The Scenario
A support engineer flagged something in the weekly ticket review: the same product component is showing up in complaint descriptions over and over, but nobody's been tracking it systematically. You manage customer success for a SaaS company, and you have 150 raw support ticket descriptions in column A of an Excel file that your support platform exports every Monday. The VP of Product wants a trend analysis by tomorrow's stand-up — which product names are appearing, which complaint categories, where the volume is concentrating.
The bad version:
- Export the ticket descriptions from the support platform to CSV, open it in your spreadsheet, and start reading through 150 rows manually to tag product names and complaint types.
- Create a tagging convention on the fly — decide whether "billing module" and "billing" count as the same thing, whether "slow" is a performance complaint or a UX complaint, and whether the product name in row 43 is the same feature called something different in row 98.
- Build a pivot table from your manual tags and realize your taxonomy drifted across 150 rows, so the categories don't aggregate cleanly.
The VP's stand-up is at 9 AM. The analysis needs to be in a form you can present, not a collection of notes from reading support tickets at midnight.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the ticket descriptions, understands the column structure, and through its built-in TextRazor integration can extract named entities and classify complaint topics across every row in one operation.
For each support ticket text in column A, extract named entities using TextRazor and write the product/company entities found into column B and the top IAB topic category into column C.
What You Get
- Column B receives the extracted organization and product entity names from each ticket — the specific components or product names TextRazor identifies as entities.
- Column C receives the top IAB content category for each ticket, giving you a consistent taxonomy for complaint type across all 150 rows.
- Rows where no product entity is found get a noted placeholder in column B rather than a blank that hides the gap.
- The output is ready to pivot — count by entity name, group by topic category, find which product-complaint combinations are most frequent.
What If the Data Is Not Quite Ready
The tickets contain customer names that shouldn't be treated as product entities
For each ticket in column A, run TextRazor entity extraction, filter out person-type entities, and write only organization and product entities into column B. Write the top IAB topic category into column C.
Some tickets are duplicates from retries
Deduplicate column A by ticket text (keep the first occurrence of each unique description), then run TextRazor entity extraction on the remaining rows and write product/company entities into column B and the top topic category into column C.
You need the top 3 entities per ticket, not just the most prominent one
For each ticket text in column A, extract the top 3 entities by relevance score using TextRazor, write them as a comma-separated list into column B, write their types into column C, and write the top IAB category into column D.
Full kill chain: clean, extract, frequency analysis
Remove duplicate ticket descriptions from column A, extract product and organization entities using TextRazor for each remaining row, write entity names into column B and top complaint topic category into column C, then create a 'Trend Summary' sheet showing each unique entity, how many tickets mention it, and its most common associated topic category — sorted by ticket count descending.
The VP wants the frequency table, not the raw extraction — combining the analysis step with the extraction means you show up with the summary, not the spreadsheet.
Try It
Get the 7-day free trial of SheetXAI and open the Google Sheet with your weekly ticket export in column A. Ask SheetXAI to extract product entities and classify complaint categories using TextRazor across every row, then tell it to build you a trend summary tab. The multi-extractor analysis spoke covers combined entity and topic extraction in a single pass.