7. Tracking MRR Changes

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It’s often useful to create a report that viewers, such as managers or clients, can easily interact with to generate insights of interest to them. We’ll create a waterfall chart for this purpose in this lesson.


Report Layout

  • One of the benefits of self-service BI tools is that we as data analysts don’t have to create every single chart and graph that might possibly be of interest to report viewers
  • Instead, we can give the users and viewers of the report the ability to find relevant insights for themselves
  • Providing a single chart with many slicers makes it easy for a user without technical skills to obtain a variety of insights from just one chart

Waterfall Chart

  • The waterfall chart lets us easily see the positive and negative contributions of each of the components of MRR growth
  • Using the slicers, we can easily compare these components across different categories
  • We find that Great Britain has a particularly high churn rate compared to the other regions


In the last few lessons, we've gained several different perspectives on the changes in MRR that have occurred in 2016 and 2017, however, we've assumed an integrative process of creating charts and generating insights.

Sometimes, these two processes will be separate.

In this lesson, we'll create a report that can be used to interactively track MRR changes. We'll start by creating a waterfall chart and placing it on the right half of the canvas.

We'll add MRR category to the category well, and MRR changes to the Y-axis well.

The chart should be sorted by MRR changes, but if it isn't, simply select the three dots at the top right corner, and change the sort order.

This chart illustrates the extent to which new customers are responsible for the largest share of MRR changes we've seen in this dataset.

We need to be careful about how we interpret this chart. Customers are classified as new only in the first month where they make payments, therefore, the new part represents the additional value added to MRR from customers who signed up at some point in the two years of our transaction data.

Similarly, expansion, contraction, and churn represent the changes in that company's total MRR figures caused by expansion and contraction over the two years.

As mentioned previously, analyzing churn, expansion, and contraction is easier when viewed as percentage rates instead of absolute amounts.

As a result, we'll add the MRR change rate and the annual MRR change rate to the tool tips well. Now we can see these percentages when we hover the mouse over one of the bars. For example, if we hover the mouse over the churn bar, we can see the annual rate of churn is about 5.5%.

We want this chart to deliver insights across various areas of the business. To achieve this, we'll add several slicers on the left side of the canvas. First, we'll add a date slicer.

We'll add the slicer, place it in the top left corner, and then add the transaction date field from the transactions table.

We'll go to the formatting section of the visualizations pane, select header, and increase the font size to 14.

We'll then select date inputs and increase the font size to 12.

We'll now add a second slicer below the date slicer, and drag in the location field from the customers table.

Again, we'll go to the formatting section of the visualizations pane, select header, and increase the text size to 14.

We'll then select items, and again set the text size to 14.

We'll use this formatting for the final two slicers, so we'll copy it, and paste twice.

We'll position these new slicers below the existing two.

We'll select the third slicer, replace the location field with MRR category from the transactions table, and ensure that all values are visible.

We'll then move on to the final slicer, replace the location field with the plan name field from the plans table, and again, expand it, to ensure that all values are visible.

These four slicers can be used to filter the waterfall chart according to the needs of different users. We won't analyze all the combinations of slicers values in this lesson, but we'll take a look at the three different locations in the dataset.

First, let's filter the MRR category so we don't see the column for new customers.

This makes it easier to compare the sizes of the remaining bars. We're now ready to look at the locations. We'll start by selecting GB to see expansion, contraction, and churn for Great Britain only. If we mouse over the churn column, we can see the annual churn rate is over 7%.

This is quite high, and significantly above the 5.5% figure we saw for the dataset as a whole.

If we select IE, and mouse over the churn column, we can see the churn rate is only 4% in Ireland.

If we select the US, we can see that their churn rate is just over 4 and a half percent.

Clearly, the churn rate in Great Britain is significantly out of line with the rest of the business and will require further investigation.

As we can see, a report like this can quickly deliver a range of insights.

While we've only looked at regional churn rates, other individuals could use other slicers to obtain similar insights for different plans or different time periods.

Put simply, a well-designed report can not only deliver useful insights itself, but also enable other people to find the information and insights that are relevant to them.

This concludes our look at trends and monthly recurring revenue for Interslice.

In the next few lessons, we'll look at the company's customers in greater detail.

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