K-means is a powerful algorithm which helps you to stay one step ahead of your competitors. Online is a robust platform where marketing and business will always go together. Every businessman will always try to outrun one another through newer adoptions.
E-commerce business is one such affair where it is very much necessary to hold the interests of potential customers efficiently. There is only one mantra in online business, which is the conversion rate. To define conversion rate in simple words, it is a process wherein a customer actually purchases or does some action apart from a simple visit. But it is also necessary that to have more conversion rates the websites should make up for the shortcomings through analyzing the customer preferences. Particular page visits, purchase history, duration of the visit, conversion rate or to be more discreet the number of orders made are to be analyzed. Well, when all these data are analyzed it will be possible to make necessary implementations in accordance with the requirements.
One can find a large amount of non-linear data to deal with. And, it will not be an easy task to segregate all these accordingly. No, this cannot be performed manually, because that will be bound to generate more errors. One of the best ways to tackle this drawback is through K-means algorithm.
K-means Clustering Algorithm
One of the best features of K-means algorithm is, it has a very simple procedure to follow. You are required to provide the data, define number of clusters and the rest will be automatically performed by the algorithm. The major action of this algorithm is customer segmentation. And having knowledge about customer needs will play an important role in building a successful venture.
Customer segmentation is the process where segregation of data is performed based on different interests of potential customers. The whole process involves various actions that help to predict the future actions. We can get a huge amount of unstructured data, so once this data is provided to K-means algorithm it effectively analyses and integrates the data to give a structured appearance. And if the user requests for a particular set of information then, properly analyzed data will be provided.
How the Algorithm Works?
Once the number of clusters is defined, each cluster will be allocated with one K-center. These centers should be positioned in such a way that each of them would provide multiple results. For better explanation let’s take an easy approach,
- Variables that are to be used in clusters should be initially defined
- The distance between variables and customers should be calculated
- The set standard procedure should be applied
- Zero-in on the number of clusters to be included
- Map and analyze these clusters
Based on the above process it is easier for a person to define the goals and requirements for a business. Understanding the customer behavior is considered as the major stepping stone in defining a successful venture. Creating dissimilar groups with a similarity between them which is based on the object attributes helps to easily recognize the overall pattern. On the whole, when a given set of data is organized, there should be high intra-cluster similarity and low inter-cluster similarity.
