Essays on the Applications of Quasi-Experimental Design in Studying Customer Behavior in a Multichannel Environment

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As many retailers are extending their channels to both online and offline, consumers nowadays are increasingly buying products across different shopping platforms, ranging from traditional catalog and retail channels to Internet stores. Thus, it is important to understand how the purchasing experiences and the factors surrounding the multichannel shopping environment shape their future customers’ behaviors. In my essays, I demonstrate the use of novel quasi-experimental designs in investigating how social influence and product unavailability affect customer behavior. >In the first chapter, “Small Networks: Intra-household Interactions in a Multichannel Environment,” I study familial interactions and the mutual influences household members have in the shopping process. A multi-person household is a small network, which raises the possibility that household members’ actions are interrelated. Although many firms recognize that individuals within a household may have distinct preferences, the possibility that purchase behaviors may be related in a multichannel environment has received far less attention. I investigate these issues in the context of a major U.S. apparel retailer. In a descriptive analysis, I first show household members’ purchases are highly correlated; when a spouse purchases, the partner tends to purchase. I then show that whether household members coordinate their purchases is predictive of the future value of their spending. Furthermore, I develop an instrumental variables approach to tackle the problem of whether a causal relationship exists. I find that a 10% increase in spending by a female partner influences the male to increase spending by 1.9%. A similar increase in spending by males leads females to increase spending by 3%. In addition to this asymmetric effect among genders, I find evidence of channel asymmetry. In particular, the intra-household effect is strongest in the retail channel, followed by the catalog and Internet channels. I also show how customer, household, and seasonal traits moderate the intra-household effect. Most notably, the baseline spending level of the customer who is being influenced by the partner’s purchase positively moderates the intra-household effect. Lastly, through simulations I find that without the existence of intra-household effects, the customers’ total spending would have been significantly lower. The ROI of targeting would also be underestimated if such effects are not properly taken into account. Overall, this research emphasizes the importance of marketing to a household not as a single unit but as a small network. In the second chapter, “The Long-Term Impact of Backorder Delays: A Quasi-Experimental Approach” (with Eric Anderson), we document how product unavailability, in the context of backorders, influences customers’ future shopping behavior. When an item goes temporarily out of stock, it is common practice for firms to inform consumers that the item will be shipped but delayed, which is referred to as a backorder. Measuring the effect of backorders is challenging due to endogeneity: the best customers are most likely to experience backorders. We develop a quasi-experimental approach that allows us to measure the near-term and long-term impact of backorders on customer purchase behavior. We use extremely detailed transaction data to create narrow event windows before and after a backorder occurs. After conditioning on these windows and controls, the backorders are plausibly random. We find that experiencing a backorder leads to the customer decreasing future orders by 2.2% in the subsequent year. The effect intensifies as the delay magnitude increases. For instance, a backorder delay that extends beyond 20 days results in the customer reducing future orders by 8.4% and an impact that endures for years. In addition, when a backorder occurs, the firm quotes a new delivery date, an estimate for when it expects the item to arrive. If customers are anchored to this new reference point, quoted delivery dates might play a role in managing their expectations. We examine whether there are any recovery or aggravating effects when the item arrives earlier or later than the quoted date and any evidence of loss aversion. Overall, we find little evidence that the firm can effectively manage customers’ expectations. Rather, some results indicate that conservatively quoted delivery dates might be negatively affecting customers. Last, we study how item popularity moderates the impact of backorder delays. We find that backorder delays that happen in the medium popularity category cause a stronger backlash in customers than those in the low or high popularity categories.

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  • 03/07/2019
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