Today's customers have such varied tastes and preferences that it is not possible to group them into large homogeneous populations to develop marketing strategies. In many cases, each customer wants to be served according to his or her individual needs. The technologies of data warehousing, data mining and other customer relationship management techniques bring new opportunities for businesses to act on the concepts of relationship marketing. Particularly through data mining - the extraction of hidden predictive information from large databases - organizations can identify valuable customers, predict future behaviours and generally be better placed to make knowledge-driven decisions.
Noori and Salimi propose a marketing decision support system with the following components:
• Customer profiling. This includes demographic details and the characteristics of purchasing transactions, which the marketer uses to decide on the right strategies and tactics to meet the customer's needs. By knowing how often a customer buys the company's products, the marketing manager can build targeted promotions such as frequent buyer programmes. Knowing how much the customer spends on a typical transaction helps the marketer to devote appropriate resources to the customer. By knowing if a significant time has elapsed since the customer last placed an order, the marketer can investigate the reasons why and take appropriate steps. If a marketer can identify typical customer groups, he or she can use a more specific marketing message. A marketer who uses data mining and knowledge discovery systems to support customer profiling can better compute customer lifetime values -a measure which provides greater understanding of what is happening to the size and value of a customer base. Moreover, customer databases can provide accurate information on the results of a marketing programme.
• Deviation analysis. A deviation can be an anomaly, fraud or a change such as the customer moving to a new house or new job. In the past, it was difficult for marketers to detect deviations in time to take corrective action. But data mining tools provide powerful means, such as neural networks, for detecting and classifying such deviations at a much earlier stage.
• Trend analysis. Advanced tools such as visualization help marketers to detect trends - sometimes very subtle trends - that would have been missed using traditional analysis tools such as scatter plots. In marketing decisions, trends can be used for evaluating marketing programmes or to forecast future sales. Advanced tools can bring to light, for example, a peak in sales of a product associated with a change in the profile or a particular group of customers.
• Systematic information-management framework. Customer relationship management involves identifying the right customers, differentiating among them, interacting with and learning from existing customers, and customizing the product or service to the needs of individual customers. All these are based on knowing customers better. Current efforts on customer relationship management focus on the customer interface and managing customer interactions. But inadequate information about customers and the lack of a systematic information management framework continue to hinder the efforts of organizations to manage their customer relationships.
• Data-mining component. Decision makers can access data warehouses and data marts using tools supporting online analytical processing.
• Internal, competitor and customer analyses. The development of effective marketing strategy involves conducting internal, competitor and customer analyses as preliminaries to formulating strategies for market segmentation, targeting and positioning.
Why doesn't marketing use the corporate data warehouse? The role of trust and quality in adoption of data-warehousing technology for CRM applications
A corporate data warehouse is a central repository of information from throughout a company. The information is typically used to perform, for example, trend analyses, forecasting and comparative analyses. A corporate data warehouse can be used to support such marketing functions as sales force automation, contact management, profitability analyses and analysis of customer preferences and profiles. For the data warehouse to support these functions properly, the information it contains must not only be good quality and of the right type, but also easily accessible by the marketing function. Through case study research involving a single health-care payer, Payton and Zahay seek to reveal why marketing tends not to use a corporate data warehouse as much as might be expected.
Three main factors explain half of marketing's disappointing use of the corporate data warehouse for customer relationship management.
First, marketing lacks trust in the data contained in the corporate data warehouse. Employees of the case study organization individually trust each other enough to interact on a daily basis, but the organization as a whole does not operate with a high degree of trust. Because of this, marketing is hesitant to use information prepared by the organization's information systems function.
Secondly, marketing perceives that the quality of the information in the corporate data warehouse is low. Data quality centres on the overall accuracy of the information, its timeliness, and that it should be easily accessible, easy to understand and believable.
Thirdly, marketing believes that its needs were not incorporated into the design of the data warehouse or data warehouse interface. The case study organization placed more emphasis on financial and billing applications than marketing. Marketing's unique needs in terms of analysing part customer performance, incorporating outside data sources into its analyses, analysing specific customer data and running targeted marketing campaigns are not the needs of the underwriting, billing and other financial and strategic functions of the organization. External, demographic and descriptive data, for the consumer market, and company-descriptive data, for the commercial applications, are missing from the data warehouse. Also missing is information on former and prospective customers. Because marketing is driving the future of the organization through using a variety of primarily customer-based data sources, and not reporting on its past using financial information, the factors predicting success of marketing's use of a central data warehouse differ from those that predict implementation success for other types of systems applications.
Other factors, which influence to a much lesser extent marketing's trust in the corporate data warehouse, include: data integration (10.2 per cent); top management support (7.94 per cent); the role of marketing in the organization (7.48 per cent); training (5.67 per cent); end-user support (5.22 per cent); the internal information technology support organization (4.08 per cent); and economic factors (3.17 per cent).
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