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Is your content converting?

One of the little-used nuggets in SiteCatalyst is “participation”. It’s a given that you want to know how many sales you’ve made, or how much revenue you’ve generated, but what about which pages have helped to contribute to that conversion. Not every visitor follows the same path through the content, and it’s therefore beneficial to be able to see which pages are more likely to drive a conversion than others, thereby exposing your most valuable pages.

One of the little-used nuggets in SiteCatalyst is “participation”.

It’s a given that you want to know how many sales you’ve made, or how much revenue you’ve generated, but what about which pages have helped to contribute to that conversion.  Not every visitor follows the same path through the content, and it’s therefore beneficial to be able to see which pages are more likely to drive a conversion than others, thereby exposing your most valuable pages.

And some surprising results may surface; some pages that you thought were key pages, may not be; others that you thought weren’t, may well be contributing more to the purchase or conversion event.

Armed with this knowledge, you can further optimize your site.

Enter Participation

Basically, this little-used metric helps you to assess whether your content is participating in conversion events or not.

There are in fact two metrics that can help – linear allocation or participation (I prefer participation).

Linear allocation provides each page or variable value that contributes to the completion of a success event (e.g. revenue or cart additions) with a partial credit for the conversion. For example, if a user navigates through five pages of your site, and the visit results in a purchase, linear allocation divides the 1 purchase across each of the five pages so that each page receives credit for 0.2 of the purchase.  If conversion is enabled, then allocation for pages is also automatically enabled.

The downside to this type of allocation, in my opinion, is that you don’t necessarily see the true value.  If there are two visitors and two purchases, but one visitor sees 4 pages and the other visitor sees 2 pages, the allocation can get confusing…especially when you multiply that out by the variations of pages, visitors and the path length (number of pages they viewed).

Participation metrics, on the other hand, assign full credit to each page or variable value that contributed to the conversion. In the example above, a visitor navigates through five pages of your site, which results in a purchase. A participation metric gives credit to each page used in the purchase process. If any events have participation enabled, then the pages participating in the event also have participation enabled.

The result would be that “1” is assigned to every page they visited (even over multiple visits).  Eventually what surfaces, is high value pages – those pages that contribute the most to a visitor purchasing something.

Looking at a real world example of this, the following shows Applications Submitted Participation metric, against each page, with Page Views and Page Bounce Rate.

participation

In the above report, the top performing page that contributes the most to applications submitted is a page of content describing the application process.   Almost 52% of visitors who have submitted an application have gone through this page.

I’ve also created a custom metric – App Submitted Conversion Rate (Participation / Visits), which shows that nearly 6% of visits to that page result in an application being submitted.  So that, for us, is a key page.

What’s also apparent is that #8 has a relatively low participation rate (14.1%).  That page is one of the “parent” pages to the best performing page, which indicates that the path that is being followed does not take people through the parent page, otherwise, the participation would he higher.  (That makes sense to us as we drive people to the top performing page from a multitude of sources, such as course pages).

Using a pathing report, we can also validate that assumption, as we can see where they came from and where they went to on their journey for this key page.

Participation also crosses multiple visits – providing your configuration is set to ensure that cookies don’t expire on the end of the visit, so you’re then able to see how many times people return before they submit an application, or purchase something.

To some extent, the above result is expected (and desired).  Likewise for the homepage (#2).  Looking down the list, it’s a little surprising to see that the search page (#9) actually beats out some of the other pages, from a participation rate, but not an overall conversion rate.

So, it would be interesting to find out what they are searching for…

Slice and dice with Omniture Discover

search_terms_leading_to_an_appUsing Omniture Discover, we can dig into this data further.  Discover helps you answer the questions that you ask when you get the first answer – because the answer inevitably leads to other questions.

So, based on segment which we created on the fly called “visitors who have submitted an application” we can see what search terms they are using.  The results are shown below:

And what we see is that 25 applications were submitted from people searching for “honours” – which is interesting because honours content is not readily available on our site.

Learnings from Participation

Using Participation metrics can help determine the “value” of pages.  Pages that have low participation rates (that you think should have higher rates) are often good candidates for further optimization to assist in the conversion process.  Likewise, the same goes for pages that have low overall conversion rates (from the calculated metric).  Only you’ll know which pages they are.  Participation can be viewed as a kind of re-enforcement of the path you anticipate them to take.

By using Participation, you can determine the unbiased influence of any page on success and use it to further optimize your site.

The content and advice contained in this post may be out of date. Last updated on November 28, 2009.

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