I n today’s highly competitive retail industry, it is crucial to recognize the factors which help retailers to thrive in the long run. One of the major factors which distinguishes a successful retailer from others is information regarding the efficiency of its stores. Like Charles Darwin’s ‘survival of the fittest’ theory, the efficiency of a store enables the retailer to function effictively in the market, with minimum inputs and maximum output, to be the fittest amongst all the rest.
A retail store is a critical asset in the retail industry. Customers interact directly with the retail store and most of the revenue in the retail industry comes from stores. Hence, efficient management of store operations is essential, to achieve and sustain customer satisfaction, as well as cost-effectiveness.
Techniques to evaluate a store’s productivity and its relative efficiency involves evaluating many factors-such as inventory turnover, sales per selling space, employee productivity, customer traffic, effectivity (retail conversion rate), average sales, gross margin (sales profit before costs), among other factors. The current industry practice generally involves a simple, logical, efficiency-based approach which is not mature in terms of leveraging solid analytical techniques. Thus, generally, store efficiency is evaluated by considering only sales density, that is, sales per square foot or number of employees. This involves only two metrics at a time, ignoring other factors. These standalone factors contribute individually and independently towards the evaluation of a store’s performance. This leads to information throw in various random directions, while business leaders are attempting store performance evaluation and goal setting. Various store performance dashboards showcase different charts as these can’t be clubbed together to get one performance index for a store. Combining them into one, to get the overall performance in terms of an efficiency score, which can then be used to compare various stores, is possible through a non-parametric approach from data mining and analytics called Data Envelopment Analysis (DEA). DEA uses a linear programming procedure to evaluate store efficiency by considering parameters-such as staff information (employee count and payroll), operating expenses, fuel consumption and space as inputs and sales, EBITDA, gross margin as outputs for a store.
DEA is a very powerful technique to evaluate the relative efficiency of business units when multiple inputs and outputs are involved. This framework has been adapted from multi-input, multi-output production functions and applied in many industries. In general, DEA minimizes inputs and maximizes outputs: In other words, lower levels of the former and higher levels of the latter represent better performance or efficiency. Instead of central inclinations, DEA focuses on boundaries. It doesn’t try to fit a regression plane through the center of the data as in statistical regressions; rather, DEA proves particularly proficient at detecting relationships that would remain unseen with other evaluation approaches. DEA benchmarks units against their best peer groups. It can be applied to explore production data as well as cost data. Based on the business problem, one must be able to differentiate the various factors into inputs and outputs to apply DEA analysis efficiently. Some benefits of this technique are:
- It is assumption-free
- It’s one of the most scientific methods of calculating efficiency
- There’s no need to explicitly specify a mathematical form for the production function
- Multiple inputs and outputs can be considered for calculating efficiency
- Inputs and outputs can have different units
- It’s an useful technique to identifying future goals for business units
- It can conduct comparisons directly against peers, and reward efficient experimental units based on past performance
DEA impact: A retail case study
About 750 stores of a top US retailer had to be compared in terms of features such as area, sales, gross margin, payroll and incentives, operating expenses, EBITDA, revenue etc. To get the best and most significant factors influencing the efficiencies of the stores, correlations among the inputs and the outputs were checked. Among all the variables, number of employees working in the store and the area of the store, represented the best inputs, whereas sales and EBITDA represented the best outputs. DEA algorithm was leveraged to analyze data and get relative efficiencies across stores.
Key insights were derived from the results (given as findings 1, 2, 3, 4 and 5) which helped store managers and operation leaders undertake critical business action plans to improve store performance.
Findings 1: Insights based on relative efficiencies across stores