Advertising profitability and lifetime customer value analysis using Python is a powerful approach for businesses to measure the effectiveness of their advertising campaigns and understand the long-term value of their customers.
By leveraging Python’s data manipulation and analysis libraries such as pandas, numpy, and matplotlib, companies can easily calculate important metrics like return on ad spend (ROAS), return of investment (ROI), customer acquisition cost (CAC), and customer lifetime value (CLTV).
This allows marketers to identify which advertising channels and strategies are driving the highest ROI, enabling them to make data-driven decisions when allocating their advertising budgets. Additionally, with Python’s ability to analyze historical customer transaction data, businesses can estimate the CLTV of each customer segment and optimize their marketing efforts accordingly.
By using Python and Pandas, companies can automate the process of analyzing customer acquisition cost (CAC) and customer lifetime value (CLTV), making it more efficient and accurate. In this post I will show you an example code to calculate these indicators and visualize them using the Python programming language and its libraries Pandas and Plotly.