The Scenario
A sales ops analyst is setting up a LinkedIn Ads campaign targeting by industry. They exported 50 account records from the CRM into an Excel workbook, 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, find the search interface, type the first industry label, and identify the best-matching official code
- Switch to the workbook, 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 — and the right one depends on context the CRM record doesn't provide
Fifty 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 Excel workbook. 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 workbook.
Read the Excel column 'Industry' (50 rows of free-text labels), look up each against Piloterr's LinkedIn industry codes, and write the matched code and official name into columns B and C
What You Get
- Column B: the official LinkedIn industry code for each row
- 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
- Column D: flagged rows where the label is too ambiguous for a clear match, with the two or three closest candidates noted so a human can make the call quickly
What If the Data Is Not Quite Ready
Some industry labels in the 'Industry' column are compound phrases that span two LinkedIn categories
Read the 'Industry' worksheet column (50 free-text labels) — for labels that contain two distinct industry signals, write both best-matching LinkedIn 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
For each industry label in the 'Industry' column, 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 not in the documented valid range
Column A has rows that are blank where the CRM had no industry value
Read the 'Industry' worksheet column (50 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 the 'Industry' worksheet column (50 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 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 Excel workbook 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.