Out of size, out of mind

The role of size optimization in the customer experience

By Alan Lipson, SAS Retail Industry Marketing Manager

Apparel managers know the feeling of dismay when they’re left with half a rack of smalls and shoppers are clamoring for larges. Customers and retailers alike have felt the frustration of not having the right size of a product available when a purchase is all but guaranteed.

Online retailers know they may never get a return visit from a customer if a sought-after shoe in their size is out of stock, especially if it’s in stock at a competing site only a brief Google search away.

As a retailer, you must have the ability to understand access to supplier, your current inventory and, more importantly, the size needs of your customers. You can apply some general rules of thumb to come up with good guesses about your assortments and where you need to send more (or less) of a certain size.

On the other hand, buying too many of a particular size means eventually marking down those items. And, many retailers have trained their customers to delay purchases and wait for markdown pricing.

Retailers have spent lots of money on pack assortment solutions and found the results were less precise and less profitable than promised. Many of these “optimization” solutions are based on corporate averages and fail to take into account seasonal opportunities and local factors that affect size demand.

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Hint: Start with the data

Maybe missed opportunities are the reality you’ve decided to live with. The thing is, you have the answer to these problems. You have customers’ buying history, and for online shoppers, you have their search history. You probably also know what your supplier’s inventory is and how quickly your manufacturers can turn around a replenishment order. The trick is data management and analytics. That’s where size optimization comes in.

Size optimization solutions consider many factors, but at the highest level, they are looking at three:

• Location. You can craft refined, effective assortments based on data from a number of geographic locations to understand their size demand differences and similarities. Then locations with similar demand patterns are grouped to balance the profile utility and process efficiency.
• Product. Size demand changes as you move up and down your product hierarchies. Size curves that work for one product cannot be applied to another because size demand is often different depending on your target along the hierarchy. Creating size profiles based on your product hierarchies will help your analytical models perform better.
• Seasonality. You know from experience that customer purchases tend to ebb and flow with the seasons. Hopefully, you’re taking that into account when you’re creating your size profiles. One of our customers, a sporting goods retailer, had fewer markdowns and better sales after they began using size profiles that accounted for seasonal peaks.

The real genius of intelligent size solutions is that they offer optimized case-pack level purchasing recommendations. Once your data sources are connected and your size models are in place, then a size optimization solution can use automatic size lookup and scoring to select the best available profile for specific locations and recommend the most effective assortments. This means fewer missed sales and markdowns.

Balancing the equation

On the supply side, retailers (and wholesalers) can stop bulk ordering and place streamlined pack orders based on the three factors above. The operational benefit you will see is getting merchandise to the right locations faster, and you can start selling at full price more and marking down less.

For example, an apparel retailer may discover that although they were buying the right assortment of smaller sizes, they were distributing them evenly across all their stores instead sending them where they were in highest demand. Once a retailer understands that the size profiles generated by the models offer a more accurate picture of demand, they can built the right assortments and see a significant improvement in their margins.

The details are in the analytics

The reality is that this is hard work. In order to achieve the results discussed above you must be willing to optimize down to the SKU at every location. This is a formidable task that can only be achieved through the use of customer analytics solutions.

The best solutions will help to manage the data, allow you to see the history, make appropriate adjustments to the data to account for stock-outs and then generate the predictive and prescriptive results necessary to achieve the optimum business results based on your business rules.