baseline. Informs future promotional calendar planning.
10–20%
improvement in promotional return on investment
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
Isolating true promotional uplift requires a baseline — what would sales have been without the promotion? This is a methodological question that needs to be resolved before the AI can help.
Promotional cannibalisation (customers who would have paid full price buying on promotion) is often underestimated and rarely captured in headline uplift metrics. The analysis must account for this.
Start with promotions that ran in isolation (not concurrent with other activity) where cause and effect are cleaner before attempting to analyse overlapping activity.
The most useful output is a ranking of promotional mechanics by efficiency (uplift per point of margin given up), not just total revenue generated. This reframes the conversation from 'did it work?' to 'was it the most efficient use of margin?'
Experiment starter: Pull data for the last 6–8 comparable promotions (same category, similar mechanics). Prompt an LLM to calculate uplift against baseline, cost-of-promotion, and net margin impact for each. Share the ranking with the trading team and test whether it changes how they evaluate the next promotional calendar.
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 Merchandising & Buying
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Generate first-draft product descriptions from attributes, imagery, and brand guidelines. Buying team reviews, edits, and approves before publish.
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Range review analysis
Summarise sales performance, markdown rates, and return data by category to surface underperformers and inform ranging decisions.
AI Transformation Playbook
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