The Scenario
Your sales team closed the quarter with an Excel workbook full of 300 leads pulled from three different sources. Column A has first name, column B has last name, column C has company name. That's it. No LinkedIn URLs, no job titles, no company size, no industry — just names and logos.
The bad version:
- Open each name in Datagma's web UI, run the lookup manually, copy the LinkedIn URL, paste it into column D, repeat 300 times across your OutreachList table
- Export the workbook to CSV, run the enrichment against the CSV, download the results file, re-import it, fix the column offset where the import landed one column to the right of where it should
- Hand the workbook to an SDR and ask them to enrich "when they get a chance" — which means it comes back two weeks later, half-done, with inconsistent field formats
This is supposed to be a qualified lead list, not a weekend project. The SDRs are on quota. Nobody budgeted a data entry sprint into this quarter.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the data, understands the layout, and through its built-in Datagma integration, it handles the enrichment calls and writes the results back into the workbook for you. No API setup, no CSV export loop.
For each row in my lead list (name in column A, company in column B), call Datagma to enrich the contact and write LinkedIn URL, job title, and company size into columns C, D, and E
What You Get
- Column C: LinkedIn profile URL for each contact, where Datagma found a match
- Column D: current job title as Datagma returns it
- Column E: company headcount range
- Rows where Datagma returned no result are marked with a note in column F so you can triage them separately — not silently left blank
What If the Data Is Not Quite Ready
The name formatting is inconsistent
Some rows have "John Smith" in column A, others have "JOHN SMITH" or "john smith." Datagma's matching is case-insensitive, but mixed formats still affect confidence scores.
Normalize the name formatting in column A to Title Case before calling Datagma, then enrich each row and write LinkedIn URL, job title, and company size into columns C, D, and E
No company name — only a domain
You have the company domain in column B but not the full company name. Datagma's company enrichment works from domains.
For each row where column B contains a domain (not a company name), use Datagma's domain-based enrichment to find the contact and write LinkedIn URL, job title, and company size into columns C, D, and E
Some rows already have a LinkedIn URL
You ran a partial enrichment previously and 80 rows already have data in column C. You only want to enrich the gaps.
Enrich only the rows in my lead list where column C is empty — use name in column A and company in column B as inputs to Datagma, and write LinkedIn URL, job title, and company size into columns C, D, and E
Full cleanup and enrichment in one pass
The workbook has mixed name formats, some missing company names, and 80 already-enriched rows. You want it all handled in one go.
Normalize name formatting in column A, skip rows where column C already has a LinkedIn URL, use Datagma to enrich the remaining rows using name and company, and write LinkedIn URL, job title, and company size into columns C, D, and E — flag any row where Datagma returned no result in column F
One prompt handles the conditional logic so you don't have to pre-clean and then re-run.
Try It
If you have a lead list in Excel with names and companies but no enrichment data, open it and get the 7-day free trial of SheetXAI. Ask it to enrich with Datagma and you'll have LinkedIn URLs and job titles written back within minutes. For related workflows, see how to find verified work emails or detect job changes using the same setup.