Retail store clustering can be a tricky subject to wrap your mind around, but it doesn’t need to be. In simplest terms, clustering is just the process of grouping stores by their ability to sell different types of products so you can make decisions about clusters rather than individual stores. Many retailers use sales volume as their primary clustering method, but some industry thought leaders have recently suggested sales volume misses aren’t enough for retail store clustering. At daVinci Retail, we argue sales volume is the correct method to cluster by, but you need to take it further to get the most significant gross margin on this strategy.
The concept is straightforward if you’re unfamiliar with clustering by sales volume. Data integrity in your retail sales is critical once stores are grouped or clustered according to their retail sales figures, usually into four or five different retail sales volume groups. The powerhouse flagship stores that move the most inventory fall into the “A” cluster, while the sleepy stores that don’t sell as much might be labeled “D” stores. This strategy ensures the stores selling the most merchandise get the most.
But there are severe shortcomings to this method. Most importantly, it doesn’t take into account customer preferences. It simply groups stores by their ability to sell without diving more deeply. It assumes an “A” store in Brooklyn is the same as an “A” in Beverly Hills, Miami, or Vancouver. But the fact is the customers shopping in those “A” stores likely have very different preferences. That’s why retail sales volume group clustering has come under fire.
See also: Why Your Retail Store Clustering Method Doesn’t Work
We agree that this method of retail store clustering fails to account for customer preference. But we still defend using retail sales volume as a clustering method. But we recommend clustering by retail sales volume group at a product class level.
Rather than treating all A stores the same, you create clusters that reflect locations’ ability to sell different types of products. That Brooklyn store might be an “A” for outerwear but a “C” for accessories. Meanwhile, the Beverly Hills store might be a “D” for outerwear but an “A” for woven dresses. Rather than treating each location as an “A” for every product, this method does a much better job of accounting for customer preferences.
| Location | Short sleeve tops | Boots | Bedding | Cookware |
|---|---|---|---|---|
| Times Square | A | A | B | B |
| Toronto | B | A | A | A |
| Hollywood | B | C | B | B |
| Honolulu | A | D | B | C |
| Chicago | B | A | C | B |
This table shows how stores can be assigned different clusters for each product category
To take this method further, we recommend clustering before you buy assortments rather than waiting until the allocation process allowing your merchandising team to determine much more precise quantities before they commit to buys and offering allocators a better roadmap for how buys are intended to be distributed.
You can also give your merchandising team more control over which stores to buy assortment for by allowing them to tailor buys according to additional dimensions of customer demand. For example, a buyer may exclude merchandise from certain stores based on factors like country, currency, and climate. Tailoring buys in this way ensures the quantity purchased matches up with the likely amount to be sold.
Despite this vast improvement over the traditional retail sales volume group clustering methods, challenges still need to be addressed. The most pressing challenge is that clustering and merchanding based on product class exponentially increase the number of unique assortment plans that must be generated. Tailoring buys by additional layers exacerbates this problem to the point where you could have thousands of unique assortments represented within your buy plan.
The traditional tools merchants use (i.e., spreadsheets) aren’t capable of managing the amount of information generated by such a system. Assortment planning software is required To take sales volume clustering to a product class level. Fortunately, in addition to improving buy quantities through better clustering, assortment planning software has many other benefits that make the investment worthwhile.
In conclusion, retail sales volume is more than satisfactory for retail store clustering, but only if you take it beyond a store level. To use sales volume groups correctly, you must cluster at a product class level and use your clusters to inform the buying process. For maximum gross margin, you should further tailor your buys to ensure you’re feeding the right stores.
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daVinci Assortment Planning
daVinci’s Assortment Planning solution transforms the way retailers manage their buying process, improving efficiencies, so merchants get back time to be merchants.
