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The Limitations of Traditional Retail Business Analytics

Retailers use reporting and business analytics are for a variety of purposes. These reports can help senior executives understand the overall performance of a store, region, or category. They can help pinpoint SKUs that are underperforming in stores. However, retailers often struggle to identify real problems at a SKU and store level and provide stores with accurate and actionable reports. In this article, we’ll look at how traditional retail business analytics fall short and what retailers can do about it.

 

How do retailers identify problems using reporting?

SKU sales are compared against expected historical performance or performance of “similar stores” according to predefined clusters. An exception is created when an item is not preforming as well as in a defined period in the past (e.g. same day last year) or performing less than the stores in the same cluster (i.e. similar stores). A cluster includes stores with similar conditions, such as demographic, overall size, climate etc.

When comparing a SKU to its past performance, we assume that the conditions in the past that affect the SKU’s demand haven’t changed significantly. In reality, conditions change rapidly over time. For example, a SKU changes its location in the store, the weather changes, and promotions that a retailer or their competitor are running change often, too. As these conditions change, demand changes. Therefore, the fact that sales are down compared to the historic performance can simply mean that the item is in less demand currently (e.g. soda on relatively cold day in July).

When comparing a SKU’s performance to other stores in a cluster (i.e. similar stores), we assume that the conditions that defined the cluster are the major conditions that affect demand at these stores. For example, a cluster could include stores with similar demographic and climate conditions. However, even stores having such conditions in common could still vary dramatically in demand for certain SKUs as many local conditions that impact demand are typically not captured in such clusters. For example, the proximity of a store to a school, a competitor, a bike lane, etc.

 

Can monitoring top performers solve the problem?

Because of the limitations of retail business analytics discussed above and because of the wish not to capture too much time from the stores’ teams, at times retailers tend to watch top performing SKUs closely. This practice typically means that once the sales of a top selling items drop to a suspicious level, the store personal is alerted. The top sellers are typically the top selling items nationally or regionally.

The main limitation with this approach is that it misses many opportunities by not highlighting underperforming SKUs that have high demand locally and are not capturing that demand due to operational issues. In addition, if the top sellers list is extended too much, we risk highlighting products that although are top sellers on a regional level, do not necessarily have high demand locally.

 

How do we overcome this challenge?

The key to capturing lost sales opportunities is having a better understanding of local demand. If we know which items should be selling at high levels at each store, we can make sure that we don’t miss opportunities with potential local best sellers. In addition, we avoid wasting store managers’ time by highlighting national best sellers that have low demand at their store due to unique local conditions.

To learn more about how CB4 helps identify items in the store with high local demand that are suffering from operational issues, take a look at our technical overview.

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