Knowledge that Transforms

Brand loyalty and the more modern topics of computing customer lifetime value and structuring loyalty programs remain the focal point for a remarkable number of research articles. At first, this research appears consistent with firm practices. However, close scrutiny reveals disaffirming evidence. Many current so-called loyalty programs appear unrelated to the cultivation of customer brand loyalty and the creation of customer assets. True investments are up-front expenditures that produce much greater future returns. In contrast, many so-called loyalty programs are shams because they produce liabilities (e.g., promises of future rewards or deferred rebates) rather than assets. These programs produce short-term revenue from customers while producing substantial future obligations to those customers. Rather than showing trust by committing to the customer, the firm asks the customer to trust the firm—that is, trust that future rewards are indeed forthcoming. The entire idea is antithetical to the concept of a customer asset. Many modern loyalty programs resemble old-fashioned trading stamps or deferred rebates that promise future benefits for current patronage. A true loyalty program invests in the customer (e.g., provides free up-front training, allows familiarization or customization) with the expectation of greater future revenue. Alternative motives for extant programs are discussed.

The rapid advance in information technology now makes it feasible for sellers to condition their price offers on consumers’ prior purchase behavior. In this paper we examine when it is profitable to engage in this form of price discrimination when consumers can adopt strategies to protect their privacy. Our baseline model involves rational consumers with constant valuations for the goods being sold and a monopoly merchant who can commit to a pricing policy. Applying results from the prior literature, we show that although it is feasible to price so as to distinguish high-value and low-value consumers, the merchant will never find it optimal to do so. We then consider various generalizations of this model, such as allowing the seller to offer enhanced services to previous customers, making the merchant unable to commit to a pricing policy, and allowing competition in the marketplace. In these cases we show that sellers will, in general, find it profitable to condition prices on purchase history.

We provide a fully personalized model for optimizing multiple marketing interventions in intermediate-term customer relationship management (CRM). We derive theoretically based propositions on the moderating effects of past customer behavior and conduct a longitudinal validation test to compare the performance of our model with that of commonly used segmentation models in predicting intermediate-term, customer-specific gross profit change. Our findings show that response to marketing interventions is highly heterogeneous, that heterogeneity of response varies across different marketing interventions, and that the heterogeneity of response to marketing interventions may be partially explained by customer-specific variables related to customer characteristics and the customer’s past interactions with the company. One important result from these moderating effects is that relationship-oriented interventions are more effective with loyal customers, while action-oriented interventions are more effective with nonloyal customers. We show that our proposed model outperformed models based on demographics, recency-frequency-monetary value (RFM), or finite mixture segmentation in predicting the effectiveness of intermediate-term CRM. The empirical results project a significant increase in intermediate-term profitability over all of the competing segmentation approaches and a significant increase in intermediate-term profitability over current practice.

Sales territory alignment is the assignment of accounts and their associated selling activities to salespeople and teams. Models, systems, processes, and wisdom have evolved over 1,500 project implementations for 500 companies with 500,000 sales territories. Optimization models have evolved over time to explicitly consider travel time along road networks and customer disruption. Personal computers with continually increasing speeds and storage capabilities, the Internet, and mapping databases have enabled the development of systems that communicate alignments visually to sales managers. Because of their combinatorial complexity, multiple conflicting objectives, and personnel aspects that touch everyone in the salesforce, the alignment models were unable to completely solve the sales territory alignment issues faced by companies. Consequently, processes that add local managerial knowledge were used to communicate and enhance model-derived solutions, while achieving very high implementation rates. The territory alignment team gains knowledge with every sales territory alignment. Alignment insights get codified. Alignment experts improve every model-derived solution. This wisdom becomes part of subsequent alignments and triggers further innovation. Over time, the role of processes and wisdom becomes larger than the role of the models and systems.