that allow the prediction of future behavior, events or results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of promotional activities, and profitability analysis by customer, by type of customer, by product and by division (Definition of data mining, online).
Data mining tools can provide companies with a competitive advantage. For example, data mining can identify a company's most profitable products or customers, and it can identify products that need to be sold at a higher price or dropped from the company's product catalog. Arthur Sturm (2004) comments that some companies spend millions of dollars trying to attract new customers, and a fraction of that amount on trying to retain existing customers. Sturm suggests in HealthCare Financial Management that data mining can help identify a company's best customers, what those customers need immediately, and what they are likely to want or need in the future. Data mining can do the same for a company's products. It can identify a company's most profitable products and suggest that additional resources should be used to support that product. Data mining can also provide insights about products that are not as profitable as most managers believed they were (Sturm, 2004, 100).
Many companies understand that increases in revenue and market share alone do necessarily result in profitability and long-term success; not all products provide the same rate of return. A popular product or line of products may be a money loser based on improper cost accounting cost allocation assumptions. Data mining could provide the solution by helping to identify all of the costs associated with producing, selling, distributing and collecting on sales made of a particular product. It is also important to remember that customers generating significant profits that happen to demand little in the way of after sales service or price concessions are mor...