Zooming out
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 optimize some of our visualizations already, so we don't lose too much time on step 3 - finding fitting visualizations to convey a message. Given Power BI is a data visualization tool, it actually makes sense to combine steps 2 and 3.4. Insights discovered so far
We already found some key insights. We know the average churn rate is around 27%, and that the main reason why customers churn is related to competitors. This could raise questions such as "Is Databel competitive enough?". We also discovered that the churn rate in California is abnormally high at 63.24%, but it does sound a bit too early to make any general conclusions. We don't have a clear explanation yet for the relatively high churn rate of 27%, and there are still so many columns we need 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 deeper 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 29 columns in the database so far. It's a good practice to have a structured approach to your 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 pages in Power BI 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!Create Your Free Account
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