The Scenario
A sales ops analyst is setting up a LinkedIn Ads campaign targeting audience by industry. They exported 60 account records from the CRM, each with a free-text industry field — things like "software development," "financial services consulting," "retail grocery," and "B2B manufacturing." LinkedIn Ads requires official industry codes for audience targeting, and the CRM values don't match LinkedIn's taxonomy.
The analyst has one afternoon to clean the list before handing it to the demand gen team, who need the codes in column B to configure the campaign audience before the campaign goes live tomorrow morning.
The bad version:
- Open LinkedIn's industry code documentation or Campaign Manager, find the search interface, type the first industry label, and identify the best-matching official code
- Switch to the sheet, type the code into column B, go back to the lookup tool, clear the search, type the next label
- Reach "financial services consulting" and find that LinkedIn has three plausible codes — "Financial Services," "Investment Management," and "Banking" — and the right one depends on context the CRM record doesn't provide
Sixty lookups, several with genuine ambiguity, all of which have to be right before tomorrow morning's handoff.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads your industry labels, understands the mapping task, and through its built-in Piloterr integration it can look up each label against LinkedIn's official industry code list and write the matched code and official name back into the sheet.
For each industry label in column A (60 rows), search Piloterr's LinkedIn industry list for the best match and write the industry code into column B
What You Get
- Column B: the official LinkedIn industry code for each row (numeric code, e.g., 4 for Accounting)
- Column C: the official LinkedIn industry name corresponding to the code, so the demand gen team can verify the match without cross-referencing a separate list
- Rows where the label is too ambiguous for a clear match get flagged in column D with the two or three closest candidates so a human can make the call quickly
What If the Data Is Not Quite Ready
Some industry labels in column A are compound phrases that span two LinkedIn categories
Read column A (60 industry labels) — for labels that contain two distinct industry signals (e.g., "financial services consulting"), break them into their two most likely LinkedIn codes and write both codes and names into columns B and D respectively, noting "split match" in column E
You want to validate that all matched codes are from LinkedIn's current active taxonomy before handing off
For each industry label in column A, look up the best match via Piloterr's LinkedIn industry codes, write the code into column B and name into column C — add a column D flag "check" for any code that is numeric but not in the range 1-155 (LinkedIn's documented range)
Column A has 12 rows that are blank (accounts where the CRM had no industry value)
Read column A (60 rows) — skip blank rows and mark them "missing" in column B; for the remaining rows, search Piloterr's LinkedIn industry list and write the matched code and name into columns B and C
You need clean labels, matched codes, ambiguity flags, and a count of "missing" rows all in one pass
Read column A (60 rows), mark blanks as "missing" in column B, for non-blank rows look up the best LinkedIn industry code via Piloterr and write code into column B and name into column C, flag ambiguous matches in column D, and add a summary row at the bottom showing how many rows are clean, flagged, and missing
The demand gen team gets a list they can use without reviewing every row.
Try It
Get the 7-day free trial of SheetXAI and open the Google Sheet where your CRM industry labels live — then ask SheetXAI to look up each one against LinkedIn's industry taxonomy via Piloterr and write the codes back. Also see how SheetXAI handles bulk Google keyword searches via Piloterr or the full Piloterr integration hub.