Measuring Customers lifetime value in Google Analytics for Mobile App and Website users

In Google Analytics, there is a report (still in beta), called the ‘Lifetime Value‘ report  through which you can measure the life time value (also known as LTV) for website users / mobile app users.

Lifetime value is the projected revenue (sales), a person may generate during his/her lifetime, as a customer, for your business.

Through the ‘lifetime value’ report you can understand, how valuable website / mobile app users are to your business.

You can also compare the users acquired through different marketing channels (organic search, paid search etc) to determine the channels which brings high value users to your website.

In Google Analytics, the ‘lifetime value’ report is available in both ‘Website View‘ and ‘Mobile App view‘.

Follow the steps below to access the ‘lifetime value’ report in the ‘Website view’:

Step-1: Login to your Google Analytics account.

Step-2: Navigate to the view which has been collecting ecommerce data.

Step-3: Navigate to Reporting tab > Audience > Lifetime value:

Follow the steps below to access the ‘lifetime value’ report in a mobile app view:

Step-1: Login to your Google Analytics account.

Step-2: Navigate to the mobile app view which has been collecting ecommerce data:

mobile appview

Note: You can use the mobile app view only when you have set up mobile app tracking

Step-3: Navigate to Reporting tab > Audience > Lifetime value:

lifetime value report

Going forward whatever I explained about the ‘lifetime value’ report is equally applicable to both website users and mobile app users.

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Components of Lifetime Value report

Following are the various components of this report:

#1 Acquisition Date Range

#2 LTV metrics

#3 Compare metric

#4 Graph

#5 Data table

Pay special attention to the various highlighted components of a lifetime value report:

ltv components

ltv components2

Acquisition date range

Acquisition date range is the duration during which website / mobile app users were acquired:acqusition data range

Any user which is acquired during this date range is included in the ‘Lifetime value’ report.

If you want to analyse the users acquired during the most recent single day campaign then set the acquisition date range to ‘Yesterday’:

yesterday

If you want to analyse the users acquired during the most recent 1 week long campaign then set the acquisition date range to ‘Last Week’:

last week

Similarly, if you want to analyse the users acquired during the most recent 1 month long campaign then set the acquisition date range to ‘Last 30 days

You can also set custom acquisition date range

For example, if you want to analyse the users acquired during a campaign which ran from say March 1, 2015 to Feb 1, 2016 then you can do that by selecting ‘custom‘ from the date range drop down menu, as shown below:

custom data range

Lifetime Time Value (LTV) metrics

Following 7 LTV metrics are available in the ‘Lifetime value’ report:

  1. Appviews per user (LTV) – applicable only for mobile app users
  2. Pageviews per user (LTV) – applicable only for website users
  3. Goal completions per user (LTV)
  4. Revenue per user (LTV)
  5. Session duration per user (LTV)
  6. Sessions per user (LTV)
  7. Transactions per user (LTV)

Note: Here, user refers to your website / mobile app user.

Appviews per user (LTV)

It is the cumulative average appviews per user.

This metric is calculated as:

Cumulative Average Appviews per user (LTV) = Cumulative Appviews (LTV) / Users

Here,

A cumulative metric is the one, whose value increases by successive additions over time.

Users => total number of app users acquired during the selected acquisition date range.

For example:appviews per user

Here,

Appviews (LTV) = 12,613

Users = 409

Cumulative Average Appviews Per User (LTV) = 12,613 / 409 = 30.84

The graph below shows the cumulative average appviews in the first 30 days after acquisition:appviews per user2

Here,

Day 0 shows the cumulative average app views of users on the day of acquisition.

Day 1 shows the cumulative average app views of users in the first day after acquisition.

Similarly,

Day 29 shows the cumulative average app views of users in the 29th day after acquisition.

Since all of these 30 data points represent cumulative average metric, the last data point (29th data point) in the graph above represents the cumulative average appviews, which is also reported in the data table of ‘Lifetime value’ report:

appviews per user3

If you want to see the x-axis on the graph to show one data point for each week, instead of one data point for each day, then select the ‘week’ tab:week tab

So if you select acquisition date range of last 30 days then you will see five 5 data points on the graph, where each data point represents one week:week tab2

Here,

Week 0 shows the cumulative average app views of users in the week when they were first acquired.

Week 1 shows the cumulative average app views of users in the first week after acquisition.

Similarly,

Week 4 shows the cumulative average app views of users in the fourth week after acquisition.

If you want to see the x-axis on the graph to show one data point for each month, instead of one data point for each day or week, then select the ‘month’ tab:month tab

So if you select acquisition date range of the last 30 days then you will see only one data point on the graph, which represents a month:month tab2

Note: Google Analytics calculates, cumulative LTV metrics for up to first 90 days after acquisition.

