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.

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

Using 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.

Just presented on how to get value from your analytics platform to around 150 people at the Marketing Higher Education Symposium 2009, at the Sydney Convention Center.

136 slides in 30 minutes (quick fire)…lots of nodding heads and a few chuckles too, which is always nice.  I think it went well though.

If you were at the conference and have questions, please feel free to ask using the comments.  If you weren’t at the conference, and have questions, don’t be shy.

You can get access to the presentation slides (although it’s a bit tough without the voiceover).

I was chatting with one of our School Deans today about various results and he posed the question “Is it possible to see which courses people viewed after seeing one course?”.  His interest was based on the fact that the user doesn’t always purchase the “most frequently visited course”.  They often view one thing, but end up purchasing something else, and our reporting doesn’t highlight that behaviour.

Now, that got me thinking…that’s probably pretty common behaviour.  So how can we make that visible?

Pathing is common

Of course, it’s easy to show page pathing (which pages are viewed before and after a certain page), section pathing (similar but for a section), but pathing isn’t available across multiple visits (for the obvious reasons).  Traffic pathing is available on s.props, so as long as you report something into an s.prop, you can generate paths to/from it.  Paths are very valuable to see where a user goes after visiting a specific item such as a page, or how they got to a specific page.

However, the problem arises when you want to see something across multiple visits.

We’ve just had a similar problem with multi-visit campaign results, where the success event was being attributed to the latest campaign id, which wasn’t neccessarily what we expected.  In our case, due to the sales cycle being long (typically 1-3 months), many visits will occur and the user won’t always come in with the same campaign code.

For example, we might send them an email which drives them to the site.  The user engages, finds out what they need, but doesn’t convert.  They then come back a few days or weeks later by either typing in our web address directly, or come in through a search engine.  In either case, the success event (if they convert) would be attributed to the latest campaign, for example, Google or Direct/Typein (as we also have a VISTA rule).

Enter Campaign Stacking…

So, to provide some visibility to this activity, we worked with our consultant who recommended we implement Campaign Stacking, which, through the use of a cookie, appends a different campaign code (if they have one) to any previous one.

So, in the above example, we now have reports which show conversions by campaign combination.  We accomplished this by setting up a new eVar and writing a cookie (through an s_code plugin) appending the next campaign code to a previous campaign code.

Now we should be able which campaign combinations are driving conversions, over multiple visits.

Now stay with me…

I’ll bet we can do the same thing to understand product view combinations over multiple visits, leading to conversion.

In our case, a product is a course, but no reason this couldn’t work for any product category.  In our case, we don’t want to see which course “pages” they visited (we have that through course page pathing).  We want to see course pathing across multiple visits (or the same visit).

By setting an eVar with the name of the course, and using the same methodology as above, we should be able to get a view on this activity and user behaviour.

In theory, we should then be able to export the data and generate promo-type content that says “People who liked this course, also liked these courses…”

That will then help us to cross-promote “related” courses – not what we think are related, but what users are thinking are related.  Do that on an automated, daily basis and you really start to apply some value for the user.

That’s one of the great things about Omniture – flexibility to do this.

Guess what I’ll be trying over the next few days…I’ll update this one over time, if we get it working.

So, there’s lots of metrics and lots of terminology. Understanding the meaning is the first step to understanding what’s important.

This post is for those of you who want a quick primer into what’s important and what’s not. It doesn’t however talk about the “why” – that’s for a future post.

Forget about these…

1. Hits
   Hits were all the rage in the 90′s. “100,000 hits yesterday!” was often heard. Wow. Problem is, a ‘hit’ counts any hit to the web server, like images, not just pages. So, pages with images received more ‘hits’… so it was a pretty inaccurate/unfair metric… and a better metric to measure web popularity came along; the ‘pageview’ metric.
   
2. Web Counters
   Remember those? Again from a bygone age. No-one ever mentioned what time period the number was for, let alone what it actually measured. And those that did have them often got upset when the number stayed really really low…to the point where they would artificially inflate their own numbers.

Of limited value…

1. Browser Types
   Pretty much only good for knowing which browsers our users are using. But, today, every site should be made to render properly in multiple browsers.
2. Platform Types
   Likewise with the browser types, however, many providers now separate this out to mobile platforms, and as mobile platforms become more ubiquitous, we keep an eye on this. However, it’s important to see what content the mobile users are viewing and ensure it’s readily available to those platforms.
3. Resolution
   Again, good for understanding how to design the site with minimum widths in place. Note however, there’s a difference between screen resolution and browser size – two different values, two different results.

Basic, fundamental metrics…

1. Page Views
   This tells you the amount of views our site pages are getting – in particular, this allows us to see how the site fares over time. A view counts as a loading of a page. Still considered a very important metric, but the increasing amount of flash/AJAX built websites, and the increase in online video, means fewer page views are counted, even though the same amount of content is being looked at. Therefore, we need to consider our ability to track AJAX built sites, and Flash driven interactions. Any metrics platform worth it’s salt will be able to track this through custom tagging (which Omniture, of course, does).
2. Visits
   A ‘visit’ is the equivalent of when someone arrives at our site and starts looking at pages. A visit can consist of many pageviews, or just one. The industry standard is to expire the visit after 30 minutes of inactivity, or 12 hours of constant activity.
3. Unique Visitors
   A unique visitor counts the number of distinct people (well, really computers) that are visiting (making visits) our site in a particular time period. A unique visitor can make up many visits, each containing many pageviews. This is still one of the best metrics to use for site popularity. Bear in mind though that it’s important to understand the timeframe as well. Daily, weekly and monthly unique visitor metrics vary because of the reporting period.
4. Referrers
   This is a great metric – it tells us all the sites that people are finding our site and visiting from. If we don’t know where people are coming from, then we don’t know how our marketing efforts are doing, and where to spend additional money. There will also be Direct/Type In’s in this report, which provide a good indication of how many people start directly at our site (or from a bookmark). We seemingly get a huge amount of traffic from Google Organic search (which incidentally also has the best conversion rate – see below), but, read on…

