Customer Segmentation Using Machine Learning

Customer Segmentation is the process of division of customer base into several groups called as customer segments such that each customer segment consists of customers who have similar characteristics. Segmentation is based on the similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits. The customer segmentation has the importance as it includes, the ability to modify the programs of market so that it is suitable to each of the customer segment, support in business decisions; identification of products associated with each customer segment and to mange the demand and supply of that product; identifying and targeting the potential customer base, and predicting customer defection, providing directions in finding the solutions.


I. INTRODUCTION
Over the years, as there is very strong competition in the business world, the organizations have to enhance their profits and business by satisfying the demands of their customers and attract new customers according to their needs. The identification of customers and satisfying the demands of each customer is a very complex and tedious task. This is because customers may be different according to their demands, tastes, preferences and so on. Instead of "one-size-fits-all" approach, customer segmentation clusters the customers into groups sharing the same properties or behavioural characteristics. Purchase frequency etc.) [1] from shopping vendors.
Step 2: Preprocessing of Data: Pre processing of the data is one of the important step for the accuracy of predictive model. In this step, the collected data will be cleaned and relevant features will be extracted. The K-Means algorithm is relatively simple. The "K" cluster points, which will be the centroids, are placed in the space among the data points. Each data point is assigned to the centroid for which the distance is the least. After each data object has been assigned, centroids of the new groups are recalculated. The above two steps are repeated until the movement of the centroid ceases. This means that the objective function of having the least squared error is completed and it cannot be improved further. Hence, we get K clusters as a result.
K-Means algorithm aims at minimizing an objective function, which here, is squared error. It is an indicator of the distance of the data points from their respective cluster centers. The process in this algorithm always terminates but the relevance or the optimal configuration cannot be guaranteed even when the condition on the objective function is met.
The algorithm is also sensitive to the selection of the initial random cluster centers. That is why it runs multiple times to reduce this effect but for a large number of data points, it tends to perform very well even though it is iterative. One major advantage of K-Means clustering is that the computational speed of this algorithm is higher than other hierarchical methods of clustering and it is also easy to implement.
The algorithm works as follows: Step-1 : Specifying the number of clusters -k value.
Step-2 : Centroids are initialized by shuffling the dataset and then randomly selecting k data points for the centroids without replacement. Step

III. RESULTS AND DISCUSSION
The goal of customer segmentation is to identify the customer's behaviour and buying patterns which indirectly helps in boosting the sales of the company. I.

IV. CONCLUSION
Customer segmentation is a way to improve communication with the customer, to know the wishes of the customer, customer activity so that appropriate communication can be built. Customer Segmentation needed to get potential customers used to increase profits.
K means clustering is one of the most popular clustering algorithms and usually the first thing practitioners apply when solving clustering tasks to get an idea of the structure of the dataset. The goal of K means is to group data points into distinct nonoverlapping subgroups. One of the major application of K means clustering is segmentation of customers to get a better understanding.