Tag: analytics

App referral traffic to your web site and how to track it

Measuring referrals from mobile and tablet apps to web sites is extremely difficult – actually, its practically impossible. Over the past few years you will have seen traffic from direct/typed/bookmarked sources increase steadily as app usage has increased. Unfortunately this is not because your web site has become a destination for your chosen content, but instead its because your analytics platforms are unable to attribute traffic to apps.

I am specifically finding traffic from social media apps, so Facebook and Twitter, the hardest to track down. There are techniques that I discuss below that can help you to track content that you post yourself, but unfortunately this doesn’t help with organic sharing.

While Facebook Insights for domains does give you some level of overall referrer information, it does not breakdown the traffic between desktop and mobile.

This post explains why your analytics package is currently unable to track this traffic and tries to find some solutions to help you to make sense of it all.

So how do analytics platforms track referrals?

Most analytics packages use header information contained in your user’s web browser to determine which site the user had been to previously to visiting your web site. This header information only appears if a user clicks on a link to your site. For example, if I am on web site A, click on a link to web site B, then the header information would show that the referrer was web site A.

Just so we are clear, if I am on web site C, then type web site D’s domain name into my web browser, then there would be no referrer and the traffic source would be Direct.

No referrer in the header

This latter example explains why measuring app traffic is extremely difficult; an app is not a web site, it is not viewed in a web browser and they do not contain header information when linking out to a web site.

There are two different techniques that apps can use to open up web content:

  1. Open the content in a native mobile or tablet browser – such as Safari, Chrome, Android Browser, Opera etc
  2. Open the content in an in-app version of the native browser

In both cases, because the app is opening up a new instance of a web browser, whether its the native browser app or the in-app version of the browser app, there is no referrer in the headers. So when your analytics package is seeing the user visiting your web site, it is seeing that there is no referral and  it will deem the traffic to have no source and therefore assign that referral as direct.

So how can we track app referral traffic?

There are a couple of things that we can do as content creators to get around this:

Google’s UTM Tracking: https://support.google.com/analytics/answer/1033867?hl=en

If you use Google Analytics, you can use the built in UTM tracker to track links that you post to other web sites or platforms. Quite a few URL shortening services and sharing plaforms such as Buffer, Bit.ly and Owl.ly allow you to add these dynamically so you don’t need to keep adding the tracking manually.

When a user visits your site using a UTM tracked link, this will override what is contained in the web browser’s header and attribute the source of the traffic to relevant keyword that you add to the tracking. You need to be very careful here as I have seen instances where these have been setup incorrectly and links posted to Facebook with Twitter as the source have been shared which makes the data inconsistent and unreliable.

One thing to note on this, is to only use this method for external, inbound site links. You should not use these links on internal links as they automatically start a new site visit – meaning that you will see a spike in visits whenever someone clicks on your tracked links.

Using a similar service for other analytics platforms:

Many other analytics platforms have a similar method for campaign tracking. While these have been primarily used for more traditional marketing campaign tracking, there is no reason why you cannot do use this same method for the sharing of content.

Limitations

This would only work for content that you share yourself. There is no way to enforce this for any organic shares – so when a user copies and pastes a url from your web sites into a social status update for example.

Also, as you cannot post content to a specific device or platform, you cannot differentiate easily between a social media’s desktop, mobile web or mobile app experience. So while you may get closer to tracking Facebook traffic, you will still not know the make up of desktop, mobile web or mobile app referrals.

 

What is Cohort Analytics?

Cohort Analytics in this context is the measurement of how often a user returns to a website over a given period of time.  By understanding how well you retain your users, the better you will understand how best to monetise them – which is what we are all chasing in digital publishing.

This is not the same as the standard vanity stats that you may find in Google Analytics or Adobe’s SiteCatalyst for return visits or visitor retention, this will give you a more detailed understanding.

Also, if done correctly, you can use cohort analysis to measure not only general users to your website but also registered users, logged in users, users that purchase or convert to something.

Cohort analytics is quite a new concept for digital publishing.  In the past, CPMs have been high enough for publishers to only have to worry about unique users, visits and pages. But now, with the advent of Google Adsense and Facebook Ads, advertisers can now target audiences, so now publishers need to focus on other ways to measure and monetise audiences.

How to measure retention

Cohort Analytics is not something that is available out of the box with most standard web analytics tools.  Unfortunately it takes a bit of hacking to get it to work with Google Analytics – even then, it is quite limited as you can only measure over five units of time.  This will all become apparent shortly.

In addition, this explanation will only give you a basic overview for general tracking.

Google Analytics allows you to configure custom variables – of which there are five – and these segments can be persistent over a number of visits.

See Google’s documentation on custom variables here.

Custom variables should be set per time period that you would wish to track.  In this instance it would track user retention over a rolling 5 month period.

Here is some example code for month one using the first of five custom variables:

*** CODE ***

pageTracker._setCustomVar(
      1,                   // This custom var is set to slot #1 for the first month. 
      "Month",           // The top-level name for your online content categories.  Required parameter.
      "January 2011",      // Sets the value of "January 2011" to "Month" for this particular aricle.  Required parameter.
      1                    // Sets the scope to visitor level.   
 );
 pageTracker._trackPageview();

*** END CODE ***

Once this code is in your site, it will need to change each month. The two paramters that will need to change are the first and third variables where each will increment when month 2 begins.

*** CODE ***

pageTracker._setCustomVar(
      2,                   // This custom var is set to slot #2 for the second month. 
      "Month",           // The top-level name for your online content categories.  Required parameter.
      "February 2011",      // Sets the value of "February 2011" to "Month" for this particular month.  Required parameter.
      1                    // Sets the scope to visitor level.   
 );
pageTracker._trackPageview();

*** END CODE ***

Once this has been implemented correctly and has gathered the relevant correct data, you will see some hopefully nice results. In addition, if you are clever with your naming and strategy you will be able to measure much more than this.

The results

Cohort Analysis

Hopefully you will see something like the image above after a period of time.  What the above table shows is how many of the users return in the following months from their first visit.

The reason for the Month 1 statistics all being 100% is that all users in Month 1 are new. In Month 2 it shows how many users from Month 1 returned in Month 2. Month 3 highlights how many users returned to the site in Month 3.

By fully understanding how long your users keep coming back to your site, you can really start to focus on some new metrics.

You will be able to work out the Lifetime Value (LTV) of your users which would help you to work out how much you may want to spend on marketing. By understanding this, you can ensure that your marketing stays profitable.

You can also start to focus your development attention on lengthening the lifetime value of your users.