Data Science at its Zenith!
Customers are the most important assets for a business organization. The number of consumers of services offered by a firm decides the overall business value and hence, terms like customer lifetime value modelling and churn prediction came into existence. For example, porting existing network SIM (Subscriber Identification Module) into another network is very popular nowadays. If a customer is unsatisfied with Airtel’s services, he/she can port his SIM into any other network like Jio, Idea, etc. Now even before this customer changes the network, Airtel can predict all this and suggest some value packs to maintain a long term relationship with him/her. So now you might be wondering about the ways to come up with some strategies to deal with quantitative analysis of customers’ behaviour. Machine learning can do that for you. Data Science techniques can be employed to predict customer churn and build customer lifetime value models. Ready to know more? Let’s jump in.
Understanding Customer Lifetime Value (CLTV)
In terms of marketing, Customer Lifetime Value or CLTV is an estimation of the net profit to an organization taking into account the entire future relationship with a customer. It can also be defined as the monetary value of a customer relationship, based on the present consumption of services by the user.
Suppose a person subscribes to Netflix and starts watching some movies and shows. Based on the history of subscribers, Netflix can predict how long that customer will remain as a subscriber, and subsequently, it can estimate the profit for that particular customer. Even Netflix can now plan its future course of action to elongate the relationship with the customer.
What is Customer Churn and Retention?
In a specified period, the customer churn rate is defined as the percentage of customers who end their relationship with the company. Customer retention is the exact opposite of customer churn. It means retention involves the process of retaining customers by the company who tend to end their relationship with it.
With the increasing significance of customer satisfaction in the business world, learning how to predict churn plays a vital role in the success of a firm.
Machine Learning Algorithms for CLTV Modelling and Churn Prediction
Regression model for CLTV:
In statistics, regression analysis is defined as the process of finding a relationship between variables by plotting data points to find key patterns. A regression model can be built for existing customers. Python supports several built-in libraries to accomplish this task. For example, NumPy, Pandas, Matplotlib for plotting data, etc.
Classification techniques for Churn Prediction:
As the name suggests, classification algorithms in machine learning are used to classify data. It is a form of supervised learning and hence, it uses labelled data. Based on historical data of customers, classification algorithms can predict whether a particular customer will opt out of the services or not. These algorithms predict a customer’s behaviour by analyzing the behaviour of similar customers. They are further classified into several categories:
Logistic Regression: It is used to describe the relationship between a binary response variable and a set of predictor variables.
KNN: The k-nearest neighbour algorithm is used to solve classification and regression problems with an assumption that similar things exist in close proximity.
Decision Tree: It is a data structure consisting of internal nodes as attributes, branches as outcomes, and leaf nodes as decisions.
Support Vector Machines (SVM): It is a supervised machine learning model to maximize the margin between the data points and the hyperplane.
Benefits of CLTV models for Business Organizations
-Assessing the financial value of customers
-Fruitful decision making
-Marketing resources allocation
-Maximizing company’s profit
We are on the brink of a digital age. Every day, hundreds of thousands of startups take birth. Companies are hiring data analysts to ensure the minimum loss of their customers. With all this taking place, only the best in the market will survive. The bottom line is that user satisfaction matters the most today. No one is going to continue the relationship with any company if that person is not satisfied with its services. Finally, there are two aspects to it. We have covered the first one in this blog. The second one is the Product Recommendation. Wanna know about it as well? Click here.
Python, Tensorflow, Azure ML, R, MatLab.
Kaggle, Jupyter lab, RapidMiner, Anaconda, R-Studio, IBM Watson Studio.