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Data mining is simply the acquisition of information that is already present in your CRM (Customer Relationship Management System) that is intended to be utilized for marketing, customer service, customer informative services and similar applications.

It is important that you first determine just what your purpose is in wanting to glean the data so you can create a data quality plan and set about using the best procedure available to obtain the data.

Why Do We Need Different Data Mining Techniques In CRM

Data from the database of your CRM is going to be used to solve a problem. A problem may exist, for example, with communicating with your customers as to the benefits that they have in working with your company. It is important that your customers feel comfortable and happy about the relationship, and there may be factors about which they know nothing about that would help that cause.

You may want to market new concepts and products to your base and in order to do that it would be helpful to promote these new areas so that a general awareness will help to create a demand for new products and services.

When companies possess large quantities of data, it is impossible to process all of this information by hand, so the data is kept in a CRM system in order to process the information efficiently. Basically, it would be impossible to do all this without a data quality manager.

Pulling The Data

With most company data systems or enterprise systems, data can be pulled by asking the system for certain criteria such as zip codes, customer preferences, income strata, age, and family size, for example.

These criteria can be a category of people who match a specific product or service need that you have to offer, or a grouping of people who need to be aware of specific pieces of information. Once these individuals are identified, it is easy to get the specific information to them.

Sometimes data has anomalies as the result of searches that deviate from the expected outcome, and the results are not what was expected. This will indicate that additional data mining techniques in CRM are going to be required to make sense of what has just occurred. In turn, this analysis can help to discover just what relationship the current data has with other data, and it may show that the relationships shows predictable outcomes from unpredictable results.

In other words it can help to better understand customers behavior, habits, and provide help in the understanding of predicting their decisions. This can be one of the more important uses of data mining techniques in CRM as it is frequently used with point-of-sale data.

Another process that is helpful in the identification of sets of data that have similarities as well as differences is called clustering. As an illustration, if the behavior of one group of people as far as their purchases is similar to another grouping, then both groups can be targeted with similar products or services.

The data mining techniques in CRM, and then the relative comparisons of similarities and differences can be used to identify and analyze customer behavior so predictions can be made in regard to future behavior, which can add a great deal of value to communications with these individuals.

The author:

RingLead offers a complete end-to-end suite of products to clean, protect and enhance company and contact information, leading to improved organizational efficiency, reliable business intelligence, and maximized ROI on CRM and marketing automation investments.Since 2003 RingLead has helped solve the dirty data problems of large enterprises, Fortune 500 companies and small businesses across the globe.