Building with bricks and mortar: the revenue impact of opening physical stores in a multichannel environment

A crucial decision firms face today is which channels they should make available to customers for transactions. We assess the revenue impact of adding bricks-and-mortar stores to a firm's already existing repertoire of catalog and Internet channels. We decompose the revenue impact into customer acquisition, frequency of orders, returns, and exchanges, and size of orders, returns, and exchanges. We use a multivariate baseline method to assess the impact of adding the physical store channel on these revenue components. As hypothesized, store introduction cannibalizes catalog sales and has much less impact on Internet sales. Also as hypothesized, returns and exchanges increase. Interestingly, transaction sizes of purchases, returns, and exchanges do not change. The “availability effect” produces a net increase in purchase frequency across channels. This more than compensates for increased returns, producing a net increase in revenues of 20% by adding the store channel. Our findings yield a deeper understanding of the revenue relation between channels, and of the dynamic cross-channel effects of marketing actions.pre-prin

Sales promotions and channel coordination

Consumer sales promotions are usually the result of the decisions of two marketing channel parties, the manufacturer and the retailer. In making these decisions, each party normally follows its own interest: i.e. maximizes its own profit. Unfortunately, this results in a suboptimal outcome for the channel as a whole. Independent profit maximization by channel parties leads to a lack of channel coordination with the implication of leaving money on the table. This may well contribute to the notoriously low profitability of sales promotions. This paper first shows analytically why the suboptimality occurs, and then presents an empirical demonstration, using a unique dataset from an Efficient Consumer Response (ECR) project; ECR is a movement in which parties work together to optimize the distribution channel). In this dataset, actual profit is only a small fraction of potential profit, implying that there is a large degree of suboptimality. It is important that (1) channel parties are aware of this suboptimality; and (2) that they have tools to deal with it. Solutions to the channel coordination problem should ensure that the goals of the individual channel parties are aligned with the goals of the channel as a whole. The paper proposes one particular agreement for this purpose, called proportional discount sharing. Application to the ECR data shows a win-win result for both the manufacturer and the retailer. Recognition of the channel coordination problem by the manufacturer and the retailer is the necessary starting point for agreeing on a way of solving it in a win-win fashion

Choice Models and Customer Relationship Management

Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers' and vendors' needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm's products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplacePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47023/1/11002_2005_Article_5892.pd

Does Multichannel Produce More Profitable Customer?

One of the most intriguing and managerially relevant findings in the multichannel customer management literature is the positive association between the multichannel customer and profits. The question is whether this is an actionable, causal relationship, specifically, whether marketing campaigns can be designed to turn single-channel customers into multichannel customers, and in turn whether these multichannel customers will become more profitable to the firm. The purpose of this research is to conduct a field experiment to investigate this question. We design a field test where we vary the incentive (financial vs. non-financial) for inducing the customer to become multichannel, and the targeting of the incentive (random assignment vs. assignment based on a model developed to identify the customers who are expected to generate the most revenues if they become multichannel). The field test is set to begin early in 2011. We discuss the design of the experiment, the model-based treatment, and the anticipated results

Does Multichannel Usage Produce More Profitable Customers?

One of the most intriguing and managerially relevant findings in the multichannel customer management literature is the positive association between the multichannel customer and profits. The question is whether this is an actionable, causal relationship, specifically, whether marketing campaigns can be designed to turn single-channel customers into multichannel customers, and in turn whether these multichannel customers will become more profitable to the firm. The purpose of this research is to conduct a field experiment to investigate this question. We design a field test where we vary the incentive (financial vs. non-financial) for inducing the customer to become multichannel, and the targeting of the incentive (random assignment vs. assignment based on a model developed to identify the customers who are expected to generate the most revenues if they become multichannel). The field test started at the beginning of January 2011. We discuss the design of the experiment, the model-based treatment, and the preliminary results

The Impact of Customer Multichannel Choices on Revenues and Retention

There is broad agreement among researchers and practitioners of a positive relationship between multichannel shopping behavior and sales volume. However, this issue needs to be analyzed more thoroughly in two ways: (1) Is this relationship due to improved retention or higher revenues? (2) Which multichannel choice combinations have particularly strong impact, i.e. which hit the \u201csweet spot\u201d of improving both retention and revenues? We develop a joint model of channel choice, quantity decisions and contract renewal (i.e., retention) to answer these questions. We follow a two-stage approach. Fist we represent channel choice and renewal through a multivariate probit model. Second, we model revenues (dollar spent) through a random effects Tobit model where all possible multichannel combinations (measured by channel-combination purchase probabilities derived from the probit model) are included as covariates. Data were obtained from a major European retailer operating across three channels \u2013 Internet, store, and catalog. We study a cohort of new customers whose transactions were longitudinally tracked for four-and-a-half years. We find evidence of a tradeoff between revenues and retention for some multichannel combinations. In particular our results show that certain multichannel choices increase revenues but decrease retention. On the other hand, there are some multichannel combinations that enhance both revenues and retention. In general, our results suggest that a vaguely defined strategy of encouraging customers to be multichannel could actually hurt retention, revenues, and hence overall sales volume

Customer Evolution in Sales Channel Migration

We study how the consumer decision process for channel choice and response to marketing communications evolves for a cohort of new customers. We assume a newly acquired customer\u2019s decisions are described by a \u201ctrial\u201d model, but the customer\u2019s choice process evolves to a \u201cpost-trial\u201d model as the customer learns his or her preferences and becomes familiar with the firm\u2019s marketing efforts. The trial and post-trial decision processes are each described by different logit choice models, and the evolution from the trial to post-trial model is determined by a customer-level geometric distribution that captures the time it takes for the customer to make the transition. We utilize data for a book retailer who sells in three channels \u2013 retail store, the Internet, and via catalog. The model is estimated using Bayesian methods that allow for cross-customer heterogeneity. This allows us to have distinct parameters estimates for a trial and an after trial stages and to estimate the quickness of this transit at the individual level. The results show for example that the customer decision process indeed does evolve over time. Customers differ in the duration of the trial period and marketing has a different impact on channel choice in the trial and post-trial stages. Insights from this study can help managers tailor their marketing communication strategy as customers gain channel choice experience