Relationship Marketing and Data Quality Management
Khalil, Omar E. M., Harcar, Talha D., SAM Advanced Management Journal
In today's turbulent environment of global business, successful organizations must be able to provide high-quality products and services on the basis of personal knowledge of customers. Customer service needs to be constructed on the premise that consumers are increasingly demanding to be treated as individuals, not just as members of a large group. Consequently, personalization - the social content of interaction between service or retail employees and their customers - becomes an important mediator of customer satisfaction and patronage behavior. Successful implementation of such relationship marketing, however, demands the integration of timely and accurate market, consumer, and product information. In response to the growing demand for integrated information, organizations have expended great effort to utilize the latest information technology (IT) to build and maintain information systems. Effective use of such systems requires a high degree of data quality, defined as data that is fit for use by data consumers (Strong, Lee, & Wang, 1997). Many managers, however, are unaware of the quality of the data they use and perhaps assume that IT ensures that the data is perfect. Customer data may be of poor quality if it does not reflect real world conditions or is not easily used and understood by the data user. Poor quality data can cause immediate economic harm and also have more indirect, subtle effects.
Organizations, however, have largely ignored the issue of poor data quality. The need for data quality management arises as the focus shifts from production process to customer concerns. Achieving customer satisfaction mandates quality production policies and production systems, and these, in turn, necessitate quality data policies and systems. Therefore, to successfully implement a relationship marketing strategy, corporate management should develop data quality policy, identify the critical data quality requirements, and establish quality data production systems.
This paper discusses relationship marketing as a relatively new marketing strategy, emphasizing the importance of high-quality data to the implementation of such strategy, the problem of poor data quality, and data quality management (DQM) as an approach to dealing with such a problem.
The history of marketing can be broken up into three periods in terms of buyer and seller relationships: the simple trade era, the mass production era, and the new marketing era. The simple trade era was a real relationship between the buyer and the seller. Retailers, bankers, and auto dealers knew their customers personally, understood what they wanted, and satisfied their needs through personalized service. The proprietor of a general store, a bank, a barbershop, or a town livery stable visualized business primarily in terms of share of customers. For example, a local grocer's business was built on a one-to-one relationship with individual customers and what the grocer knew about each of them. This allowed the retailer to solve problems for customers, to give special attention to the most valuable customers, to sell them more products, and to adjust the service or product offerings to meet their evolving needs. Therefore, the pre-twentieth-century store owners were relationship marketers who nurtured customers as individuals and had to keep data about their customers in their head (Peppers & Rogers, 1995).
After the onset of the Industrial Revolution, most companies had moved into the mass production era. During this era, customers traded relationships for greater variety and lower prices. The advent of mass media in the early part of this century undercut the relationship between customer and companies and replaced it with mass marketing techniques. Standardized messages could be communicated to millions of people. A branded product that used a symbol took the place of a relationship, and consumers became statistics in the marketers' databases (Alanzo, 1994). …