01
Step 1
Gather matched-period sessions, add-to-cart and checkout events, orders, and device or channel splits for each page in scope, using one session definition and one comparison window for every page compared.
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Spark playbook scenarios · Spark: AI Store Assistant
Compare conversion performance across product pages
Traffic arrives while orders lag, and the funnel stage responsible is guessed at rather than located in the data. In this case, the immediate task is to compare conversion performance across product pages. The workflow should produce a conversion diagnosis locating the largest evidenced drop, together with an experiment backlog and guardrail metrics.
Topic
Product Performance Analytics
Target keyword
shopify product conversion report
Copyable AI prompt
A prompt you can drop directly into Spark
First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.
ai-prompt.txt
Act as a Shopify operations specialist.
Help me compare conversion performance across product pages.
My store context is [describe the business, products and target audience].
My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [matched-period sessions, add-to-cart and checkout events, orders, device and channel splits for the pages in scope, and the shared funnel event definitions]. Compute each page's funnel pass-through under the shared definitions, locate the stage with the largest evidenced drop rather than the stage with the largest absolute count, and mark pages whose traffic is below the level needed for a stable rate.
Return a conversion diagnosis with an experiment backlog and guardrail metrics, verified against those records for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.How to solve
A recommended resolution workflow
Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
02
Step 2
Compute each page's funnel pass-through under the shared definitions, locate the stage with the largest evidenced drop rather than the stage with the largest absolute count, and mark pages whose traffic is below the level needed for a stable rate.
03
Step 3
Deliver a conversion diagnosis naming the located drop per page, an experiment backlog ordered by evidenced loss and feasibility, and guardrail metrics for each experiment, then verify the event counts against the platform records.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Why compare matched periods rather than totals?
Campaign mix and seasonality move the overall rate, so unmatched windows can hide a real drop or manufacture one. Matching the window for every page compared isolates the page factor from the traffic-mix factor.
Should every page be tested at once?
No. Prioritise the pages where the evidence shows the largest loss and where the change is feasible to ship, because simultaneous experiments on shared traffic interfere with each other's readings.
What makes an experiment result trustworthy?
A success metric and a guardrail metric named before launch, plus enough elapsed time for the page's recorded traffic to be representative. Results read before the traffic minimum is met reflect noise rather than the change being tested.
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