The Profit Lab: You’re biased, but it’s not your fault

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

Strategy #3: Make time to analyze your assumptions for like products and see if they were true in the past

In the last post of this series we discussed location clusters in the allocation process. Here we’ll take that a step farther and talk about merchandise grouping.

In virtually all allocation processes it’s necessary to select a product, merchandise hierarchy level or group of products to use as a base of historical data to make allocation decisions. Assuming a constant of source data (historical sales, historical demand etc.) the decision of what products or product groups can be the single most influential factor affecting the results of your allocation.

Choose a base of products that’s truly reflective of the item you’re allocating and it’s likely you’ll get a decent result. Choose a base that’s not and you almost certainly fail.

Ultimately the goal of this process is to select product(s) that have acted in a way that we expect the allocated product to act. In the process of making this decision we use our judgment to make assumptions based on what we believe to be similarities. Since many products we allocate are new, we can’t use the same item’s actual history. Even when we can there is rarely enough activity at SKU / store level to make good decisions. As a result we look for similar items to group together and give us enough data for the decisions we need to make.

In the process of deciding what to group, we tend to think about similarities of products. Similar fabrications, silhouettes, colors etc. We assume that these similarities mean items will act similarly. Sometimes that’s true. More often than most people realize, it’s not true. We’re biased in our assumptions, but we have to be. Unless you are unique in the retail industry, as an allocator you’re not afforded the time to prove out the assumptions you make – you have to execute so often and so quickly that there’s just not enough time to validate every decision for each allocation. So what’s an allocator to do?

What you can do now

Make time to do some analysis. A better allocation puts merchandise that would have been sold at markdown into a store that will sell it at full price rather than being out of stock and missing the sale completely. The truth is that making a better decision about the product base of your allocation consistently can often lead to as much as a 1% increase in revenue and margin. It may not sound like much at first but for a $1B company that’s $10M in sales and somewhere around $3-4M in profit in a typical case. That can quickly pay for a few more people, better technology or other improvements.

Take a day as an analysis day and look at products based on the characteristics you tend to group by. When you look at all blue tops individually, do they sell similarly? Is this true by store or cluster? If so, it’s a keeper criteria; if not, maybe fabrication or silhouette will have more impact as a group. The influencers will be different for different product groups, so take different passes for different product groups. Expect the things you find and results you get to be subtle. Using the better criteria will usually only shift a few packs within a given distribution. When that shift is consistently for the better and it’s multiplied across all stores, all products and for all receipts it quickly adds up to significantly better performance for your company!

What you should consider when looking for new capabilities

When looking at updated capabilities in allocation look for systems that are constantly analyzing product behavior by store. The best systems today are looking across multiple levels and groups of merchandise and location and learning about how each relates to individual product behavior within individual stores. Doing this allows them to do the analysis described above at much more detailed levels and to constantly update the results which become the basis of your allocation decisions.

Done right, results from using technology with this capability can quickly push that 1% gain mentioned above into 2-3% gains. That’s potentially $9-12M of profit per year. Enough to pay for the investment within just a few months.

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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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