Utilizing Analytics to Customize & Optimize Retail Networks

Location, location and what was the third? Although location is known to be the key ingredient for success in retail, it is not the only criteria in driving sales. Often, the wrong combination of employees, lack of local marketing and various other factors can diminish returns on investment in a premium location…

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For a long time, retailers, as well as other companies with sizeable retail networks (such as banks and telecom operators) have been optimizing their sales networks, identifying the best location for each store / branch they have, in order to gain a competitive edge. Unfortunately, many have stopped there, as if a store’s future performance is set in stone once it is opened, without looking further into fine-tunings that can have a substantial effect on sales performance.

We believe that after store locations have been set, companies can build analytical models to understand the impact of more variable success factors – i.e local marketing budget, store employee profile, store layout – on local sales performance of each store and apply their learnings across the sales network. Such analysis would lead into tactical changes, customized for each store, which would ultimately result in the maximum return from each location.


Even before the 21st century, leading retailers have realized the impact of non-geographical factors on sales performance, such as Sears, which built a revenue model quantitatively linking employee attitude to increase in revenue growth for each store. As the expectations of customers have substantially increased over the years, the variety of such factors affecting local sales performance has become more and more complicated and significant. As for significance, according to a 2010 survey, only 8% of customers would bargain for lower prices, if that would mean a lower level of customer service as well. As for complications, a recent AC Nielsen survey on consumer preferences indicated that even whether a store has recyclable bags or not has become a considerable factor in store selection.

By scientifically analyzing what factors affect local sales performance, and by how much, companies can invest in the right store for growth with the right focus. Such analysis could reveal that replacing an employee in a given store with someone having the right demographic or psychographic profile would increase store sales by 5%, changing the store layout by 7%, and advertising in a local newspaper by an additional 10%.


There are four main steps towards identifying and capitalizing on the factors and their impact on store performance:

1. Gather Data on Stores and Potential Factors

Modeling relations between different factors and their impact on sales performance requires detailed store profile data to begin with. Any characteristic that may have an effect on sales performance needs to be taken into account and measured for each store. Such factors would range from store design to staffing and local marketing, and require both systematical and manual data collection, such as employee surveys and site visits. Hence, the first step is to hypothesize on factors as well as the data elements to be collected and gather the required data for each store.

In addition to data on potential factors, another data set is required to isolate the effect of environmental factors on store performance, which would include elements on store location, market size and profile as well as competitive pressure. Such data is required to segment stores into peer groups, so that their inherent impact on sales performance is isolated from the factors to be studied.

2. Segment Stores for Peer-to-Peer Evaluation

Once the data is ready, the next step is to segment all stores under evaluation into peer groups, where environmental differences are minimized to isolate the impact of factors under study. In addition to building the basis for impact analysis, such segmentation is valuable for ongoing performance evaluation with an objective comparison of store sales.

3. Model Impact of Factors on Store Sales

After the stores are segmented into peer groups for analysis, the next step is to build analytical models to establish quantitative links between the hypothesized factors and the sales performance of stores within each peer group, starting with simple hypotheses testing and correlation analysis.

For building the actual models, various techniques could be used, ranging from regression models to decision trees, although Structural Equation Modeling (SEM) is a perfect fit to study causal relations, as it allows for analyzing relations between measured factors, latent constructs (i.e. variables that are abstract on their own, such as ‘store design’) and results. Such modeling could reveal that a 5% improvement in ‘staffing’ would improve sales by 2%, and that such 5% improvements in ‘staffing’ could be created by replacing 2 employees with university graduates and boosting employee morale by 2 points.

4. Utilize Findings to Increase Sales

As the final steps, finding from the modeling exercise need to be translated into business actions for each store, where significant improvement opportunities exist. Such translation would incorporate cost analysis of each potential action with the expected impact of each based on the model outcomes, and prioritize stores and actions based on the results. Based on the prioritization outcomes, an implementation road-map can be defined and put into action.

What Next?

This exercise of optimizing store characteristics for increasing sales is not a one-off exercise; it should be performed on a regular basis, as the factors affecting sales and their level of impact are subject to change over time, with changing market conditions and customer expectations. Companies excelling at this exercise should also incorporate their learnings into new stores in order to turn them into success stories in short timeframes. To learn more about making the most out of your retail network, please contact info@forteconsultancy.com.


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