Step 1
Assemble spend, conversion and order figures for every channel over one dated period, keeping the period boundaries, currency and counting rule identical across channels.
Channel figures come from different periods and different definitions, so no side-by-side reading of them holds up. In this case, the immediate task is to compare marketing channel outcomes and tradeoffs. The workflow should produce a comparison over one dated period that states the tradeoffs and the attribution limits behind each channel's numbers.
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 compare marketing channel outcomes and tradeoffs.
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: [spend, conversion and order figures for every channel over one dated period with identical boundaries, currency and counting rule]. Judge the channels on the tradeoff those figures actually show, comparing only channels placed at the same stage and keeping separate any channel whose figures came from a window that could not be matched to the shared period.
Return the comparison with the return report 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.
Assemble spend, conversion and order figures for every channel over one dated period, keeping the period boundaries, currency and counting rule identical across channels.
Judge the channels on the tradeoff those figures actually show, comparing only channels placed at the same stage and keeping separate any channel whose figures came from a window that could not be matched to the shared period.
Deliver the comparison with each channel's attribution limits stated beside its numbers, then verify the definitions and period against the supplied records and note where a restatement would change the ordering.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
Rarely, because spend, return and reach describe different things. State which tradeoff the ranking reflects and put the supporting figures beside it, so a reader can see what was given up to reach the top position.
Keep them apart where possible, since brand terms capture demand created elsewhere. Placing both in one table credits brand search with sales that earlier channels produced, which distorts both rows.
Only if the periods and definitions match as well. Equal spend under different windows or value bases still mixes several variables, so the result cannot be attributed to the channels themselves.
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.