Customer Lifetime Value Prediction

To gauge the significance of customers and their longevity with a company, businesses utilise a metric known as customer lifetime value, which predicts the revenue a customer is expected to generate for the company throughout their relationship with the company. Customer value, or CLTV, signifies the total revenue a customer will generate for a company throughout all their transactions before potentially switching to competitors. In its simplest form, the metric can be calculated using the formula, which can be enhanced to incorporate various nuances of a business, such as associated costs.

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CLTV = Average Order Value * Average Customer Frequency * Average Customer Lifespan

When deployed at scale, this metric can assist businesses in comprehending the viability of their business model. A subscription-based company with a low CLTV could potentially incur losses, especially if the cost of retaining a customer is substantial. Moreover, CLTV aids in identifying customers who may be on the verge of churning out. It's widely acknowledged that the cost of acquiring new customers is typically higher than retaining existing ones. Therefore, it becomes imperative to prevent loyal customers from churning out in order to maintain profitability and sustainability. 

 The retail and e-commerce market is characterized by stiff competition, with every business aiming to retain its customers and prevent them from switching to competitors. To achieve this goal, businesses often offer targeted discounts and coupons to customers, encouraging them to return to the platform and conduct more business. It's important to note that retaining every customer may not be necessary in practice. By nurturing loyal customers, they enhance their business through repeat purchases and increased revenue. 

Companies use CLTV to identify several aspects of their business

• Evaluate the profitability of investing in new customer acquisition. 

• Understand who the most valuable customers are and how to attract similar profiles through targeted marketing.

• Allocate appropriate resources to retain customers likely to churn, ensuring a cost-effective strategy for customer retention.

• Accurately determine who the key customers are, allowing for personalized engagement and service.

These insights help companies make informed decisions regarding customer acquisition, retention and resource allocation.

For a company, CLTV is an evolving metric that changes over time, reflecting shifts in customer behaviour. Although CLTV can be found using simpler averaging methods, they only give an overall estimate of the metric. Machine learning techniques can be employed to identify CLTV at a customer level by analyzing their behaviour in the initial months, enabling predictions regarding their long-term behaviour. In this project, we are planning to utilize machine learning techniques like clustering to identify customers and predict their CLTV value. 

• Predict the lifetime value of new customers using machine learning

• Predict the customers who are about to churn

• Average CLTV for the customer base

• Variation of CLTV across different customer segments or demographics

• CLTV changes over time, and factors influencing these changes

• CLTV trend for newly acquired customers compared to long-term customers

• Cost of acquiring customers compared to their CLTV, and how does it impact overall profitability

• Customer retention efforts in increasing CLTV

• The churn rate of customers

• Most valuable customers for the business

• Distribution of CLTV among the customer base

• Specific products or services that tend to drive higher CLTV

• Product pricing strategies impacting CLTV