Demo: AI Product Segmentation for Retail Pricing
Watch a five-minute DS Stream demo of AI product segmentation that sorts thousands of SKUs into margin drivers, traffic drivers, and inventory risk.
A new five-minute demo from DS Stream shows how AI-powered product segmentation sorts a retail catalog by the way each product sells, so pricing decisions can follow the role a product plays rather than the category it sits in. It is meant for merchandising and pricing teams who manage thousands of SKUs and cannot keep every one of them in their heads.
Watch on YouTube: AI-Powered Product Segmentation for Smarter Retail Pricing Decisions
Why category averages mislead pricing teams
The demo follows a head of merchandising at a multi-category retailer. She has a margin target for the year, and she is expected to reach it without losing store traffic or ending the season with unsold stock. With thousands of SKUs to oversee, she works from blended figures such as the average margin or the average inventory turn of a whole category. Those numbers say very little about any single product.
When a category is priced as one unit, the averages hide problems that show up later. Products that bring shoppers in lose that pull once their price goes up along with the rest of the category. Premium lines kept in the same category get marked down whenever regular products are discounted, even though their buyers were not waiting for a sale. And while the team concentrates on margin and revenue, aging stock keeps piling up in the warehouse.
What the demo shows
- Segments based on selling behavior. Sales, inventory, and cost data are processed so that each SKU lands in one of the segments: margin drivers, traffic drivers, or inventory risk.
- A view of the whole portfolio. One screen shows how much of the portfolio falls into each segment, so it is clear at a glance whether the assortment leans toward margin or carries a lot of inventory risk.
- The SKUs behind each segment. From that summary, the user opens a segment and sees the individual products in it, which turns the portfolio view into a working list for pricing decisions.
- Segments that stay current. Data pipelines recalculate the segments as new sales and price changes come in, so a product can move from one group to another over time. The view always shows the portfolio in its current state.
- Exploration before automation. The demo stays at the exploration stage. The team works with processed data, much as it would in a business intelligence tool, and learns how its products behave before any price changes are handed to an automated system.
Where to go next
Segmentation is where the process starts. The full recording of our Pricing at Scale session, which goes on to pricing new products and setting up pricing campaigns, is available on the DS Stream webinars page. For the method behind the demo, from category rules to SKU-level models, read our guide to retail price optimization.
If you want to see how this would work with your own catalog, see how DS Stream approaches pricing optimization for retailers.
