E-commerce & Digital
Conversion funnel drop-off analysis
Analyse where customers exit across the purchase funnel — product page, basket, checkout — and summarise patterns with hypothesis suggestions. Prioritises where CRO effort should focus.
5–15%
improvement in checkout completion rate
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
Analytics data quality is the prerequisite — if your funnel tracking has gaps (missing events, inconsistent naming) the analysis will reflect those gaps. Audit your tracking before feeding data to the AI.
The AI identifies patterns in the data; it cannot explain why customers are dropping off. Pair the quantitative analysis with qualitative signals (heatmaps, session recordings, customer feedback) before forming hypotheses.
Funnel analysis is most useful when segmented — by device, by traffic source, by customer cohort. An aggregate view can be misleading if different customer segments have very different journeys.
The output should be a prioritised hypothesis list for the CRO team, not a list of every observed drop-off. Ask the AI to rank opportunities by estimated impact and ease of testing to make the prioritisation decision easier.
Experiment starter: Export three months of funnel analytics data. Prompt an LLM to identify the three stages with the highest drop-off rate, calculate the revenue impact of a 10% improvement at each stage, and propose one testable hypothesis for each. Use the output to structure the next CRO sprint and measure whether the hypothesis-to-test conversion rate improves versus the team's previous approach.
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
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