This is how an AI Product Manager can Analyze CLV Using Python

Customer lifetime value analysis estimates the overall value that customers bring to a business throughout their relationship. This analysis guides businesses in making informed decisions regarding resource allocation and enhancing customer relationships. If you're interested in learning how to analyze customer lifetime value, this article is tailored for you. Here, I'll walk you through performing Customer Lifetime Value Analysis with Python.

What is Customer Lifetime Value Analysis (CLV Analysis)?

Customer lifetime value analysis estimates the aggregate value that customers contribute to a business throughout their relationship. It aids companies in deciding optimal investments in customer acquisition and retention, while also pinpointing key customers deserving of focused retention efforts.

Through customer lifetime value analysis, companies can pinpoint effective marketing channels and campaigns for acquiring high-value customers. Additionally, they can craft targeted retention strategies to foster ongoing engagement and loyalty among these customers.

To conduct the Customer Lifetime Value analysis, a dataset detailing customers' interactions with the business is essential. I am using sample data from statso.

In the section below, I will take you through the task of analyzing Customer Lifetime Value using Python.

I’ll start this task by importing the necessary Python libraries and the dataset.

Here, I will be visualizing the distribution of acquisition cost and revenue generated by the customer using histograms:

Email marketing yields significantly higher ROI compared to all other channels, whereas paid advertising shows the lowest ROI. Now, let's proceed to compute the customer lifetime value for each channel. Given the available data, we will apply the formula provided below to calculate CLTV:

CLTV = (revenue – cost) * conversion_rate / cost

So the customer lifetime value from Social Media and the referral channels is the highest.

Now let’s compare the CLTV distributions of the social media and referral channels:

There's a slight advantage in Customer Lifetime Value from the Social Media channel compared to the referral channel, although the difference is minimal.

Hence, this is how you can analyze and compare the customer lifetime value from various channels.

Conclusion

Customer lifetime value analysis estimates the comprehensive value that customers bring to a business throughout their relationship. This analysis assists companies in deciding optimal investments in customer acquisition and retention, and in identifying the most valuable customers to prioritize for retention efforts.

If you found this article informative on Customer Lifetime Value Analysis using Python, please feel free to ask any questions or share your thoughts.