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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?

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.

The content and advice contained in this post may be out of date. Last updated on July 30, 2009.

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