Subscription Upgrade Analysis

Overview

KittyCarts is a fictional eCommerce company that sells monthly subscription boxes containing feline care products. They have two tiers of subscription offerings. They currently offer five similarly priced subscription plans in their first tier, each one based on a cat’s age (kitten, young adult, elderly, etc). Each of these plans has a similar number of subscribers.  

KittyCarts wants to see which Tier 1 subscription has been driving the most upgrades to Tier 2 subscriptions over the last 90 days.

In addition, KittyCarts is currently conducting an AB test on new Tier 1 subscribers. 50% of new Tier 1 subscribers are given free one-month trials.

Kitty Carts wants to understand how free trial participation is affecting the rate of upgrades. 

Questions:

  • Which subscription plan are customers upgrading from most frequently?
  • What is the impact of the AB test on subscription upgrades?

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Question 1: Which subscription plan are customers upgrading from most frequently?

 

Open the Segmentation Tool: Let’s click the Segmentation icon on the left side of the screen.

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Chart “Subscription Upgrade”: The data panel on the right hand side contains all events that are available for analysis. Let’s add the “Subscription Upgrade” event into the chart area.

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Modify Date Range + Time Interval: To perform analysis over a 90 day time frame:

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  • Let’s change the date range from “Last 7 days” to “Last 90 Days”.
  • Now, change the time interval from “Daily” to “Weekly” to view subscription upgrades week-over-week.

 

Breakout by “Previous Plan ID”: We can see that the total number of subscription upgrades has increased over the last 90 days. To dig deeper, let’s perform a property breakout to see which subscription plans are contributing most to this increase.

 Let’s switch to the Event Properties tab in the Data Panel, then drag the “Previous Plan ID” event property into the “Segment by” dropzone.

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View Results: In the legend below the chart, property values are ordered from most common to least common. However, it may be difficult to discern which plan contributed to the most Subscription Upgrade events using the line graph visualization.

To better visualize the results, let’s explore some other charting options:

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  • Change the Time Interval to Full Range: This will change the chart type from a line chart to a bar chart. Each bar represents the aggregate count of Subscription Upgrade events for a specific plan type, over the full range of 90 days.

 

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  • Change the Count from Non-Cumulative to Cumulative: Instead of individual counts for each week, the Cumulative view shows the week-over-week running total.

 

Observation 1

  • The most common previous subscription is Plan ID “45”, with a total of 95,128 upgrades during the last 90 days.
  • Plan ID “23” a very close second with 90,919 upgrades during this same period.

Plan ID 45 is a box meant for kittens, and Plan ID 23 is meant for adolescent cats. Based on these results, it makes sense to more heavily target owners with cats in these age categories.


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Question 2: What is the impact of the A/B test on subscription upgrades?

Now that we have discovered the most frequently upgraded plans, let’s explore some of the other factors driving upgrades. KittyCarts is currently conducting an A/B test to measure the impact of one-month free trials at signup on upgrades from Tier 1 to Tier 2.

To measure the impact of the A/B test, we will use the calculator tool to identify the percentage of users who upgraded and received a free trial.

 

Reset the Query Builder: In the query builder, let’s hit the reset button to clear the analysis.

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Create Calculation Components: To perform this calculation, we must first create its components. Since we are creating a percentage, we’ll need two query rows to form the numerator and denominator components of the calculation: 

  • Numerator: the number of users who had a free trial, and also upgraded their plan
  • Denominator: the total number of users who upgraded their plan.

 

Create the Numerator: Let’s begin by creating the numerator for our calculation.

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  • Let’s select the event “Subscription Upgrade”, and set it to “Users who performed” to give us the number of unique users who performed this action.
  • Next, let’s use a “Where” clause to filter for users who had a free trial.
  • The query row should read: “Users who performed Subscription Upgrade Where Free Trial? is equal to Yes”.  

We have now created a query row that represents the number of users who had a free trial and also upgraded their plan.

 

Create the Denominator: Let’s create the row that will become the denominator in our calculation. For this query row, we need to find the total number of unique users who performed subscription upgrades.

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  • Let’s drag the “Subscription Upgrade” event into the query builder
  • Next, change “Total count of” to “Users who performed”.

 

Modify Date Range + Time Interval: To perform analysis over a longer 90 day time frame:

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  • Let’s change the date range from “Last 7 days” to “Last 90 Days”.
  • Now, change the time interval from “Daily” to “Weekly” to view subscription upgrades week-over-week.

 

Create the Calculation: Having created the components that will form our calculation, we’re now ready to move into the calculator function. To create the calculation:

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  • Let’s open the calculator.
  • Select the two rows that we created, putting our group of free trial users who upgraded over the total number of users who upgraded.
  • Next, select the % function from the calculation options, so we can see our results as a percentage.
  • Execute the query.

 

Observation 2

About 70% of users who upgraded had also had a free trial. Considering that only 50% of new users received the free trial, this indicates that the free 1 month trial is an effective strategy to  drive upgrade conversions. KittyCarts should look into expanding this promotion.

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