ML in E-Commerce LTV


The market today is very customer-centric, and hence, it is now essential to know the Customer Lifetime Value for a business to survive and flourish. Nowadays, customer lifetime value is monitored by 69% of organizations, according to a survey, but those organizations do it inefficiently.
Instead, 81% of those companies that excel at the precise measurement of the LTV increase their sales. The Customer Lifetime Value is a figure that helps predict, using machine learning, the lifetime value or profitability that a customer can offer. It will help any firm to focus more on profitable customers through customer retention and secure all the potential profits they can bring to the organization.

This value, including the potential purchases and revenues, maximizes the efficiency of an organization by providing the total flow of cash of any particular customer in consideration, making it is pretty easy to evaluate customer retention and, thus, increase the rate of return or the ROI
(return on the investment that a company puts behind every Customer).
According to a survey by HubSpot, a significant fraction of developing companies opine that it is quite important to consider investing in customer service programs because many studies, including the one by Bain & Company highlighted the fact that a mere 5% increase in the rate of retention could increase profits, amounting to almost 25% to 95%. Thus, it is pretty evident that the Customer Lifetime Value is essential as it helps to amplify sales and optimize marketing expenses while also increasing retention of customers and encouraging brand loyalty, all of which are the
fundamentals of strengthening one’s business.

After understanding the benefits of a customer’s lifetime value, the question is how one should implement it. Machine learning, which is a combination of the study of algorithms and statistics, provides a solution to this. It is based on pre-existing data patterns in large samples. It doesn’t require additional commands and instructions to perform any specific operation or to
complete any task. It analyses the data, learns and makes decisions without much human intervention needed in the process. It reduces the chances of human error by replacing the process of manually entering data while dealing with a large set of numbers. ML also helps to analyze a customer’s purchase history and compare it with the collected data trends, thus providing recommendations for similar products. This, along with the Customer LTV and historical trends, further helps improve customer service, increasing customer loyalty and ensuring their satisfaction.

In digital marketing and e-com applications, customer lifetime value is one of the most important parameters to measure any business’s gross margin and success rate. It also helps strike a balance between acquiring new customers and retaining them and provides accurate information about implementing strategies. An increase in the average lifetime value of a customer implies that those strategies are working, ensuring higher sales and the chances that a customer would return. Machine learning algorithms help extract data-driven insights, thus decoding helpful information like the Customer’s sentiments, which allows us to predict the Customer’s behavior in the future. Netflix is one such example of companies that puts these technologies to use and save Billions per year on customer retention. Machine learning is an aid that helps to simplify this task by automating and optimizing the process while also identifying the hurdles in the path and providing solutions to tackle them, based on extensive data analysis, the connections between the sets of data and
the ongoing trends.

While implementing a customer’s lifetime value, they must first collect relevant information from the customers. This data should be correct and centralized, and then they should use it to build a model based on Machine learning for predicting the lifetime value. The validation and fine-tuning of the
A machine learning model is also required. The algorithms are needed for marketing so that the data is processed and the trends are identified quickly.
The Machine Learning algorithm categorizes customers and helps determine the marketing targets on whom the strategies are focused. Revenues increase, and the business earns profits. Therefore, ML has not only simplified the implementation of this concept but also has a lot of
importance in this domain, owing to the increasing competition between e-commerce websites and the need to hold onto customers.

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