Data mining uncovers patterns in data using predictive techniques. These patterns play a critical role in decision making because they reveal areas for process improvement. Using data mining, organizations can increase the profitability of their interactions with customers, detect fraud, and improve risk management. The patterns uncovered using data mining help organizations make better and timelier decisions.
Data mining helps SPSS customers solve business problems
SPSS data mining solutions and services have enabled hundreds of organizations to achieve remarkable results in many areas. For example, organizations have used data mining to:
- Boost sales by 50 percent and reduce marketing costs by 30 percent by uncovering cross-selling and "rollover" sales opportunities.
- Triple online profits by improving personalization features.
- Secure an additional $50 million in revenue by using an accurate propensity model to target offers.
- Improve the response rate of direct mail campaigns by 100 percent.
Using data mining tools
Most analysts separate data mining software into two groups: data mining tools and data mining applications. Data mining tools provide a number of techniques that can be applied to any business problem. Data mining applications, on the other hand, embed techniques inside an application customized to address a specific business problem. Regardless of whether we are aware of it, our daily lives are influenced by data mining applications. For example, almost every financial transaction is processed by a data mining application to detect fraud. Both data mining tools and data mining applications are valuable, however. Increasingly, organizations are using data mining tools and data mining applications together in an integrated environment for predictive analytics.
So what do data mining tools add? Data mining tools are used to ensure flexibility and the greatest accuracy possible. Essentially, data mining tools increase the effectiveness of data mining applications. Since no two organizations or data sets are alike, no single technique delivers the best results for everyone. Not only do data mining tools deliver in-depth techniques, but data mining tools also deliver flexibility to use combinations of techniques to improve predictive accuracy.
Because data mining tools are so flexible, a set of data mining guidelines and a data mining methodology have been developed to help guide the process. The Cross-Industry Standard Process for Data Mining (CRISP-DM) ensures your organization's results with data mining tools are timely and reliable. This methodology was created in conjunction with practitioners and vendors to supply data mining practitioners with checklists, guidelines, tasks, and objectives for every stage of the data mining process.
Clementine data mining transforms data into actionable results
Clementine, the SPSS data mining workbench, enables your organization to quickly develop predictive data mining models and deploy those data mining models into your organization's operations - improving decision making. Using Clementine's powerful, visual data mining interface and your business expertise, you can quickly interact with your data and begin discovering patterns you can use to change your organization for the better. To learn more about the Clementine data mining workbench, visit the comprehensive Clementine page.
The latest data mining advances—text mining and Web mining
Recent advances have led to the newest and hottest trends in data mining—text mining and Web mining. These two data mining technologies open a rich vein of customer data in the form of textual comments from survey research and log files from Web servers, which were previously unusable. Applying data mining to these data adds a richness and depth to the patterns already uncovered through your data mining efforts.