The Scenario
A support engineer flagged something in the weekly ticket review: the same product component keeps showing up in complaint descriptions, 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 the 'Tickets' sheet in 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:
- 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 find that 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 Excel workbook. 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.
Run TextRazor entity extraction on every feedback text in column A of the 'Tickets' sheet and write a comma-separated list of organization and product entities into column B.
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.
- The extraction rule is applied identically across all 150 rows — no per-row taxonomy drift.
- 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, find which components appear most frequently.
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 of the 'Tickets' sheet, 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 support platform retries
Deduplicate column A of the 'Tickets' sheet by ticket text (keep the first occurrence), then run TextRazor entity extraction on the remaining rows and write product/company entities into column B.
You need the top 3 entities per ticket
For each ticket text in column A of the 'Tickets' sheet, extract the top 3 entities by relevance score using TextRazor, write them as a comma-separated list into column B, and write their types into column C.
Full kill chain: clean, extract, frequency analysis
Remove duplicate ticket descriptions from column A of the 'Tickets' sheet, 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 trend summary is what you're showing the VP — building it in the same prompt means you show up with the analysis, not the data.
Try It
Get the 7-day free trial of SheetXAI and open the Excel workbook with your weekly ticket export. Ask SheetXAI to extract product entities using TextRazor across every row, then ask it to build the trend summary sheet. The multi-extractor analysis spoke covers combined entity and topic extraction in one pass if you need both dimensions.