1. Zooming out
Let's get back to it! This time you'll be analyzing the demographics of Databel.
2. Data analysis flow
If we look back at the 5 data analysis steps from chapter 1, it's easy to see we are only at step 2.
3. Data analysis flow
We did create some initial visualizations already, but we'll continue to explore different visuals throughout the rest of the case study.
4. Insights uncovered so far
We already found some key insights. We know that over 1,750 customers have churned giving an average churn rate of around 27%.
The most common reason that we have found for customers churning related to our competitors. More specifically that our competitors are offering better devices and offers.
This could raise questions such as "Is Databel competitive enough?". It's a bit too early to draw any general conclusions, especially as we don't have a clear explanation as to why churn rate is relatively high at 27%. There are many more columns we are yet to analyze.
5. There are many things we don't know yet
We started creating calculated fields from the get-go to measure the churn rate. This was a great start, but now that we're a bit further in the analysis we should make sure we have a holistic analysis plan. Looking at the metadata sheet it's easy to realize we only used a handful out of the columns in the database so far. It's a good practice to have a structured approach to our analysis.
6. The metadata sheet is your friend
The metadata in the sheet is grouped in different categories, so let's follow that approach when analyzing the data. We can create different sheets in Excel to analyze these different topics. Not every analysis will reveal insights, but nonetheless, it's important to do your exploratory analysis thoroughly.
7. Let's practice!
Now it's your turn! Are you ready to analyze the demographics of Databel? Enjoy!