E-commerce & Digital
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.
5–15%
uplift in search-to-purchase conversion
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.
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
Zenni Optical's AI-powered search drove a 44% increase in search traffic, 34% increase in search revenue, 27% increase in revenue per session, and 9% uplift in conversion (Retail TouchPoints).
Search log data is usually available but often large and noisy — define the analysis cadence (weekly, monthly) and what questions you're asking before building.
The real value is in acting on the insights: failed searches that map to gaps in the range need a buying response, not just a search tuning response.
Specialist tools (Algolia, Klevu, Constructor) may deliver faster time-to-value than a custom build, particularly for smaller teams.
Failed searches — queries returning zero or very poor results — are the highest-value starting point. They represent customers who could not find what they wanted and are a direct revenue leak.
Experiment starter: Export one month of search logs. Use an LLM to categorise queries by intent and flag those with high frequency and poor results. Present the top 10 failed search categories to the buying and trading teams. Measure whether actioning them improves conversion on those terms within 60 days.
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.
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.
Returns prediction
Score orders by likelihood of return based on product category, customer history, order composition, and fulfilment signals. Enables proactive intervention.
AI Transformation Playbook
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