If you’re currently measuring various kinds of outcomes on your site (orders, leads, micro-conversions, etc.) with SiteCatalyst’s custom events (or default ecommerce metrics), you will want to know which content on your site is influencing them (successes or failures). Some content may be pulling more than their fair share while other content may just be getting in the way of conversions. The term, “content”, can span everything from pages to site sections (groups of pages) to page types (e.g., article page, category page, product page, etc.), etc., but for the purposes of this post I’m going to stick to the standard HTML page when I say content.

There are three different approaches you can use to determine content effectiveness (or ineffectiveness). The right method for measuring content may depend on the type of outcome you’re measuring as well as the difficulty of implementing the approach for your particular site. Let’s run through the three different options.

1. Event Participation (Indirect)

First, one of the best ways to evaluate content performance in SiteCatalyst is to use the event participation approach. Event participation essentially looks at the downstream, indirect influence of pages on subsequent conversion events happening during the same visit. In the example below, each page that preceded the order (represented by the shopping bag) receives full credit for the purchase. Any pages viewed after the order wouldn’t receive any credit for the purchase.

On its own, the participation metric will tell you two things: what percentage of the total events the page influenced and how many total events the page influenced. While this information is useful, it will be skewed towards high-traffic pages (e.g., homepage), pages where the event actually occurs (e.g., confirmation page), or multi-step process pages that always precede an event (e.g., billing/shipping page, lead form page, etc.).

In order to find the golden nuggets amidst the ever-growing pile of pages on your site, you will want to create a calculated metric which divides the participation metric by visits. Now you will have the average rate in which the page influences a particular outcome if it is seen during a visit. Certain pages will always outperform other pages due to their purpose or position; however, the participation rate puts the low-traffic and high-traffic pages on more equal footing.

In a use case example, you might have several case study pages on your site and you’re trying to generate trial software downloads. Using the calculated metric (trial download participation / visits), you can compare the different case study pages side-by-side to determine which ones have a higher rate of influence on trial downloads. If you identify clear winners and losers amongst the case study pages, three actions can be taken.

First, you can feature high-performing case studies in more prominent positions (e.g., homepage) to give them more exposure, which may lead to more trial downloads. Second, you can rework low-performing case studies to improve their performance. Third, you can remove the weaker case studies if they just aren’t resonating with site visitors, which will mean fewer distractions from your top-performing case studies.

Note: If you are interested in event participation, you will need to have your account manager or consultant enable event participation for specific custom events and traffic variables. Once event participation is enabled, it is not retroactive – it only rolls forward from the moment it was enabled. If you have Discover, you automatically have participation for all of your custom events.

2. On-Page (Direct)

While event participation measures the indirect influence of a page or other content on success events, you may want to know which specific page has directly influenced an action or outcome. For example, you may want to know which pages are generating video views or product ratings/reviews. In these scenarios, you don’t want any other pages to get equal credit for the action other than the page that the visitor was on when he or she instigated the action such as clicking a link or button. In the example below, the visitor passed through three pages before finally triggering a video view on the fourth page (Page D), which is the only page that receives any credit for the video view.

In order to measure the direct influence of pages, you need to capture the page name in an eVar on every page load. Whenever a custom event fires, it will be attributed to the last page that was loaded. If you would like to know at what point in the visit the visitor triggered the event, you will want to set a counter eVar that increases by one unit on every page load. This variable will give you a histogram of the number of page views that occur before the custom event happens. You can then manually calculate the average number of page views before someone triggers the action in question.

In terms of direct influence analysis, you’ll want to create a calculated metric of the intended action divided by visits so you can compare the event conversion rates of various pages. In addition to comparing individual pages against each other, you might also consider examining different groups of pages to spot actionable patterns or trends. For example, you might find that people are viewing videos more frequently for a particular product category (e.g., video games) or on a new page template that was just introduced. Based on these insights, you might consider creating videos for other product pages within the same category that don’t yet have videos or adopting the new page template more broadly on the site to increase overall video views.


3. Previous Page (Origination)

In a related but slightly different scenario, you may want to know which page originated a specific action such as an internal search, a lead form start, or the use of a store locator. Sometimes it can be difficult or impossible to track certain actions with on-click triggers so the custom event is set on the subsequent page where the action actually occurs. In the following example, internal searches would be triggered on the search results page (Page D). However, it’s more useful to know which page (Page C) the visitor was on when he or she initiated the search query.

In these “look-back” scenarios, you will need to use the getPreviousValue plug-in, which maintains or persists the page name value through to the subsequent page. Using this special plug-in, you can place the previous page name (or site section, page type, etc.) into an eVar variable. If you’re analyzing content performance, you’re going to create the same calculated metric (action metric / visits) so that you can identify low- and high-performing pages.

If you were to use the previous page approach for store locator usage or lead form starts, pages with high actions per visit would be viewed favorably as the content is driving potential offline sales through the store locator or lead form. In the case of internal searches, you will be looking for pages that have high internal searches per visit, which indicates that the pages are failing to present the right content. Visitors are not finding what they expected and are being forced to search for what they really wanted. In the example below, the bracketology page appears to be having some issues as it is experiencing a higher than average searches per visit.  Tip: With some additional tagging that is relatively simple, you will be able to tie search terms to specific pages. You will then be able to see what types of keywords are being searched for, which will give you hints as to what information may be ineffective or missing from the page.


As you can see, there are many ways to analyze the influence of content on success events, and different approaches can yield different insights. Hopefully, sharing these different approaches has generated some new ideas for how your company can better understand which content is and isn’t contributing to your online success. Good luck!

3 comments
Mikemorges
Mikemorges

Hi Brent, love the approach, thank you. 2 questions:

1. let's say you redesign multiple pages and launch the changes at the same time and want to compare before and after performance using participation. How to you measure the uplift knowing that your marketing people have dramatically changed the traffic composition just before the site redesign? In other words should you stabilise the traffic variable when you calculate participation conversion rate?

2. When you apply participation metrics in your page report, and you've changed a few pages, do you either 1. Sum up participation metrics (e.g. lead participation) for the modified pages using a filter and compare or 2. do you look at the total customer experience and sum up participation for all site pages as they all influence in a certain way your bottom line performance?

Brent Dykes
Brent Dykes

Jack, Can you be more specific about which option you could find in SiteCatalyst? Thanks, Brent.

jackmob
jackmob

well i just tried omniture but i cannot find one of the option which was mentioned in the above contents