Pageviews Per user (LTV)

It is the cumulative average pageviews per user.

This metric is calculated as:

Cumulative Average Pageviews per user (LTV) = Cumulative Pageviews (LTV) / Users

Here,

Users => total number of non- mobile app users acquired during the selected acquisition date range.

The graph below shows the cumulative average pageviews in the first 30 days after acquisition:

Here,

Day 0 shows the cumulative average pageviews of users on the day of acquisition.

Day 1 shows the cumulative average pageviews of users in the first day after acquisition.

Similarly,

Day 30 shows the cumulative average pageviews of users in the 30th day after acquisition.

Since all of these 31 data points represent cumulative average metric, the last data point (31th data point) in the graph above represents the cumulative average pageviews, which is also reported in the data table of ‘Lifetime value’ report:

If you want to see the x-axis on the graph to show one data point for each week, instead of one data point for each day, then select the ‘week’ tab:

Similarly, if you want to see the x-axis on the graph to show one data point for each month, instead of one data point for each day or week, then select the ‘month’ tab.

Goal Completions Per user (LTV)

It is the cumulative average goal completions per user.

This metric is calculated as:

Cumulative Average Goal Completions per user (LTV) = Cumulative Goal Completions (LTV) / Users

For example:goal completions per user

Here,

Cumulative Goal Completions per user (LTV) = 58,030 / 15,605 = 3.72

Revenue Per User (LTV)

It is the cumulative average revenue per user.

This metric is calculated as:

Cumlative Average Revenue per user (LTV) = Cumulative Revenue (LTV) / Users

Session Duration Per User (LTV)

It is the cumulative average session duration (in seconds) per user.

This metric is calculated as:

Cumulative Average Session Duration per user (LTV) = Cumulative session duration (LTV) / Users

For example:session duration per user

Here,

Cumulative session duration (LTV) = 1611:31:01

Here, 1611 is the number of hours,

31 is the number of minutes and

1 is the number of seconds

Let us convert hours and minutes into seconds.

1611 hours * 3600 = 5799600 seconds

31 minutes * 60 = 1860 seconds

So, Total number of seconds = 5799600 seconds + 1860 seconds + 1 second = 5801461 seconds

Now,

Cumulative session duration (LTV) = 1611:31:01 = 5801461 seconds

So,

Session Duration Per User (LTV) = 5801461 seconds / 120,480 users = 48.15 seconds per user = 00:00:48

Sessions Per user (LTV)

It is the cumulative average sessions per user. This metric is calculated as:

Cumulative Average Sessions per user (LTV) = Cumulative Sessions (LTV) / Users

Transactions Per user (LTV)

It is the cumulative average transactions per user.

This metric is calculated as:

Cumulative Average Transactions per user (LTV) = Cumulative Transactions (LTV) / Users

Comparing LTV metrics

To compare LTV metrics with each other, click on the following plus ‘+’ button next to the LTV metric and then select the metric you want to compare from the drop down menu:plus button

compare metric

compare metric2

Here we are comparing ‘Transactions Per User (LTV) with ‘Tenure’ metric.

‘Tenure’ is the number of users who have been with your business for a particular time duration (days, weeks or months).

Dimensions in Lifetime Value report

Following four dimensions are available in the lifetime value report:

  1. Acquisition Channel
  2. Acquisition Source
  3. Acquisition Medium
  4. Acquisition Campaign

dimensions

These dimensions help you in analyzing the various LTV metrics in different context.

Note: You can’t apply secondary or custom dimension to the ‘lifetime value’ report.

 

What is the difference between Digital Analytics and Google Analytics?


99.99% of course creators themselves don’t know the difference between Digital analytics, Google Analytics (GA) and Google Tag Manager (GTM).

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Digital Analytics is the core skill. Google Analytics is just a tool used to implement ‘Digital Analytics’.

You can also implement ‘Digital analytics’ via other tools like ‘adobe analytics’, ‘kissmetrics’ etc.

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I truly hope you find it helpful.  

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Himanshu Sharma

Digital Marketing Consultant and Founder of Optimizesmart.com

Himanshu helps business owners and marketing professionals in generating more sales and ROI by fixing their website tracking issues, helping them understand their true customers purchase journey and helping them determine the most effective marketing channels for investment.

He has over 12 years experience in digital analytics and digital marketing.

He was nominated for the Digital Analytics Association's Awards for Excellence.

The Digital Analytics Association is a world renowned not-for-profit association which helps organisations overcome the challenges of data acquisition and application.

He is the author of four best-selling books on analytics and conversion optimization:

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