Data mining software Data mining has the potential to give businesses a competitive edge in Customer Relationship Management. Organizations use methods such as complex algorithms, artificial intelligence, and statistics to mine meaningful patterns from large sets of data. These patterns can then be utilized to do a number of things including targeting customers by predicting future behavior and learning more about present behavior.
One widely accepted model is the Cross-Industry Standard Process for Data Mining (CRISP-DM) which has six phases: business understanding, data understanding, data preparation, model building, testing and evaluation, and deployment. These six phases are shown in this diagram that was included in the Data Mining Process article.
The process starts with understanding the goals and needs of the business in order to develop a plan. This usually involves identifying a problem or gap in knowledge that the company wants fixed . The second phase is collecting, describing, sorting, and verifying the data. Next is selecting and formatting the relevant data to prepare it to build models. Modeling is when companies apply data mining software tools to the collected data in order to understand it better and create more sophisticated modeling. The results are then evaluated according to the original objective created when understanding the business. If successful then the data can be applied to the problem.
This isn 't a rigid process and companies tend to flow between parts as needed, even skipping over some or doubling back as new insights are gained. As new data is discovered or changes happen over time companies may need to re-do their models to ensure that their original findings are still relevan...
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...e out profits generated by angels” leading to the conclusion that the wide net cast by their marketing team that focused on quantity over quality of customers was actually hurting the business. In response to the findings Best Buy began implementing a number of changes to deter devil customers including removing them from mailing lists and decreasing promotions that tended to draw them in.
After the devils were dealt with it was back to data mining to figure out how to cater to the company 's angels better. By looking at the types of purchases, amount, and when, helped identify desirable customers and divide them into segments. Best Buy was now able to identify upper-income men, suburban moms, and male technology enthusiasts along with the products that appealed to them the most like cameras, products that enhance family time, and the latest gadgets respectively.
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