Do you run a subscription based website? You know the type of site: customers pay you a monthly fee and you give them access to valuable content. I’ve worked with a number of clients with this business model and have noticed that many make a consistent mistake when setting up their analytics package: they fail to segment people who subscribe to the website (i.e. members) from those who do not.
Why is this so important? Here’s an example. The following image shows some real data that is not segmented other than the removal of internal employees:
Now, here are the same metrics with members removed:
Big difference, huh? Just image how this changes all those metrics that are calculated using Visits, like conversion rate, abandonment rate, etc.!
It doesn’t matter what analytics tool you use. If your website has some type of member’s area you need to segment out members to get an accurate view of your website performance and online marketing activities.
The Google Analytics Way
__utmSetVar(), to set a cookie, named
__utmv, on the visitor’s machine. The cookie is a persistent cookie and lasts for
6 months 2 years. You need to call
__utmSetVar() when a member identifies herself. You can put it on a ‘thanks for logging in’. Here’s a perfectly good implementation of the code:
Remember, the above code snippet should appear AFTER the standard Google Analytics tracking code. The reason is that the
__utmSetVar() function is in the
urchin.js file. So if you try to call
__utmSetVar() before the urchin.js is loaded by the browser then the visitor will receive an error.
Once the cookie has been set on the visitor’s machine you can use the custom segment value to exclude them from a profile. The exclude filter would look something like this:
Any profile that has this filter will only show data for those visitors that are not members thus providing a more accurate view of how effective the website is at converting visitors.
And let’s not forget about the members. You can create a profile that only includes members (the filter settings are almost identical to the filter above, just change the filter type from ‘exclude’ to ‘include’). By creating a profile specifically for members you can focus on their usage of the member’s area. For example, the Top Content report will identify the content that they find most engaging. You could also use some of the loyalty reports to see how often they use the website.
Tip: Tracking Different Subscription Levels
If your membership model has various level, like Gold, Silver and Bronze, you can include this information in the custom segment value. This allows for a more detailed analysis of each membership level. Just modify the value you pass to the
__utmSetVar() function. For example:
After the custom segment cookie has been set you can create different profiles for each subscription level. Use the filter shown above, just change the value for Filter Field to match one of the values in the code above. Then you can use the profiles to analyze the member data and observe their habits.