8. Comparing Customer Sign-Up Trends

 
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Overview

In the next few lessons, we’ll analyze the customers of Interslice. In this lesson, we’ll analyze past sign-up trends to see when new customers are most likely to sign-up.

Lesson Notes

Analyzing Customer Sign-Ups

  • The stacked area chart is used to track signups over time
  • It is clear that the company expanded into different locations in a planned manner
  • There may be something of a seasonal trend to signups; sign-ups appear to increase in the last three months of the year
  • There are also trends in signups by day of the week. Sign-ups are highest on Friday and Saturday and are very low on Sunday

Transcript

In the previous lessons, we focused on Interslice's monthly recurring revenue.

In the remainder of the course, we'll focus on the company's customers. In this lesson, we'll create some visuals to analyze trends in customer sign ups.

Interslice can learn from these trends to determine how to best attract new customers in the future. Let's start by tracking sign ups over time with a stacked area chart.

We'll select this chart, and place it on the left half of the canvas.

We'll then navigate to the Customers table, drag Sign Up Date to the access well, Customer ID to the values well, and Location to the legend well.

We'll then select Expand All twice to view data at a monthly level.

This chart shows us the number of customers signing up each month in each region. The area indicates a total share of new customers for each region.

This chart is useful for tracking the development of the company over time. We can see that the first customers signed up in Ireland at the start of 2013.

Customers from Britain followed later that year, and customers from the U.S. started signing up in 2014.

Clearly, the business grew and expanded in a carefully planned way.

We can also use this chart to gain some insight into Interslice's business cycle.

For example, if we look at the area representing the Irish customers, it looks like this area was fairly small in 2013 and 2014, became larger in 2015, and contracted slightly in 2016 and 2017, indicating a focus on other geographic locations.

We'll now create a couple of tables to help gather useful insights for increasing future sign ups.

First, we'll analyze sign ups by month.

We'll create a matrix, as this will allow us to drill down through the hierarchy of the sign up date.

We'll add Sign Up Date to the rose well, and Customer ID to the values well.

We'll then change the aggregation of Customer ID to Count.

We'll also go to the Formatting section of the Visualizations pane, select Grid, and increase the text size to 14.

We want to see if there are any seasonal trends to sign ups, so Interslice management can prioritize their resources at certain times of the year.

We'll select the two down arrows twice to move down the hierarchy and view sign ups by month independent of year.

Based on this table, it looks like the last three months of the year have more sign ups than others.

This would suggest that Interslice should put in an extra effort to attract new companies towards the end of the year. Let's also analyze sign ups by day of the week.

To do this, we need to create a new column to extract the day of the week from the Date column.

We'll go to Data View, select the Customers table, and create a new column called Sign Up Day.

We'll use the Format function to pull the weekday information from the date.

The first argument is the Sign Up Date column, and the format will be DDDD in quotes.

This gives us a column showing the day of the week for each sign up date.

Next, we need to create a column to sort the days so we can get them to appear in the correct order in our table. We'll create another column and call it Sign Up Day Number.

In this instance, we'll use the Weekday function on the Sign Up Date column.

This produces a column displaying the number of the weekday for each sign up date.

We now need to sort our Sign Up Day column by the weekday numbers we just created.

We'll select the Sign Up Day column, navigate to the Modeling tab, select Sort By Column, and then Sign Up Day Number.

We'll now go back to Report View, create a new table, and position it to the right of the matrix.

We'll navigate to the Customers table, and add both Sign Up Day and Customer ID.

We'll again change the Customer ID aggregation to Count, navigate to the Formatting section of the Visualizations pane, select Grid, and increase the text size to 14.

We can see that there's a large increase in sign ups on Fridays and Saturdays, while Sunday has low sign up numbers.

Interslice should devote more sales resources towards the end of the working week, as this seems to be when customers are most willing to sign up. Let's stop the lesson here. Over the next two lessons, we'll continue with our customer analysis, looking at how long customers stay with a business and when they're likely to turn.