MailchimpStart with the sources
- List every place client data lives: ad accounts, the store, the analytics tool, shared sheets and report files.
- For each number, mark which source is the record. Write the answer in the definitions sheet.
Clean the three things that break first
- Names: one naming rule for campaigns, ad sets and ads, with a registry for every new name.
- Tags and UTMs: one fixed list of values for each field, checked before a link goes live.
- Definitions: one meaning for each metric, such as what counts as an order and what counts as a new customer.
Make cleaning a routine
- Once a month, check for duplicate names, empty required fields and old exports still in use.
- Archive old sheets instead of editing them, so the history stays readable.
- Log each cleanup with the date, the owner and the number of rows changed.
Decide what an AI tool may read
- Give an assistant only the sources the client has approved for that use.
- Keep personal customer data out of any sheet an assistant can read, unless the client has agreed to it in writing.
Made-up scenario: a made-up store runs one test under three campaign names, typed three different ways. An assistant reports three small tests. The monthly cleanup maps the names in the registry, and the next summary shows one test with the right total.
