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What is the K-means Clustering Algorithm and How Does an Enterprise Use it to Analyze Data?
We wanted to provide a brief explanation of the K-means Clustering algorithm
What is the K-means Clustering algorithm?
The K-means Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. K-means Clustering is a grouping of similar things or data. For example, objects within group 1 (cluster 1) shown in image below should be as similar as possible.
But there should be much difference between an object in group 1 and group 2.
The attributes of objects decide which objects should be grouped together. This method is used to find groups that have not been explicitly labeled in the data, and it can be used to confirm business assumptions about what types of groups exist, or to identify unknown groups in complex data sets. Once the algorithm has been run and the groups are defined, any new data can be easily assigned to the correct group.
How Does an Enterprise Use the K-means Clustering Algorithm to Analyze Data?
In order to understand how best to make use of this algorithm; let’s look at some general examples, followed by some business use cases.
- Loan applicants in a bank might be grouped as low, medium, and high risk applicants based on applicant age, annual income, employment tenure, loan amount, the number of times a payment is delinquent etc.
- A movie ticket booking website can group users into frequent ticket buyers, moderate ticket buyers and occasional ticket buyers, based on past movie ticket purchases.
K-means Clustering can be applied to segment customers by purchasing history, segment users by the activities they perform on a website, define demographic profiles based on interests, and recognize market patterns.
Use Case – 1
Business Problem: Organizing customers into groups/segments based on similar traits, product preferences and expectations. Segments are constructed on basis of the customers’ demographic characteristics, psychographics, past behavior and product use behaviors.
Business Benefit: Once the segments are identified, marketing messages and even products can be customized for each segment. The better the segment(s) chosen for targeting by a particular organization, the more successful it is assumed to be in the market place.
Use Case – 2
Business Problem: Discount Analysis and Customer Retention will help the organization to target discounts to specific customers and the business will need to visualize ‘segments of sales group based on discount behavior’ and ‘customer churn to identify segments of customers on the verge of leaving’.
Business Benefit: The business marketing team can focus on risky customer segments in an efficient way in order to avoid losing those customers. Sales team segments that are facing challenges based on any current discounting strategy can be identified and a deal negotiation strategy can be improved and optimized.
The K-means Clustering algorithm is very useful in identifying patterns within groups and understanding the common characteristics to support decisions regarding pricing, product features, risk within certain groups, etc.