The Scenario
You are a sales ops analyst. Your team spent six weeks scraping 500 target company profiles from LinkedIn using PhantomBuster. The company objects are all sitting in PhantomBuster org storage, with employee count, industry, and HQ location attached.
The next outbound push starts Monday. Your SDR manager needs every company scored against the ICP before Friday, and the ICP scoring model lives in an Excel workbook with formulas in columns G through J that run on employee count and industry.
The bad version of this week:
- You export PhantomBuster company data from the dashboard as a CSV
- The CSV has 500 rows but the column headers use internal API names, not human-readable labels
- You open the CSV in Excel and spend an hour renaming columns to match your scoring workbook
- You paste the data into the Scoring tab and discover 90 rows have employee count stored as a text range, which breaks the formula in column G
- You fix the ranges manually, row by row, switching between number formats
- You finish Thursday night with the data mostly working but five rows still showing formula errors you cannot track down.
The fast version is one prompt.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Excel workbook that queries PhantomBuster company storage directly, not from a CSV export, and writes the fields into the columns your scoring formulas already expect.
Open the SheetXAI sidebar and type:
Search all PhantomBuster company objects and write company name, LinkedIn URL, website, industry, employee count, and HQ location into columns A through F of this workbook.
SheetXAI calls PhantomBuster's company storage API, paginates through all 500 objects, and writes each one into the workbook in the exact columns you specified. No column renaming, no CSV import, no header reconciliation.
What You Get
A populated workbook tab with all 500 PhantomBuster company objects in scoring-ready columns:
- Column A — company name
- Column B — LinkedIn company URL
- Column C — website
- Column D — industry
- Column E — employee count
- Column F — HQ location
The ICP scoring formulas in columns G through J run immediately. No format adjustments, no manual column renaming.
What If the Data Is Not Quite Ready
Company data from LinkedIn scrapers often has gaps. SheetXAI handles them in the same prompt.
When employee count is stored as a text range
PhantomBuster scraped the LinkedIn "company size" field, which returns ranges like "51-200 employees" instead of a number.
Write all PhantomBuster company objects into columns A through F of this workbook. In column E, convert employee count ranges to midpoint numbers: "51-200" becomes 125, "201-500" becomes 350, "501-1000" becomes 750. Write the numeric midpoint so the ICP scoring formulas can run without errors.
When some companies are missing an industry tag
A batch of 40 companies were scraped without the industry field populated. The scoring formula will error on those rows.
Write all PhantomBuster company objects into columns A through F. For any row where the industry field is blank in column D, write MISSING INDUSTRY in column D and flag the row with an X in column G so the team can classify them before scoring.
When you only want companies in specific industries
The ICP is narrower than the full scrape. You only want SaaS, fintech, and healthtech targets.
Search all PhantomBuster company objects and filter to companies where industry contains "software," "fintech," "financial technology," or "health technology." Write name, LinkedIn URL, website, industry, employee count, and HQ location into columns A through F of this workbook.
When you need to pull, clean, score, and sort in one go before Friday morning
The SDR manager needs the scored workbook by 9 AM Friday. No more iterations.
Search all PhantomBuster company objects and write name, LinkedIn URL, website, industry, employee count, and HQ location into columns A through F. Convert employee count ranges to numeric midpoints in column E. Flag rows with missing industry in column G as MISSING INDUSTRY. In column H, apply a preliminary ICP score: "Tier 1" if employee count is 51-500 and industry is software or fintech, "Tier 2" if employee count is 501-2000, "Tier 3" for everything else. Sort the workbook by ICP score, Tier 1 first.
The pattern: one prompt pulls the data, fixes the formats, and applies the preliminary score. The SDR team gets a ready-to-use file, not a raw export.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook, then ask it to pull your PhantomBuster company objects into the workbook. The PhantomBuster integration is included in every SheetXAI plan. For related workflows, see how to push target accounts into PhantomBuster company storage or the PhantomBuster in Excel overview.