TikTok reports land in the account's own currency and time zone, which shifts figures away from the store's calendar. In this case, the immediate task is to consolidate TikTok advertising performance data. The workflow should produce a unified reporting model and discrepancy log listing every unmatched or rescheduled figure.
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 consolidate TikTok advertising performance data.
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: [TikTok campaign and ad group exports, the advertising account's currency and timezone, the store's reporting calendar and currency, the conversion columns each export provides, and store-side order records for the same periods]. Normalise every export onto the store's reporting calendar by shifting timestamps into the store timezone and reassigning period boundaries, keep any conversion column the export does not provide as unavailable rather than substituting a store value, and record the exchange rate and rate date used for each currency conversion.
Return a unified reporting model and discrepancy log listing every unmatched or rescheduled figure 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 the campaign and ad group exports, the currency and timezone the advertising account reports in, the store's own reporting calendar and currency, and the conversion columns each export actually provides alongside the store-side order records for the same periods.
Normalise every export onto the store's reporting calendar by shifting timestamps into the store timezone and reassigning period boundaries, keep any conversion column the export does not provide as unavailable rather than substituting a store value, and record the exchange rate and rate date used for each currency conversion.
Deliver the unified reporting model and discrepancy log listing every unmatched or rescheduled figure with the rule that produced it, then verify that shifted totals equal the original export totals, that each converted amount names its rate and that every rescheduled figure states both the original and the reassigned period.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
The account reports in its own timezone, so an order near midnight can belong to a different day than the store records. Shifting timestamps into the store timezone before joining keeps daily totals honest, and each moved figure is logged with both dates.
Marked unavailable in the panel and noted in the log, never filled with a store-side value. A substituted figure looks complete while quietly changing the definition, which is exactly the difference this comparison exists to expose.
Only once periods, timezone and currency are aligned, because each platform credits the conversions it can observe. Until then, channel figures are shown side by side and their sum is described as overlapping claims rather than total demand.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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