   This is one to be slightly wary of as well… As this metric shows where a visitor originated from on the first visit, future visits can also be attributed to this original site. The wary part is that sites like Google write a cookie that hangs around for a really long time (6 months, versus the standard 1 month for media sites) and so if the first visit comes from Google, then two additional visits as direct type in’s, the referring domain will always show as Google (3 visits, whereas it should be 1 from Google and 2 Direct).

5. Search Engines
   This metric is a more detailed version of ‘referrers’ and tells us which search engines people are visiting our site from.
6. Keywords
   Hugely important metric. As it sounds, this metric tells us the top keywords that people are typing in at search engines and ultimately clicking through to our site. It’s basically an even more valuable, in-depth version of the ‘top search engines’ metric.
7. Geo
   Where are they coming from? Particularly useful for marketing overseas. You’d be surprised at how the content visited differs from country to country, and even regions within a country.

Spend more time looking at the following…

1. Average Time Spent
   This metric indicates the amount of time a visitor spends on our site and pages. It’s usually a good indicator of the quality of our website. The longer the time spent, usually, the better. However, a long number can be an indicator of a bad website experience and that people can’t find what they are looking for. It’s best to combine it with the bounce rate and exit pages (see below) to get a more accurate picture of the quality of site content. Also, the average time spent doesn’t take into account the last page seen (it has no way of knowing when the visitor closed their browser or walked away), so typically blog home pages suffer from this.
2. Entrance Pages
   All too often people just analyse and improve the homepage, because they think that’s where the majority of their traffic arrives from. However, the reality is that many people will arrive deep into the site through search engines. Looking at this metric reveals which of our pages are most often used as entrance pages. We look to improve these pages and make sure it’s easy for visitors to navigate from these pages – otherwise these entrance pages will become exit pages.
3. Exit Pages
   This metric indicates the amount of ‘exits’ from pages on our site. Therefore, it reveals the pages that drive people away. But remember, some exit pages are more natural exit pages, like purchase confirmation or ask for information signup confirmation pages. We look for the highest exited pages that seem to be an important path in our site flow (user journey), like course pages or information pages, and improve these.
4. Bounce Rate
   This is one of the most under-used, but most revealing metrics. To put it simply, it indicates the amount of people that, upon arriving at our site, immediately leave. Therefore, it’s a great indicator of the quality of our site. Bounce Rate is the percentage of single-page visits from entrance page visits for individual pages. A bounce rate below 40% for pages is considered good.
   
   While single-page visits / entrance pages is good, we also use page views / number of times this was an exit page…a slightly different view. This shows overall how much traffic left from this page.
5. Internal Search Keywords
   This is definitely a key metric and one of the most revealing and a subject of a future post. By looking at the keywords people use to search internally, it shows exactly what people are wanting/expecting to see on the site.
6. Multi Page Rate
   This is an interesting metric, used in combination with the Entry and Exit metrics. Basically, this one shows how well a page view contributes to a multi-page visit. Key pages should have a high multi-page rate percentage, as should key entry pages.
7. Repeat Visitor Rate
   This is another great metric to use, and is a great indicator of the quality of the site. Simply put, the more visitors return, the better the site is likely to be. The higher the percent of repeat visits versus first time visits is another great indicator to use for site quality.

The Holy Grail, well, definitely the most important…

1. Conversion
   Knowing the conversion rate is one of the most powerful things to know and act on. And not just conversion for the site as a whole but conversion rate by page or set of pages. Conversion doesn’t automatically mean that you have to be selling something. While it is the most common definition for conversion, it can also be a very powerful metric to highlight how user progress through content (pathing is similar but not quite the same). Ideally have a funnel for each conversion to understand where people are leaving before they convert – a prime candidate to analyze conversion rate and funnel is pages within an checkout form – traditional retail stuff.  However, any multi-page process will produce a funnel. Another important view is to look at conversion rates by referrers, which gives a good indicator of the value of various sources.We correlate conversions by country, by campaign, by promotion, by traffic source, by keyword (paid and organic), by path etc, which gives excellent insight and allows us to constantly look at ways to improve the conversion rates.
2. Value
   What is value? Value is many different things for many different reasons. Value might be an estimation of the value of a visitor, of a lead, of an application, of a purchase, of a request for information. You often hear that a company has lost millions of dollars when their site goes down. This is because they know on average how many orders might be placed and the dollar value of the orders…therefore they can calculate value.

   We also calculate value; the average value of a lead. Leads are a primary KPI on our site. Leads submit applications. Applications drive revenue. By reverse calculating through the funnel, we can calculate the value of a lead. Hence, the importance of leads. And hence the importance of understanding conversions.

In a future post I’ll explain why just looking at the numbers really doesn’t accomplish much. The value is in the strategy. Metrics are used to measure business goals and user goals – therefore, understanding both business goals and user goals is critical, as it helps to shape the information tracked, which ensures that you get the best value from the metrics provided, and ultimately the best insight and ROI.