The Scenario
The media buyer on your mobile team sent you a Slack message at 8 AM: "I need carrier breakdown for the US app users before the 10 AM targeting call. IPs are in the usual sheet." The "usual sheet" is a Google Sheet with 5,000 app user IP addresses in column A. You need carrier name, MCC, and MNC for each one, plus a count-per-carrier summary to share in the call.
You've done this before. Last quarter someone built a Python script that hit the IPinfo carrier endpoint, but the script lives on a laptop you no longer have access to. You're starting from zero with 90 minutes on the clock.
The bad version:
- Re-build the API call script: import requests, handle auth, loop 5,000 rows, extract carrier name, MCC, MNC from the JSON, write to CSV.
- Realize the API rate limit means you need to batch or slow the calls — add retry logic.
- Import the CSV back into the sheet, align the columns with the existing data, build the summary table.
None of that is your job. You're supposed to be analyzing which carriers convert — not rebuilding infrastructure that should already exist.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Google Sheet. It reads your IP list and calls the IPinfo carrier endpoint through its built-in integration, writing carrier name, MCC, and MNC directly to your columns.
Paste this into the SheetXAI sidebar:
Look up carrier info for all IPs in column A using IPinfo and write carrier name, MCC, and MNC to columns B, C, and D, then add a count-per-carrier summary table below the data.
What You Get
- Column B: carrier name (T-Mobile, Verizon, AT&T, Vodafone, etc.) or blank for non-mobile IPs
- Column C: MCC (mobile country code) — blank for non-mobile IPs
- Column D: MNC (mobile network code) — blank for non-mobile IPs
- A summary table starting below the data: carrier name in one column, IP count in the next, sorted by count descending
- Rows where no carrier data is available (WiFi, desktop, datacenter) are written with empty carrier fields, not errors
What If the Data Is Not Quite Ready
I need to split mobile vs. non-mobile before the summary
Look up carrier info for all IPs in column A using IPinfo. Write carrier name, MCC, and MNC to columns B, C, and D. Add column E with the label "Mobile" if MCC is present and "Non-Mobile" if it is not. Then write two summary tables below the data: one with mobile IP count per carrier, one with the total non-mobile count.
Some IPs are WiFi and I want to exclude them from carrier analysis
Enrich all IPs in column A with IPinfo carrier data. Write carrier name to column B and MCC to column C. Filter out any row where MCC is blank (non-mobile IPs) and write only the mobile rows to a new tab called "Mobile Carriers," then add a carrier count summary at the bottom of that tab.
I want to map carriers back to ad networks for the targeting brief
Look up carrier info for all IPs in column A with IPinfo, write carrier name to column B, then use VLOOKUP to match carrier name against the mapping table in Sheet2 column A (carrier name) and column B (ad network group) and write the ad network group to column C.
Full analysis pipeline in one shot
Column A has 5,000 app user IPs. Look up carrier name, MCC, and MNC with IPinfo and write to columns B, C, D. Flag column E as "Mobile" or "Non-Mobile" based on MCC presence. Write a count-per-carrier summary for mobile IPs to a new tab called "Carrier Targeting Brief," sorted by count descending, and include the percentage of total mobile IPs for each carrier.
Asking for the segmentation and the summary together saves a separate round of work after the enrichment lands.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet with a list of mobile app user IPs, then ask it to pull carrier data from IPinfo and build the targeting summary. See also geographic traffic distribution or privacy-flag screening. Hub: IPinfo + Google Sheets.