11. Calculating Average Revenue per User

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Average Revenue per User (ARPU) measures how much a customer pays per license. Variations in ARPU can reflect discounts that are offered to larger customers on more expensive plans, which we’ll analyze in this lesson.


Average Revenue per User (ARPU)

  • Interslice charges customers according to the number of licenses they purchase
  • However, business customers generally have a large number of subscribers, and these large companies are capable of negotiating discounted prices
  • Analyzing Average Revenue Per User allows us to analyze these discounts

Analyzing the ARPU

  • Individual subscribers pay the fixed list price for each plan and do not have any power to negotiate discounts
  • Business customers pay a fixed price per license when they subscribe to one of the two cheaper plans. No discounts are offered on these plans
  • When we analyze the expensive plans, it appears the price per user is variable. In particular, we can see that larger companies are able to negotiate larger discounts.
  • This policy encourages larger companies to sign up for more expensive plans


Up to this point, our analysis of revenue has generally focused on the total MRR figure for each customer. However, most of the company's customers are businesses with large numbers of users.

As such, it makes sense to look at the average revenue per user or ARPU.

Our goal in this lesson to analyze the average revenue per user for different plans and company sizes. Our data model has information on the list price charged per user.

As we look over the Plans table, we can see that each plan has two prices, one for individuals, and one for companies.

Notice the Enterprise Plan does not have an individual price as this plan is only available for corporate clients.

These prices reflect the monthly cost of the plan for a single user. However, these are not necessarily the prices that customers will actually pay.

We expect that some customers such as large companies would be able to negotiate a discounted rate.

We'll test this theory and to analyze the actual average revenue per user in the dataset.

To start, we'll switch to the Transactions table and create a column representing ARPU.

We'll navigate to the Home tab, select New Column, and name it MRR per User.

We'll then enter a formula that divides the MRR column by the Number of Licenses column and press Enter.

Let's go to Report View and create a chart analyzing this new column.

We'll add a clustered column chart to the left half of the screen leaving room at the top for slicers.

We'll then navigate to the Plans table, drag the Plan Name field to the Access Well, and both the Business Price and Individual Price fields to the Values Well.

We'll change the aggregations from count to average to show us a list of prices for each plan.

We'll then navigate to the Transactions table, add MRR Per User to the Values Well and again change the aggregation to Average.

This shows us the actual prices paid.

As you can see, our chart includes a blank plan.

This blank plan covers transactions where the customer is churning.

These transactions are not of interest to us as they have no revenue.

We'll filter them out with a page level filter.

To do this, we'll scroll down to the Filter area of the Visualizations pane and drag Plan Name to the Page Level Filters Well.

We'll then select All and unselect Blank.

As we can see, our column chart updates to remove the blank plan.

Let's analyze individuals and businesses by adding a slicer and positioning it above the chart.

We'll then navigate to the Customers table and add Type of Customer to the slicer.

If we select Consumer, we can see that the MRR per user is exactly equal to the individual price for each plan.

This tells us that the individual consumers always pay the list price for any plan.

We won't analyze individuals any further, so we'll navigate to the Fields area and remove Average of Individual Price from the Values Well.

We'll also change the slicer to Business to see the list prices and actual prices for businesses.

As we can see, the MRR per user is equal to the business price for both the Basic and Standard plans.

This indicates that businesses pay the list price for these cheaper plans.

However, MRR per user is lower than the list price for the Advanced and Enterprise plans.

This indicates that businesses can negotiate a discount on these more expensive plans.

Next, we'll try to establish what sort of discounts businesses are given on these expensive plans.

It's likely that largely companies will receive bigger discounts, so we'll create a line chart showing the MRR per user according to the number of users a company has.

We'll add a line chart to the canvas and place it on the right half of the screen.

We'll then navigate to the Transactions table, add Number of Licenses to the Access Well and MRR Per User to the Values Well.

We'll also change the aggregation of the MRR Per User field to Average.

Finally, we'll navigate to the Plans table and add Plan Name to the Legend Well.

This chart shows us the average revenue per user per each plan by the number of users.

The chart makes it clear that the Basic and Standard plans have a single price while the Advanced and Enterprise plans have more flexible and negotiable prices.

In order to analyze the plans individually, we'll add a new slicer above the line chart and add the Plan Name field.

We'll look at the Advanced Plan first.

Based on the chart, it seems that the smallest customers of the Advanced plan pay a fixed price per user but most companies negotiate some level of discount.

It looks like the level of discount offered increases with the number of users.

To test this theory, we'll go to the Analytics pane, and add a trend line.

This trend line suggests that the price paid per user is lower for larger companies indicating bigger discounts.

If we select the Enterprise plan from the slicer, we can see a similar pattern is evident with larger discounts offered to larger companies.

This information not only gives us an understanding of Inner Slicer's current sales strategy but could also inform future sales.

The trend lines could be used to create target prices for sales to future customers based on their size. In the next lesson, we'll take a more detailed look at how Inner Slice deals with large customers.

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