Adobe Archives - Mike Dixon's Blog

Mobile Native Browsers vs In-App Browsers

I was surprised to find that Adobe Analytics (Adobe SiteCatalyst or Omniture, depending on how far back you go) doesn’t allow you to report on mobile browsers. Even though, I’ve been using Adobe SiteCatalyst for many years and I had always assumed that the ability was there. I am actually a little ashamed that I hadn’t noticed this before.

SiteCatalyst Device Type with Browser Breakdown

The main reason for me to look at this was that a colleague sent me a link about Embedded Mobile Browser Usage on http://www.lukew.com. While the blog post is more to do with how developers should be testing on in-app versions of mobile browsers as well as native browsers. This is mainly due to the in-app versions of Safari and the native Android browsers having less functionality and lower rendering powers. Luke explains that the in-app version of Safari for example does not include the Nero Javascript engine, certain Safari caches, or the “asynchronous” rendering mode. Meaning that web sites could appear to load more slowly, contain errors etc that would normally not be detected if testing on the native versions.

As Facebook and Twitter app usage continues to grow, its going to become more and more important to understand the differences between these different browser types and how users browse your sites when using these apps. In addition, when users click on your content from apps such as Facebook and Twitter, they appear as either Direct or Typed/Bookmarked depending on which analytics package you use and whether you are using any form of campaign tracking or not.

When I realised that Adobe Analytics does not track mobile device browsers, I turned to our implementation of Google Analytics. Fortunately, GA does track in-app browsers on iOS – so you can see data for Safari and Safari (in-app).

What I have found in my initial dive into this is that users browsing our sites using an in-app verison of the native browser consume less content (in terms of pages per visit), has a higher bounce rate and are on the site for about half the time than the native version.

In-app browsers vs native mobile browsers

What is going to be hard to work out is whether this is due to a behavioural or technical motivation.

If the site renders slowly, contains certain errors, or is not fully optimsied for the in-app experience, you could attribute some of the reduction of usage to this rationale.

In terms of behavioural, you need to consider what users are doing. If I am on my Facebook app on my phone, its probably while I am in the middle of doing something else. Potentially on the toilet, in a meeting (hopefully my boss is not reading this) or in transit therefore I have limited time to view.

Also, to move this point further, if I am in my Facebook app, I’m also interested in seeing what other content my friends have posted. So I might just read the article that a friend posted, then click back in the app so I go back to my feed and continue my Facebook journey.

One of the main things that I tell colleagues at my job is that Analytics is great at telling you what happened. Trying to find the reasons behind a person’s behaviour is not generally achievable. You can find commonalities between certain types of users, segment them and try to implement changes to convert users to behave in the same way. But in terms of understanding the drivers for users to act in a particlular way is not something that I believe Analytics will answer.

You need to be talking to your users and understanding why they are doing what they are doing – whether it be done through social media, email or surveys. You really need to have all the information to make decisions and I believe that Analytics is only one part of it in this case.

Actionable Social Analytics isn’t that hard!

I have seen many blog posts recently regarding the importance of social media integration and how easy it is. A few lines of code here for a Facebook Like button, a few pieces there for a @follow Twitter button there and so on. But what happens after that? Just leave it there and wait for the traffic?

I’m not seeing many resources out there for more advanced tracking of social media plug-ins and integration which is a real shame as there is so much you can track and measure to work out the effectiveness of your implementation and it will allow you to iteratively improve your integration and get more out of social networks in the long run.

Firstly, I do not see the point of just implementing social media plug-ins into your site because that’s what everyone else does. Facebook gets a lot of information sent back to their servers about your users when they view pages on your site that contain a like button. So any implementation needs to be of actual benefit to you as well as Facebook.

You need to have a strategy. What is is that you are trying to achieve? Do you want to drive awareness through word of mouth? Page impressions? Unique users? Sales? Do you want to be putting effort in to talking to your users directly through an official Facebook page or Twitter account or through installing Facebook comments on your article pages?

There are so many options and ways to integrate social media into your site to achieve so many different goals that by integrating them all without proper and effective tracking results in you not knowing what has caused any sort of uplift in your KPIs.

This blog post hopes to offer insight into a few techniques that can be applied to your social media integration and will be focussed towards the Use of Google Analytics – although similar techniques are available for the likes of Adobe’s SiteCatalyst but you will probably need to talk to your Account Manager about it and get some help implementing the concepts.

The main issue is that without more advanced tracking of your social media integration, you just see referrals from Facebook or Twitter, not whether traffic came to your site from a user clicking a Like button, a wall post or a Tweet that a member of staff posted on the official Facebook page or on Twitter or a wall post or Tweet that a user made.

It is really important to differentiate between these so you understand what’s working for you so that you can focus your efforts in the right places rather that trying to do everything averagely.

Tracking The Facebook Like Button

The Facebook Like button is a really smart piece of functionality. It’s relatively simple to integrate into your site, is immediately recognisable and users *hopefully* know what happens when they press it. If you use Google Analytics, there is a nice little tool available to you to help track urls, the URL Builder – http://support.google.com/googleanalytics/bin/answer.py?hl=en&answer=55578

This quickly allows you to generate the bits at the end of a URL to track what users do once they arrive on your site. Follow the instructions and you will end up with something like this:

http://www.mike-dixon.com/?utm_source=article-id&utm_medium=facebook&utm_campaign=fb-like

By adding these tokens at the end of the url in your Facebook Like code, when a user clicks the Like button, it is this url that will appear in the user’s Facebook profile and friend feeds. You can do the same for the Twitter widgets and many others – just make sure that you amend the tokens to reflect the widgets that you are using.

Tracking editorial wall posts

Similarly to the above, if you have an official page for your brand, service, web site or product use a similar method to the above. Use the URL Builder http://support.google.com/googleanalytics/bin/answer.py?hl=en&answer=55578 to generate your tokens and use this new URL to post on your wall.

Example: http://www.mike-dixon.com/?utm_source=article-id&utm_medium=facebook&utm_campaign=wall-post

This will then allow you to start differentiating between two different ways of driving traffic to your site from Facebook. So rather than just looking at your referral list and just seeing ‘facebook.com’, you can look at your Campaign reports and see specifically what is working best for you.

Once you have a benchmark of data, you can start tweaking your implementation of the Like button and the wording used on your Wall posts to start to optimise your efforts. If you decide that you want to start split testing your implementations, you can tweak the tokens for each instance being tested so you can compare the advantages of both.

You could also look at auto-posting versus manual posting and see which works best.

Gut feel does go a long way, but having actual acitonable data goes much further when asking for more resource or setting/achieving realistic targets.

And finally…

This is all about trying to use existing tools to give you more granular data for you to make better decisions. Rather than trying to implement a Social Media strategy blindly, you can really start to get granular data and make some actionable decisions.

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

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.