E-commerce & Digital
Catalogue gap identification
Cross-reference search queries that return no results or poor results against the live product catalogue. Identifies what customers are looking for that the business doesn't stock.
3–8%
uplift in search-to-purchase conversion from gap closure
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.
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.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
Zero-results searches are the most direct signal of a gap. High-frequency zero-results terms that map to a consistent product type are the most actionable, as they indicate demand that could be captured.
Not every search gap is a buying opportunity — some terms represent customer confusion (wrong terminology) rather than a genuine range gap. Human interpretation is needed to distinguish the two.
Catalogue data must be current. A gap identified because a product is listed under a different name is a taxonomy or SEO problem, not a buying one.
The output of this analysis should be a structured brief for the buying team, not just a list of terms. For each significant gap, include estimated demand volume and the closest current product alternatives so buyers can make an informed ranging decision.
Experiment starter: Export one month of search log data and filter for queries with zero results or a results count below 3. Prompt an LLM to group similar queries and identify the 10 most frequent product-type gaps. Share the list with the buying team and identify which gaps represent genuine commercial opportunities vs. SEO or taxonomy issues.
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 E-commerce & Digital
Product categorisation at scale
Automatically assign products to the correct site taxonomy categories using title, description, and attributes. Removes a bottleneck on new product onboarding.
Site search query analysis
Analyse search queries for intent, gaps, and failed searches. Identify where customers can't find what they want and prioritise catalogue improvements.
A/B test copy generation
Generate multiple headline, CTA, and banner copy variants for testing. Covers different value propositions, tones, and lengths with a single brief.
AI Transformation Playbook
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