Analysis of Lifetime Value Using SQL and Python: Part 2

In this segment, Favio will continue the analysis of the different variables that lead to the differences in LTVs among the customers. You have already seen that customers leaving the company who were under the 80th percentile showed very different behavior from those in the top 20 percentile.
 
Now, the next video will help you understand whether the tenure of a customer has any influence on the LTV. Let us go ahead and watch the video.

So, in the video, we were able to conclude that: 

• The average LTV of 80% of those customers who unsubscribed is $711, and their tenure is nearly 10 months. 
• On the other hand, the average LTV of the top 20% of those customers who unsubscribed is $4811, and their tenure is nearly 50 months. 

Thus, we can conclude that the customers in the top 20% have a much higher tenure and that is the reason for their high lifetime value (LTV). Once again, this suggests that we should focus on serving those 20% customers, who have high LTVs.

So, now that we are able to make a clear distinction between the high- and low-LTV groups, the next objective is to check which services affect the customers’ LTV.

So the question that we are going to answer next is this: 
“What type of services had the customers subscribed to when they were still in subscription?”

We will see the answer to this question in the next video.

So, in the video, Favio drew these conclusions:

• Of the 20% (high-LTV) customers, only 2% did not use the phone service. 
• On the other hand, for the customers in the 80% (low-LTV) group, 11% did not use the phone service.
• This means that not having a phone can determine whether a customer is more likely to belong in the high-LTV or low-LTV group.

Moving on, in the next video, Favio will analyze whether customers were using Multiple Lines. If a customer is using multiple lines, then it means they can engage in two or more calls simultaneously and have more than one phone number.

So, in the forthcoming video, we will see if having multiple lines can affect a customer’s LTV. Let us go ahead and watch the video.

So, in the video, you saw the analysis for multiple lines.

In order to start with this analysis, Favio first added a filter to check whether a customer had the phone service; then he checked for the usage of the multiple lines service in the high- and low-LTV groups.

After performing the analysis, Favio drew these conclusions:

• Among the customers in the high-LTV group (20%) who used the phone service, 84% used multiple lines.
• On the contrary, of the customers in the low-LTV group, only 59% used multiple lines. 

Thus, the data suggests that having multiple lines can bring you closer to the high-LTV group.

In the next segment, Favio will analyze the rest of the services and the contract.