Direct Marketing Essay Example | Topics and Well Written Essays - 1500 words

Direct Marketing - Essay Example Data is generated by transactions that form the foundation of many industries, such as retail, manufacturing, utilities, transportation, insurance, credit cards, and banking. In addition to these internal data, external data sources also provide demographic, lifestyle, and credit information on retail customers, and credit, financial, and marketing information on business customers. Data mining is a very useful tool to analyse business data and to use it to identify key customers and in turn increase business opportunities by targeted marketing. From the direct marketing perspective, data mining and data fusion provide a necessary means to collect and analyse customers' data in order to utilise direct marketing strategy most effectively. Data mining and data fusion are the exploration and analysis of large quantities of data in order to discover meaningful patterns and rules (Berry et al., 2004). Used in various simpler forms in earlier times, mining and fusion have made way for businesses to finally make some sense out of all the data that they have accumulated for years (Rudd, 2000). Several books and journals have been published to strengthen the cause of these tools to improve relations between customers and generate more business. Some other areas where data mining and data fusion have been extensively used include credit scoring, direct marketing, sales forecasting, insurance, manufacturing, telecommunications, web-mining and text mining. An area pertaining to the scope of this paper is the application of data mining and fusion in direct marketing. Mining and fusion are useful tools in almost all aspects of a business and direct marketing. It helps in building supporting systems for day-today business. It is u seful in forecasting trends, it is used in decision making processes. It can be used in strategic planning of the course of action to be followed (Berson et al., 1999). A significant application in this area is the usage of data mining and fusion as tools in customer marketing and effective promotion (Berry et al., 2004). Customer acquisition is another such application. Statistical modeling using data mining and fusion are useful in effectively segmenting customers so that suitable marketing efforts can be carried out (Berson et al., 1999). Estimation of customer profitability is another such vital application of data mining. Determining customer segments help establish better marketing and services practice (Berson et al., 1999). Data mining and fusion thus help businesses to a very great extent in managing customers and helps maximise the tenure of relationship with customers, optimise the transactions or business carried out, increase profits associated (Berry et al., 2004). Mining is an activity that processes raw data or information recorded on a transactional basis. Earlier in smaller companies, this 'mining' process was carried out by people who dealt directly with customers. Now with companies that have billions of customers, a system has to be established to carry out these activities. Mining is a follow-up and is closely connected with data warehousing (Berson et al., 1999). If useless data has been collected and warehoused, the mining process will yield no better results. At the same time, having