Computing and visualizing KPIs from data with Python

With the foundation of various marketing KPIs that we have discussed so far, let us dive into a potential real-world use case of some of these KPIs, how to compute and visualize them, and how to interpret them for further data-driven decision-making. We will be using an insurance product marketing dataset for our example.

From a financial point of view, the success of marketing is to bring in more revenue with less spending. The most straightforward approach in tracking and measuring this is to look at conversion rate, CLV, and CPA. The conversion rate tells us the percentage of leads converted into paying customers, CLV tells us the estimated revenue being generated per acquired customer, and CPA tells us how much it costs to acquire a paying customer. By combining these three KPIs, we can understand the ROI and contribution to the net income.

Using the above-mentioned example dataset, let’s discuss how these KPIs...