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KNOWING WHEN TO LOWER PRICES OR RAISE SERVICE OFFERINGS

DBR | 1호 (2008년 1월)
A version of this article was originally published by IESE Insight(http://insight.iese.edu)
 
A study of the mobile phone industry by the consultancy Bain and Company showed that customers with higher usage levels were more likely to change providers than those with lower usage levels. This surprising result seems to go against the received wisdom that the more a customer uses the same provider, the less likely he is to switch. The same has also been observed in a financial services setting, among many other fields. Who's right?
 
Sure, customer satisfaction has a proven impact on customer retention, but findings like the Bain study and others would seem to add another shade to the picture. Gabriel Bitran, Paulo Rocha e Oliveira and Ariel Schilkrut decided to explore the dynamics of service quality and usage, customer retention and profitability.
 
They created and employed a mathematical model, which takes customer satisfaction and the depth of the relationship with the service provider as paths toward measuring the impact of price and service level on a company's profitability. Their analysis sheds light on the underlying dynamics governing service delivery systems and the optimality of price and quality-based managerial decisions. It provides a rational basis for the previous findings, proving counter-intuitively how an increase in service or a decrease in price may result in a smaller customer base but higher profits.
 
The authors argue the following: When a company experiences a decreased customer base after changing prices or the level of service quality, the resulting increase in profits may stem not just from a decrease in costs, but from the indirect effects of the lower prices or higher level of service on the customer's behavior.
 
The key to the analysis is the development of a model in which customers choose the depth of their relationship with the company based on their satisfaction level. While companies are wont to tout deep, personalized relationships with subscribers, these types of relationships actually increase the tension on the capacity-constrained service delivery model. Thus, when a satisfied customer chooses to pursue a deeper relationship, adopting more and more services, the service provider is forced to either lower service quality or invest in capacity to meet the subscriber's needs.
 
Interestingly, the authors argue that higher usage intrinsically leads to a higher number of service failures, thereby eroding a company's future profits. In addition, increased use of a service facility can lead to a decrease in supplier responsiveness and service quality or higher costs in the form of further investments to prevent such failures.
 
The analysis thus provides managers with insight into how to tweak quality service levels while maximizing the value of their customer base and ultimately pursuing higher profits.
 
An increase in quality or a decrease in price will make services more valuable to customers, but the effects of these policies on the long-term financial performance of a firm are not easily determined.
 
"A decrease in the number of customers does not always lead to lower profits," the authors write. "The number of customers that a company has can actually be a remarkably poor indicator of the value of the customer base."
 
Given the tangled relationship among all the variables, it is possible that managers may see their profits drop while their customer base is growing, and vice versa. The authors recommend actions that managers can take to achieve optimality between pricing and quality.
 
The key variable is the type of relationship a customer has with the service provider, which is based on each person's perception of service quality. The authors remind managers that they must take care of their service-quality levels in order to differentiate their service offerings.
 
This study shows managers how devising and implementing pricing and quality level policies can be mathematically modeled and tested. And such methodologies can be applied to test optimality in other fields, such as marketing spending in relation to new subscription rates. The authors are currently testing the results of their paper in situations where customers interact with the firm through multiple channels.
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