We describe a methodology by which a retailer can identify action steps that are likely to increase sales and customer satisfaction and demonstrate the methodology using proprietary data from a large retailer with over 500 stores. We use monthly store-level data on a number of operational variables including in-stock rate, store staffing level as measured by payroll and store employee turnover, together with customer responses to satisfaction surveys. We develop a nested three-stage econometric model to analyze the marginal effects of various execution levers on sales, customer satisfaction and the percentage of customers who answer yes to the question Did you find everything you were looking for?, which we term customer perceived in-stock. Our model explains approximately 75%, 97% and 71%, respectively, of the residual variation in sales, customer satisfaction, and customer perceived in-stock. We find that customer perceived in-stock is primarily driven by actual in-stock and customer rating of employee knowledge; overall customer satisfaction is primarily driven by customer perceived in-stock, payroll level, customer rating of employee knowledge and check-out efficiency; and sales is primarily driven by actual in-stock, overall satisfaction and payroll level. Finally, we estimate relative magnitudes of these effects, propose specific actions to improve sales and estimate the likely sales increase from those actions. Our results suggest that a modest reallocation of the payroll budget among stores could be expected to yield a 2-3% increase in sales with no increase in cost.
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