THE PROFIT LAB // 4 Strategies to Optimize Assortment Planning

WEEK 2

Years ago the store owner knew his customers by name. He could pull their goods in advance of them coming in to the store. If the customer wanted something that the store owner didn’t carry, the customer could request a specific item to be added to his assortment and he could choose whether it would be worth it or not. As store chains developed, non-centralized planning and merchandising allowed the store manager to keep his finger on the pulse of his customers. What were they asking for? What did they like? What did they not like?

Today is a much different picture. These same chains have expanded their store counts by hundreds, if not thousands, and now rely on buyers and planners that sit in headquarters trying to determine how to localize the assortments to maximize the potential revenue and margin that each individual store has the ability to provide. How can today’s merchant personalize and localize an assortment the way the store owner or store manager would have done when they were responsible for just one store? The obvious answer would be to assort each store independently, but that just isn’t realistic. There are not enough people to do that. The answer lies in clustering.

The Beginnings of Clustering

Clustering started decades ago as chains began reaching the high double digits in store count and merchandising became more centralized. Back then, everybody did it the same way. Stores were ranked in terms of sales and grouped, usually by percent of average. The “A” stores may be those stores that perform at 200% of the “average store.” Of course, there was no “average store,” but it was the total sales divided by the number of stores that represented the average. “B” stores could be 160% to 199% the average store, and so on. The number of clusters were somewhat a semblance of how many stores were being managed, but also the number of clusters a buyer or planner could manage was a factor as well.  The more clusters there were, the more precise the assortment could be, but the more difficult it was to merchandise. Trade-offs were common. This was the beginning of clustering.

Merchandise Hierarchies

Next the merchants started to group the stores by merchandise hierarchies. Categories, departments, and classes now were getting their own clusters of stores, a logical transition. An “A” store could be a fantastic store in women’s career apparel, but terrible in men’s accessories. This allowed merchants to be increasingly specific in building assortments that would perform better in certain stores.

This is about where your typical retailer is today. A majority of retailers dissect their stores into volume (sales) based clusters in this manner at a merchandise hierarchy level. That merchandise hierarchy varies, but it’s typically at the level that planners are building an assortment plan, most likely to be class. While a majority of retailers are at this point, a few have successfully moved beyond this stage and made a variety of improvements.

Nested Clusters

Some clusters are nested, building clusters not just on sales volume, but also on a variety of store attributes. Climate is probably the most common and most logical. This has a big impact on a variety of categories. Outerwear will sell better (and earlier) in Minneapolis than in Miami. Store size is another somewhat common attribute that merchants use to cluster as is demographic information such as race, religion (for some classes heavily influenced by holidays), or income. All of these make sense, but they are far from being universally adopted.

Statistical Clustering

A mathematician would tell you that what I have previously referred to in this article as clustering is actually “grouping of stores.” Pre-determining both the break points as well as the number of groups doesn’t allow stores to truly “cluster” together, but instead to simply “group.” By applying statistical methods to clustering, stores that are truly more alike will end up in the same cluster. The number of clusters becomes statistically relevant as well, and not something as simple as 26 clusters because that’s how many letters there are in the alphabet. You laugh, but I’ve seen it more than once in my career.

An Evolving Process

So, the evolution has begun, clusters are now really clusters, as opposed to groups. Stores are being clustered together based on more options than sales volume alone and being clustered with statistical accuracy. Consideration for demographics or store attributes such as climate are now commonplace.  However, there is a big piece missing that I haven’t hit on yet.

Three major problems of clustering

While there might be exceptions out there the vast majority of clustering has three major problems associated with the process. These three issues are seriously inhibiting the retailer from truly localizing their stores.

1. Clusters of stores are almost always based on historical performance.
2. Clusters are typically locked in for a season or similar time period. If the recent economic climate has taught us anything, it is that store behavior changes and it changes rapidly, especially at the merchandise levels.
3. Clusters are hindered by store attributes. Significant value can be gained if stores were clustered based on merchandise attributes.

Plan for Future Demand

Clustering stores based off history is a mistake that almost every retailer makes. I understand why, the typical merchandiser does not have much of a choice. History is the only thing that they have at their fingertips on which to cluster. But, this means of clustering misses the quite obvious fact that stores performance last year will not equate to store performance this year. That’s why history is not the best base for clustering stores together.  A consideration of expected future behavior must be made. Clustering on a trend or, better yet, a forecast at the store/merchandise level is a better way to cluster the stores.

Stores are Dynamic

The second issue mentioned is that store clusters are typically locked in for a season or more.  An individual store that performed as an A cluster last year during the Spring season in Womens Tops will be clustered again as an A store for the entire Spring season this year. However, as often as not, that store will not repeat the same performance year over year especially in every department. Stores need to be able to move within a cluster to more closely align their actual performance with merchandise levels.  If stores don’t move with their performance, they aren’t being localized. Stores will underperform and be left with merchandise to markdown or overperform and stock out. If, however, stores actual current performance dictates the cluster and thereby their merchandise levels, these things are less likely to happen and the store is being localized more effectively. By doing that, we are introducing continuous small amounts of change into the way that products are being assorted into stores, which in itself is more manageable and timely in reacting to the way that customers are really acting in the stores. That’s an incredibly powerful piece of the puzzle.

The best way for stores to be localized given that it is impractical to expect an assortment per store is by having dynamic clusters. The assortment planning process should include a periodic, typically weekly, review of each store’s performance versus its cluster and make a recommendation to move that store to a different cluster based on a variety of criteria. This allows merchants to fine tune the assortment that will perform best in a store given the store’s behavior this season, not last year.

Don’t Forget the Merchandise!

The last issue that I have called out is clustering solely on store attributes. There is clearly value in merchandising based on some store attributes. Climate is the best and most obvious example as this not only affects the breadth and depth of the assortment, but also the flow of the merchandise. I remind you of my earlier Miami and Minneapolis example in outerwear. You’re not only going to have more choices in jackets in Minnesota, but you’ll have more inventory as well as an earlier flow of merchandise. However, clustering solely on store attributes missed a significant opportunity for store localization based on how merchandise attributes collectively perform at an individual store.

An example of this can be found in price point. If you cluster a class of merchandise based on the price tier (“good, better, best” is common representation of this), the stores that perform better with higher priced merchandise will be grouped together. I would argue that this is even more accurate than grouping the stores based on demographics such as income level. Just because a store is in a nicer neighborhood does not mean that higher priced merchandise will sell better in that store. Honestly, if the retailer creates clusters with stores that actually perform better in the type of merchandise, the demographic information hardly matters!

In Summary

Today, nobody expects every store to receive its own assortment plan. Every store, however, can receive its own localized, unique assortment even when clusters are being utilized.

Recap:

1. Cluster on more than just volume and history, by incorporating attributes of not only stores but of the merchandise.
2. Constantly update the store cluster assignments based on actual store behavior.
3. Create localized clusters based on how merchandise attributes collectively perform at an individual store.

By following these guidelines, a merchant can have a positive impact on their chain’s performance and will be able to create localized plans for the individual stores.

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