The Scenario
You are a B2B sales rep and you just inherited a list of 50 target accounts from the SDR who left last month. The workbook has company names in column A and nothing else. Your manager wants the list enriched with LinkedIn URL, company size tier, industry vertical, and main product category before you start outreach — ideally by end of week.
The bad version:
- Search the first company on LinkedIn, find the company page, copy the URL into column B, note the employee count, convert it to a size tier, paste it into column C.
- Open a new tab, look up the company's industry, decide which vertical it belongs to, paste into column D.
- Skim the company's website to understand the product category and paste into column E.
- Repeat 49 more times, across 50 browser tabs, over the course of a day you should have spent on calls.
You are not being paid to do data entry on accounts you have not yet qualified. Every hour spent enriching by hand is an hour not spent selling.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the company names, and through its built-in Parallel integration it runs web research on each row and writes the enriched fields back directly.
For every company name in column A of this workbook, use Parallel to find and fill in the LinkedIn company page URL into column B, the company size tier (1-10, 11-50, 51-200, 201-500, 500+) into column C, the industry vertical into column D, and the main product category into column E.
What You Get
- Column B filled with LinkedIn company page URLs for each account.
- Column C showing a size tier bucket based on Parallel's web research.
- Column D with the industry vertical Parallel identified (e.g., SaaS, Fintech, Healthcare IT).
- Column E containing the main product category (e.g., Sales Enablement, HR Software, Payments).
- Blank cells where Parallel could not confidently resolve a field, so you know which accounts need a manual check.
What If the Data Is Not Quite Ready
Some company names are ambiguous — same name used by multiple companies in different industries
For every company name in column A, check whether the name is ambiguous (used by multiple unrelated companies). If it is, write Ambiguous in column F and skip the enrichment for that row. For all unambiguous names, use Parallel to find the LinkedIn URL (column B), company size tier (column C), industry vertical (column D), and main product category (column E).
You have both company name and domain in the workbook and want to use both for disambiguation
For every row, use both the company name in column A and the website domain in column B as inputs for the Parallel enrichment. Write the LinkedIn company page URL into column C, company size tier into column D, industry vertical into column E, and main product category into column F. Where the domain disambiguates the name, prefer the domain-matched result.
You only want to enrich rows where column C is currently blank
For every row where column C is blank, use the company name in column A as input for a Parallel enrichment task. Write the LinkedIn URL into column B, company size tier into column C, industry vertical into column D, and main product category into column E. Leave rows where column C already has a value untouched.
You want enrichment plus ICP scoring in one pass
For every company name in column A, use Parallel to enrich each row with LinkedIn URL (column B), company size tier (column C), industry vertical (column D), and main product category (column E). Then, based on those values, score each company as ICP-Fit, Borderline, or Not-Fit using these criteria: ICP-Fit if size tier is 51-200 or 201-500 and industry is SaaS or Fintech; Not-Fit if size is 1-10; otherwise Borderline. Write the score into column F.
The pattern: enrichment and qualification logic in one prompt — so the workbook is ready to hand off, not just filled in.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook with a list of target company names, then ask it to enrich each row with LinkedIn, size, industry, and product data using Parallel. You can also look at how to build a competitive intelligence matrix or return to the Parallel overview.