The Scenario
A new product manager left you a ticket at the end of the day: "We need to know which language and regional background our customers skew toward before we finalize the localization roadmap." You're a localization manager at an e-commerce company. The customer list in Google Sheets has 20,000 names. The product roadmap review is Monday morning.
Nobody on the team has a demographic enrichment subscription. The CRM doesn't capture language preference. Surveying 20,000 customers before Monday isn't a real option.
The bad version:
- Try to infer origin from the email domain — which tells you ".de" for Germany but nothing for Gmail users, which is 60% of the list.
- Use a free name-origin tool that requires uploading a CSV and emailing a link — which doesn't scale to 20,000 rows and returns results in a format you'd have to clean before use.
- Pull a sample of 200 names, run them manually, and extrapolate — then explain to the PM why the localization decision is based on 1% of the customer base.
The product roadmap affects which languages get developer resources for the next two quarters.
The Easy Way: One Prompt in SheetXAI
SheetXAI reads your Google Sheet and calls Interzoid's name origin API for every customer row — appending regional background estimates without you touching a single formula.
For each name in column A, use Interzoid to detect the likely country or region of origin and write the result to column B.
What You Get
- Column B: estimated origin region per customer name (e.g., Western European, East Asian, South Asian, Latin American).
- A dataset ready for pivot analysis — how many customers by origin region, which regions are underrepresented in the current product experience.
- Rows where Interzoid returned low confidence flagged for exclusion from the regional count.
What If the Data Is Not Quite Ready
Column A has only first names — you want to work with first name for origin inference
Call Interzoid's name origin API for every first name in column B and write the inferred origin region to column C, then add a summary pivot counting customers by origin region.
Some rows have company names instead of personal names — those should be skipped
For each entry in column A, first classify whether it looks like a personal name or a company name. If it looks like a company, write 'SKIP' to column B. If it looks like a personal name, call Interzoid's name origin API and write the detected region to column B.
You want to weight the results by revenue so high-value segments are visible
After writing origin regions to column B, create a summary sheet showing average order value from column C grouped by origin region in column B, sorted by average order value descending.
Full enrichment + regional analysis in one shot
For each name in column A, call Interzoid's name origin API and write the detected region to column B. Flag low-confidence results as 'LOW CONFIDENCE' in column C. Then create a summary sheet with a count of customers per region and the average revenue from column D per region, sorted by revenue descending. Skip rows with 'SKIP' or 'LOW CONFIDENCE' in the summary count.
The product roadmap meeting now has actual data behind the localization priority list.
Try It
Get the 7-day free trial of SheetXAI and open your customer list — ask SheetXAI to append Interzoid name-origin data to column A before your localization roadmap review. Then see the spoke on detecting the language of survey responses, or the full Interzoid integration overview.