The Demise Of Brick-and-Mortar Retail Is Greatly Exaggerated: Four Ways Advanced Analytics Can Help

E-commerce may be the darling of retail of late, but that doesn’t spell the end for brick and mortar stores. Far from it, 93% of total retail sales worldwide still take place in a store. And with consumers willing to pay 50% more for items that they can touch and see, it’s obvious that physical stores still represent a significant opportunity for any retailer.


So what can retailers do to maximize this opportunity? Here are four ways that advanced analytics can transform the profitability of any physical retail store:

Have the Right Stock in the Right Place at the Right Time

Most retail chains develop assortment plans using high-level historical sales with some rules-based customization across store clusters. Unfortunately, this often results in poor inventory performance and customer dissatisfaction due to stock-outs. Why? Because the plans don’t account for micro-variations in demand.


Consider these scenarios:

A) Stocking the sizes that match your customer demographics. Let’s look at an example. In Store B (Figure 1), there is less demand for small and medium sizes, but since stock is not adjusted to reflect this demand, these garments have to be marked down at the end of the season, while store A has the same issues with regard to larger sizes. Analysis of SKUs that sell only during mark-downs, can help the retailer get the right sizes into the right shops. This approach worked for the retailer in this example and led to more than 5% increase in sales and around 50% reduction in mark-downs for affected products.

B) Factoring in the impact of competition.
This is especially so for stores in shopping center locations. Retailers will need to consider whether other retail chains in the same mall carry the same products at a different price point.

C) Forecasting based on weather. For one retailer, sales of hosiery in winter made up 31% of inner wear sales for those stores in cold climes. But these stores received roughly the same amount of stock as the stores in the tropical latitudes, increasing the likelihood of stock-outs and dissatisfied customers (Figure 2).


Price it Right

Despite the availability of granular data around price and sales, many pricing decisions in retail are still based on gut feel, past experience and simplistic Excel analysis of summarized data.

The result? Retailers tend to apply proportional price increases in response to manufacturing cost increases which could lead to a substantial volume decline. If retailers use price sensitivity analysis on their granular data, they could be far more precise about which prices should go up, by how much and at what time. A totally different approach to pricing that could yield incremental revenue for the company.

Maximize the Impact of Promotions

Analysis of promotions for a single clothing brand in a number of national retail chains over 2 years showed that only a third of the promotions for this brand yielded more than 20% uplift over average and 41% didn’t yield any uplift at all.

Decision tree analysis (like the example in Figure 3) can tell us why. If promotions are not aligned with special events, such as Christmas, then they might not work as there is no additional foot traffic at that time. Just as surprising – 33% of promotions don’t yield the results expected because of insufficient stock. And what about your competitors? If a deal similar to that advertised can still be found cheaper at a competitor, the promotion is not going to take off. Retailers should also be able to find out if an unexpected effect of a promotion was a “halo effect”, increasing sales across the range.


Get Value for Money on Marketing Spend

One retailer I worked with had a markedly different spend on marketing in two years. However, the sales across the two years indicated that the extra marketing spend had not translated to additional sales. Clearly, this is a case where understanding the factors that contribute to the success of individual marketing efforts can optimize the overall spend.

Some factors that impact on the ROI?

  • Brand equity. Is the brand a category leader or a new entrant?
  • Channel mix. Customers may receive marketing promotions from different channels and campaigns, but which one(s) had an impact with the customer?
  • Contractual obligations. Which retail marketing efforts are as the result of the need to support suppliers or the need to consume media during specific times?
  • External factors. Economic cycles, competitor advertising and special events could all affect the return on marketing investment.

Retailers now have access to increasingly granular operational data such as point of sales (POS), inventory. When combined with other data, such as catalogue promotions and media consumption, and viewed through the lens of the right analytics, retailers can understand customer buying behavior at stores better than ever before.

Never want to miss another opportunity? Then find out how you can overcome the barriers to analytics adoption in your business today.

This post first appeared on Forbes TeradataVoice on 22/10/2015.

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