What will your business actually do with large amounts of data that is gathered daily? Interpreting this data and locating the most valuable data is sometimes a very difficult task. But, data clustering can help with this. In this article, I will discuss about what is clustering in data mining, how businesses can utilize data clustering, examples of clustering in data mining, primary clustering techniques in data mining and customer clustering and clustering as a data mining solution.
What Is Clustering In Data Mining?
Clustering is the grouping of a specific group of objects depending on their characteristics, aggregating them in accordance with their similarities. When it comes to data mining, this methodology partitions the data by implementing a particular join algorithm that is highly suitable for the desired information analysis. Clustering places data into related groups usually without the advance knowledge of group definitions. The data points are assigned to different groups(few similar ones and others are dissimilar ones) by a data mining clustering algorithm.
How Businesses Can Make Use Of Data Clustering?
Clustering can enable businesses to handle their data much better. Information retrieval, market segmentation, grouping web pages and image segmentation are four examples. Data clustering helps with customer retention, sales campaigns and customer shopping behavior for retail businesses. Clustering is employed regularly in customer retention efforts, risk factor identification and fraud detection in the insurance industry. Clustering is used for analyzing customer profitability, credit scoring and customer segmentation in banking.
In order to build these groups or data clusters, data like income, age, occupation, housing, and race are often utilized in combination with census data. In the overall database, the clustering information can be utilized to “tag” customers. Eventually, the users can intuitively forecast how distinct clusters will react to marketing offers.
Clustering Examples In Data Mining:
Below here are two instances that explain how clustering techniques in data mining translate to useful insights for managers and business owners. In both the below cases, the practical application was recognizing a data record that is distinct from other groups.
In the first instance, a department store in Illinois, USA organized a sale that involved men’s suits. Except one store, all the others have shown an increment of at least 100% in sales revenues. The store that was the only exception utilized radio advertising rather than television.
In the second instance, with one exception that is notable, the wine distributors who sell cheap wine in New York city were gaining profit at a foreseeable level when seen in amalgamation with some shipping volumes. Because of data clustering, it was disclosed upon closer inspection that payments weren’t being taken in time from one of the customers.
Major Clustering Techniques In Data Mining And Customer Clustering:
The four main categories of clustering methods are grid-based, density-based, hierarchical and partitioning. Nevertheless, for marketing programs and customer relationship management(CRM), customer clustering emerges as the highly significant strategy.
Customer clustering utilizes purchase transaction data in order to track the buying behavior and then devise new business initiatives depending on findings. The general marketing goal is to focus on and retain high-profit, high-value and low-risk customers. This “premium” cluster that represents 10-20 percent of total buyers often generate 50-80 percent of an organization’s profit.
You must consider cross-selling and up-selling techniques once this cluster is identified. These techniques will further contribute to this attractive data cluster’s profitability potential.
Clustering As A Data Mining Solution For Businesses:
Data Clustering cannot be considered as a “stand alone” solution for your data mining efforts. You must not lose sight of critical goals such as achieving data quality. Also, you must keep an eye on the ball while you think about enhancing data mining and data management initiatives within your organization. You should also recognize what data mining operation is highly suitable for your particular needs before you even begin.
Business Application Of Clustering:
A grocer retailer utilized clustering to segment its 1.3MM loyalty card customers into 5 distinct groups depending on their buying behavior. It then adopted customized marketing strategies for every segment to target them more efficiently. One of the groups was known as “Fresh food lovers.” This consisted of customers who purchase a high proportion of organic food, salads, fresh vegetables etc. A marketing campaign that highlighted the freshness of vegetables and fruits and availability of organic produce in the stores throughout the year appealed to this customer group.
Another cluster was known as “Convenience junkies.” This consisted of people who shopped for easy-to-prepare, semi-cooked/cooked meals. A marketing campaign focusing on the retailer’s in-house line of frozen meals and the check-out counters’ speed at the store worked well with this audience. So, in this way, the retailer could provide right message to the right customer and maximize the efficiency of its marketing.