Turn Returns into Revenue—Without a Data-Science Budget
Stop the return Tsunami with help from ChatGPT
If product returns are chewing through your margin but you don’t have the time, head-count, or budget for an in-house machine-learning build, this new 16-minute tutorial is for you. In the video I show how to combine KNIME’s free, visual workflow builder with a low-cost GPT-3.5 API call to:
join orders + returns at the line-item level
auto-classify messy “reason for return” text in seconds
roll up net-revenue, return-rate % and days-to-return KPIs
surface the biggest profit leaks on a clean dashboard
Watch the step-by-step demo (“Stop the Return Tsunami: KNIME + ChatGPT”):
Click here to watch the video on YouTube.
Who will get the most value
If you are, a small business or speciality retailer with no data-science head-count, then this workflow helps you build an insight engine with zero code and pennies of API cost.
If you are, an ops or merchandising team tired of waiting in the data-science backlog, then this workflow helps you use drag-and-drop KNIME nodes let you act today—no model training cycle required.
If you are, any team on a tight budget, then this workflow helps you perform GPT-3.5 classification runs ≈ $0.01 per 1 000 rows; everything else is open-source.
Ready to try it?
Download the sample workflow & CSVs (links in the video description).
Point the CSV Reader to your own orders and returns files.
Swap the API key and run—your first KPI dashboard appears in minutes.
Questions or roadblocks? Drop a comment on the video or reply here and I’ll help you wrangle the data. Happy analysing!