Understanding the Customer Services in Tableau Story - Part 1

In this segment, we will start using Tableau and perform an analysis to understand the impact of the different services in terms of churn and the lifetime values (LTVs) of the customers.

So, as a first step, Favio will be creating a simple pie chart to show the churn ratio in our data. Let us go ahead and watch the forthcoming video.

So, in the video, Favio created the visualization, which you can see below.

Now that we have built our first visualization, we can now start with creating individual plots for each of the services that we had analyzed in the previous session. If you recall, the key services that came out from our analysis were phone service, multiple lines, internet services, and the streaming movies and streaming TV services.

So, in the next video, Favio will create a visualization that will tell you about the impact of internet services not just on churn but also on the LTVs of the customers. Let us go ahead and watch the video.

So, in the video, Favio created this visualization for the phone service.

So, in the video, Favio created a simple pie chart to present the churn ratio in our data. The visualization is shown below for your reference.

Here are the conclusions drawn from this plot:

1. People on 2-year contracts and in the fiber optic domain pay the highest total charges. At the same time, people in the fiber optic domain are also paying heavily as monthly charges; this could be a reason why there is more money in total charges.

In the next video, Favio will perform a similar analysis for the phone service and multiple lines. Let us go ahead and watch the video.

Here are the conclusions drawn from this plot:

• We can see a difference between people who churned and did not churn. For example, people who churned and had the phone service were paying more money in total as compared with those who did not pay for the phone service. 
• People who are paying more money on a monthly basis and using the phone service are more likely to leave the company.

In the video, you also saw a similar analysis performed on the multiple lines. The visualization for this is shown here for your reference.

We were able to draw these conclusions based on the plot on multiple lines with churn and LTVs:

• Churn: Customers who had left the company were paying a lot of money for the phone service and multiple lines as compared with those who were still with the company.
• Contract: Customers who have a 2-year contract have much higher LTVs than those on a month-to-month contract. 
• Lifetime value: People with a 1- or 2-year contract contribute more to the LTV. Hence, we should sell such contracts in order to get customers closer to higher LTVs. Nevertheless, this might risk people leaving the company, as we saw that they would be more likely to churn.

Moving on, in the next segment, Favio will perform an analysis of the streaming TV and streaming movies services.