The Scenario
You are a CRM administrator at a Russian e-commerce company. The next marketing send is in three days. You have a Google Sheet with 5,000 customer records — names, phone numbers, emails, and delivery addresses — collected over the past two years from multiple intake forms.
The data is a mess. Addresses are free-form strings in varying formats. Phone numbers mix dashes, spaces, brackets, and missing country codes. Email addresses have obvious typos. The marketing platform rejects malformed records on import, and last quarter 412 records bounced at that step.
The bad version of this week:
- Export the sheet, send it to the dev team to write a cleaning script
- Wait two days for the script
- Get it back, find that it mishandled the address parser for three regions
- Fix the edge cases manually, row by row
- Import at 11 PM the night before the send
- 412 records still bounced because the phone format was wrong.
The fast version is one prompt and the sheet is clean before lunch.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your spreadsheet that reads the columns, coordinates multiple DaData endpoint calls per row, and writes the cleaned values back without you touching a script.
Open the SheetXAI sidebar and type:
For each row in my sheet, use DaData to clean the address in column A, standardize the phone in column B, and fix the email in column C. Write the cleaned address into column D, the normalized phone into column E, and the corrected email into column F. If DaData cannot parse an address, write "UNRESOLVED" in column D. If the phone is invalid, write "INVALID" in column E.
SheetXAI reads the columns, calls DaData's address cleaner, phone normalizer, and email validator for each row, and writes the results back. You get a fully cleaned sheet, with explicit flags on the records that need manual review, in one pass.
What You Get
Columns D, E, and F filled with standardized values across all 5,000 rows:
- Column D — DaData-standardized address in FIAS format
- Column E — phone normalized to +7XXXXXXXXXX, or "INVALID" if unparseable
- Column F — corrected email, or the original with "UNRESOLVED" if DaData could not fix it
Explicit flags instead of silent failures. Every record that DaData could not clean is marked, not silently dropped, so your review queue is a filter away rather than a surprise on import day.
Want to add a quality score column? Ask SheetXAI to write DaData's confidence score for each address into column G. Low-confidence addresses go to the manual review queue first.
What If the Data Is Not Quite Ready
Real CRM exports have more edge cases than the scenario above. SheetXAI handles them in the same prompt.
When addresses are split across two columns
The street address is in column A but the city is in a separate column B, and DaData needs a combined string to parse correctly.
Concatenate columns A and B into a single address string for each row before passing to DaData. Write the DaData-standardized full address into column C, and the parsed postal code, city, and street into columns D, E, and F separately.
When some rows are already clean
A subset of records were imported from a verified source last month and do not need re-cleaning. They have "VERIFIED" in column G.
Skip any row where column G says "VERIFIED." For all other rows, run DaData cleaning on columns A, B, and C and write results to columns D, E, and F.
When you only need to fix the phones before the send
The address data can wait, but the send platform rejects non-E.164 phones on Monday.
Normalize every phone number in column B using DaData and write the E.164 format into column E. Mark "INVALID" in column E if DaData cannot parse the number. Leave all other columns unchanged.
When you want cleanup plus a deliverability report in one shot
You need the cleaned data and a summary of how many records had each type of problem, so you can report back to the marketing team.
For each row, use DaData to clean the address in column A, normalize the phone in column B, and fix the email in column C. Write cleaned values to columns D, E, F. Then write a summary table below the data showing: total rows processed, number of unresolved addresses, number of invalid phones, number of corrected emails, and number of rows that had all three fields clean.
The pattern: instead of cleaning the sheet in three separate passes, you ask for all three endpoint calls and the summary in one prompt. SheetXAI coordinates the DaData calls, the column mapping, and the reporting inline.
Try It
Get the 7-day free trial of SheetXAI and open any sheet with Russian contact records, then ask it to run DaData cleaning on the columns you care about. The DaData integration is included in every SheetXAI plan. For related workflows, see how to standardize addresses and add geocoordinates or the DaData in Google Sheets overview.