Data Mining Techniques in CRM to Improve the Data Quality Management

As the need for quality and quantity of content increases profoundly, it eventually becomes difficult to track and derive insights from the heaps of data collected by businesses. A remarkable modern invention to deal with this problem is now available in the form of data mining.

Data mining is capable of diving deep into the databases and providing proactive, detailed insights and information. With the increasing acceptance of customer relationship management (CRM) solutions and advancement of technology, data mining has begun to be accepted as a part of CRM.

Before implementing any of the data mining techniques, it is pertinent to understand each one of them and depending upon the desired outcome, businesses may choose any one or few of the below mentioned data mining techniques.

Classification
As the name suggests, this data mining technique is useful for classifying the data and placing it into appropriate categories. For instance, the emails can be classified using this technique so that any specific senders, certain words, or some IP addresses are classified as spam mails.

Anomaly Detection
If you desire to locate items of specific interest that didn’t match expected behavior or a projected pattern, anomaly detection is the technique that you can use. These items so located are known as anomalies, outliers, or exceptions. These anomalies become a good indicator when additional analysis is sought for.

Association rule learning
With large databases at your discretion, and for the desire to derive relationships between the different elements thereat, this technique can be put to good use. A great aid to discover hidden patterns, this technique is one of the most important and commonly used one. The insights derived from this technique can be used to understand the behavior and expectations of your customers and also make predictions of their impending decisions.

Regression
Regression technique, one of the most advanced data mining techniques, is useful to locate dependency between different items of the data. However, it differs considerably from correlation or association. While it can effectively measure the variables which are affected by other variables, however, it cannot provide insights regarding the variables which can affect others.

Clustering
Compared to other techniques, this one is more complex in nature. The clusters identified by this method provide detailed insights for both, the similarities as well as the differences in the data.

The data identified with similarities can be targeted for beneficial usage and conversion rates. For instance, if the data reveals similar traits in the buying behavior of two or more groups, all of them can be targeted with same products or services.

Conclusion
The complexity of these techniques would have perhaps made it clear that data mining is much more than a simple analysis of a given database. These techniques can be useful to derive data, restructure it, and derive actionable insights to understand and predict customer behavior.

Authored by Mia Greer. Mia has been in the data mining industry for almost two years now. He has learned one thing, he says that data is power. If a small, failing business knows how to mine data effectively and leverage that information- it can dash ahead of the biggest of industry leaders. His motivating power and working skill are unmatched in data mining.