What does your business do with the huge volumes of data collected daily? Analyzing this information and discovering the most important data is not always an easy task, but data clustering can help. In this article, DataEntryOutsourced provides some background about this essential data management strategy.
What Is Clustering in Data Mining?
The use of clustering involves placing data into related groups – typically without advance knowledge of group definitions. A data mining clustering algorithm assigns data points to different groups, some that are similar and others that are dissimilar.
How Businesses Can Use Data Clustering
Clustering can help businesses to manage their data better – image segmentation, grouping web pages, market segmentation and information retrieval are four examples. For retail businesses, data clustering helps with customer shopping behavior, sales campaigns and customer retention. In the insurance industry, clustering is regularly employed in fraud detection, risk factor identification and customer retention efforts. In banking, clustering is used for customer segmentation, credit scoring and analyzing customer profitability.
To build these data clusters or groups, data such as race, housing, occupation, age and income are often used in combination with census data. The clustering information can be used to “tag” customers in the overall database. Eventually users will be able to intuitively forecast how different clusters will react to marketing offers.
Examples of Clustering in Data Mining
Here are two examples that illustrate how clustering techniques in data mining often translate to helpful insights for business owners and managers. In both cases noted below, the practical application was identifying a data record that is different from the other groups.
In the first example, a department store in Illinois held a sale involving men’s suits. All stores except for one displayed an increase of at least 100 percent in sales revenues – the one exception was the only store to use radio advertising rather than television.
In a second example, with one notable exception wine distributors selling inexpensive wine in New York were producing a predictable level of profit when viewed in combination with certain shipping volumes. Upon closer inspection as a result of data clustering, it was revealed that payments were not being collected in a timely fashion from one of the customers.
Major Clustering Techniques in Data Mining and Customer Clustering
The four major categories of clustering methods are partitioning, hierarchical, density-based and grid-based. However, for customer relationship management (CRM) and marketing programs, customer clustering emerges as the most important strategy.
Customer clustering uses purchase transaction data to track buying behavior and then create new business initiatives based upon findings. The typical marketing goal is to emphasize and retain low-risk, high-value and high-profit customers – this “premium” cluster representing 10-20 percent of total buyers often produces 50-80 percent of a company’s profit.
Once this cluster is identified, you should consider up-selling and cross-selling techniques that will further contribute to the profitability potential of this attractive data cluster.
Clustering as a Data Mining Solution
Of course, data clustering is not a “stand alone” solution for your data mining efforts. You can never afford to lose sight of critical goals like achieving data quality – professionals like DataEntryOutsourced can help you “keep the eye on the ball” when you are thinking about improving data management and data mining initiatives within your company. DEO can also help you identify which data mining operation is most appropriate for your specific needs – before you even start.
Please leave your data clustering and data mining experiences below and then use social media icons to share your comments.
-Â DataEntryOutsourced
Prev
