Clicks arrive but orders do not, and the landing pages, session records, checkout steps and conversion definitions in use are available to trace. In this case, the immediate task is to diagnose why advertising clicks are not converting. The workflow should name the limiting stage and the checks that would confirm it.
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.
Act as a Shopify operations specialist.
Help me diagnose why advertising clicks are not converting.
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: [clicks and sessions per campaign with their landing page, on-page events and checkout step completions for the same sessions, the conversion definition and attribution window in use, and each export's currency and date range]. Walk the path from click to purchase and locate the stage where the largest share of sessions stops, attributing the loss to intent when click-through is healthy but landing page engagement is not, to the page when engaged sessions fail to add to cart, and to checkout when carts reach payment and stop.
Return a diagnosis naming the limiting stage, its evidence and the checks that would confirm it 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.Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Collect clicks and sessions per campaign with their landing page, the on-page events and checkout step completions belonging to the same sessions, the conversion definition and attribution window in use, and the currency and date range of each export.
Walk the path from click to purchase and locate the stage where the largest share of sessions stops, attributing the loss to intent when click-through is healthy but landing page engagement is not, to the page when engaged sessions fail to add to cart, and to checkout when carts reach payment and stop.
Deliver a diagnosis naming the limiting stage with the session counts behind it and the checks that would confirm it, then reconcile click counts with session counts for the same window and note any gap caused by tracking that did not fire.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
By comparing counts at each consecutive step for the same sessions, so the stage with the largest drop is named from numbers; a stage where counts barely move cannot be the constraint, whatever the team believes about it.
A click count higher than recorded sessions usually means pages were not tagged or sessions were dropped, and diagnosing conversion on those numbers would blame a page for a tracking gap the store created.
No, fix the named stage first and re-measure the same window, because changing several stages together removes the baseline and leaves no way to tell which change produced the improvement.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.