Center for Business Analytics

Customer Analytics

Research on customer analytics examines the impact of marketing and prediction on customer and business value. Example articles include:

The Impact of a New Retail Brand In-Store Boutique and Its Perceived Fit with the Parent Retail Brand on Store Performance and Customer Spending
With data from a specialty apparel retailer, we present two studies that investigate the impact of an in-store boutique displaying merchandise of a new retail brand on overall performance of the parent stores in which the boutiques are placed, and on customer spending on merchandise offered by each of the two brands. Findings from the two studies generally support that the in-store boutique enhances three key store-level metrics: average customer transaction value per store visit; comparable sales growth from one year to the next; and customer conversion ratio. At the customer-level, both studies show that average customer transaction value on merchandise offered by the new brand is negatively related to average customer transaction value on the parent brand, and vice versa. Furthermore, both studies reveal that the relationship between the perceived fit between the two brands and average customer transaction value on the parent brand increases at an increasing rate (positive main and quadratic effects of perceived fit), but that the impact of perceived fit on average customer transaction value on the in-store boutique brand increases at a deceasing rate (positive main effect, but a negative quadratic effect of perceived fit). Implications for retail practice and theory are offered. PDF
Return Shipping Policies of Online Retailers: Normative Assumptions and the Long-Term Consequences of Fee and Free Returns
To limit costs associated with product returns, some online retailers have instituted equity-based return shipping policies, requiring customers to pay to return products when retailers determine that customers are at fault. The authors compare the normative assumptions about customers that underlie equity-based return shipping policies with the more realistic, positivist expectations as predicted by attribution, equity, and regret theories. Two longitudinal field studies over four years using two surveys and actual customer spending data indicate that retailer confidence in those normative assumptions is unjustified. Contrary to retailer assumptions, neither the positive consequences of free returns nor the negative consequences of fee returns were reversed when customer perceptions of fairness were taken into account. Depending on the locus and extent of blame, customers who paid for their own return decreased their postreturn spending at that retailer 75%–100% by the end of two years. In contrast, returns that were free to the consumer resulted in postreturn customer spending that was 158%–457% of prereturn spending. The findings suggest that online retailers should either institute a policy of free product returns or, at a minimum, examine their customer data to determine their customers' responses to fee returns. PDF