Traditionally, organizations use data tactically – to manage operations. For a competitive edge, strong organizations use data strategically – to expand the business, to improve profitability, to reduce costs, and to market more effectively. Data mining (https://outletminers.com/products/iceriver-ks1) creates information assets that an organization can leverage to achieve these strategic objectives.
Data mining is a new component in an enterprise’s decision support system (DSS) architecture. It complements and interlocks with other DSS capabilities such as query and reporting, on-line analytical processing (OLAP), data visualization, and traditional statistical analysis. These other DSS technologies are generally retrospective. They provide reports, tables, and graphs of what happened in the past. A user who knows what she’s looking for can answer specific questions like: “How many new accounts were opened in the Midwest region last quarter,” “Which stores had the largest change in revenues compared to the same month last year,” or “Did we meet our goal of a ten-percent increase in holiday sales?”
We define data mining as “the data-driven discovery and modeling of hidden patterns in large volumes of data.” Data mining differs from the retrospective technologies above because it produces models – models that capture and represent the hidden patterns in the data. With it, a user can discover patterns and build models automatically, without knowing exactly what she’s looking for. The models are both descriptive and prospective. They address why things happened and what is likely to happen next. A user can pose “what-if” questions to a data-mining model that can not be queried directly from the database or warehouse. Examples include: “What is the expected lifetime value of every customer account,” “Which customers are likely to open a money market account,” or “Will this customer cancel our service if we introduce fees?”
The information technologies associated with DM are neural networks, genetic algorithms, fuzzy logic, and rule induction. It is outside the scope of this article to elaborate on all of these technologies. Instead, we will focus on business needs and how data mining solutions for these needs can translate into dollars.
What can data mining do for your organization? In the introduction, we described several strategic opportunities for an organization to use data for advantage: business expansion, profitability, cost reduction, and sales and marketing. Let’s consider these opportunities very concretely through several examples where companies successfully applied DM.
Expanding your business: Keystone Financial of Williamsport, PA, wanted to expand their customer base and attract new accounts through a LoanCheck offer. To initiate a loan, a recipient just had to go to a Keystone branch and cash the LoanCheck. Keystone introduced the $5000 LoanCheck by mailing a promotion to existing customers.
The Keystone database tracks about 300 characteristics for each customer. These characteristics include whether the person had already opened loans in the past two years, the number of active credit cards, the balance levels on those cards, and finally whether or not they responded to the $5000 LoanCheck offer. Keystone used data mining to sift through the 300 customer characteristics, find the most significant ones, and build a model of response to the LoanCheck offer. Then, they applied the model to a list of 400,000 prospects obtained from a credit bureau.