The Profit Lab: Debunking the Cluster Myth

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THE PROFIT LAB // Top 10 Ways to Pull Profit from Allocation

Strategy #2: Learn the unique behavior of each store before clustering it

Location clusters. We all know them and some love them. They have their place, especially in larger retail organizations. It’s important to think about why they exist before determining when and how to utilize them.

For the most part they only exist to allow merchants to look at data in a way that reflects some localized perspective, but makes it more manageable than evaluating each individual location. This is fine for review purposes, but when it comes time for execution, we need to consider the uniqueness of each individual location.

There are a few different ways clusters are utilized in Allocation

Some of the most common include:

Setting constraints: Min, max, calculations and caps, inventory target ratios, presentation quantities etc.
Making adjustments: When reviewing allocations making cluster or store group level changes to allocation quantities.
When allocating: Sending X number of cases to “A” stores, Y number to “B” stores etc.

There are two realities that generally keep clusters from being effective – especially in the context of allocation

First – Cluster definitions must be created. To keep this process manageable this is executed at aggregate levels of merchandise – either hierarchy levels (categories or classes) or by manually selected groupings. This process involves making assumptions about what products will behave similarly and in the case of using hierarchy levels, forces items known to be different to participate in the groupings. This can be convenient as it tends to smooth or “normalize” some of the volatile activity out of the result. Unfortunately it virtually always colors the answer to the point that clusters have similarities to some of the products you’re applying them to but are not truly reflective of the unique behavior of any of the products you’re applying them to.

Second – Store behavior ALWAYS changes. As a result, no matter when you’ve clustered and how perfect your clustering criteria are, the cluster definition is almost immediately out of date. Conventional wisdom says that there isn’t enough change in a season or quarter to warrant revising clusters. The reality is that unbiased analysis shows us that this assumption is undeniably wrong. The change is constant and relentless. This is especially true in the schizophrenic economic conditions we’ve been experiencing in recent times.


If you’re company has localization as a goal, you can’t truly achieve that if you’re driving the allocation process with clusters as described above. Clusters can still be a part of a successful allocation strategy, but a process to uncover the unique behavior of each location is the only way to achieve true localization in the allocation process.

What you can do now

If you’re currently allocating by setting the number of cases you send to a cluster, STOP! Even the most technologically challenged organizations can usually find some source of recent historical data by store and build a spreadsheet that can use that individual store understanding to improve results. You can still use cluster level constraints (i.e. min / max) to buffer the volatility that comes from looking at store detail.

If you already have technology that looks at store history and you’re setting criteria at cluster level, evaluate the clustering criteria more frequently. Stores are changing their behavioral characteristics faster than ever before. We need to keep up with the change or results will suffer. Even if your system dynamically generates clusters based on indexes, the assumptions that went into those index definitions have probably all changed – and they’re different for different merchandise. Put the time into updating them.

What you should consider when looking for new capabilities

Modern allocation systems are capable of learning much more about how individual stores and products behave. In addition to considering demand rather than just historical sales (as discussed in the last post in this series), these systems understand a variety of levels that have been proven to influence the behavior of products within locations. They constantly monitor that behavior and utilize it to set store specific objectives, eliminating much of the need for clusters from the Allocation process. Clusters are still used to set some constraint criteria, however the individual store awareness means that this criteria is less critical to achieving the desired results. The range of these criteria is often softened (e.g. reducing mins and increasing maxs) or in many cases eliminated due to the fact that the detailed store understanding accommodates them.

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Follow this series to learn all 10 strategies for improving allocation. We will be deconstructing the allocation process and exploring opportunities to improve within your current allocation processes and technology limitations. We will also review key areas to think about if you are considering investing in improved allocation capabilities.

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One Comment

  1. Giovanni Arslan says:

    Dear Sirs,
    can I get professor Wilson’s papers or books written by him?
    I’m interested about retail localisation and ubication matters, wich in Italy is named “Economic Land Gepgraphy”.
    Thanks, RSVP if possible!
    Gianni ARSLAN
    via Altinate 67
    35121 Padova, Italy

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