Well, it’s August and true to their word, Adobe upgraded us to SiteCatalyst v15 on the 1st, and so I thought I’d share a few of the golden nuggets within v15.

I was thinking about how to order them…do I go by not bad to flamin’ eck, that’s awesome? Or start with the big bang and then let it continue to smoulder throughout? 

The problem is there are too many new and great features that you can’t really put them in any type of order.  They appeal to you on different levels, from functionality, to UI, to analysis, to reporting, to combination segmentation and sub reporting.

And as this post is kind of huge (sorry you might a coffee and a bagel on this one), here’s a little taste of what’s covered in it:

•  Segment, the all powerful segmentation
•  New segments
•  Site Overview Report
•  Segmented Overview Report
•  Side by Side segments (well sort of)
•  Key Metrics report
•  Normalization (one of my new best friends)
•  Visits, Visitors and PageViews
•  Full Sub Relations – multiple breakdowns on eVars
•  Traffic prop breakdowns
•  Login as another user
•  Calendar events specific to report suites
•  Significant changes

So let’s start with the big one that everyone knows about, or at least should.

Segments

Unless you’ve been living under a rock for the last 5 months, you’ll have heard about the segmentation capability within SiteCatalyst v15.

Out of the box, it comes with 7 pre-defined segments that are shared across SiteCatalyst, Discover, and Test&Target.

These segments were chosen because, according to Ben Gaines at Adobe, they are “valid (and important!) for all of our users—across vertical and market size. We’ve also seen these types of segments predict different behavior across a variety of actions: registration flows, purchases, and general site browsing.”

New Segments

You can, of course, create your own segments on the fly and apply them to any report as well.  If you’re a Discover user, you can create them in Discover and save them back into SiteCatalyst for later use.

I’ve not yet figured out how to create a segment available across all report suites, without creating it in Discover.  Perhaps Adobe can help on this one?

Segments can also be used in Test&Target too. There’s a one-click little target icon next to the segment box which opens up a new A/B..n campaign in Test&Target, although it doesn’t specifically target custom segments that you’ve created as that’s a little more complex – but the preconfigured ones are available for use immediately.

Site Overview report

This new report is actually a dashboard, but, it’s highly useful and can be modified to your needs.  Using the same features as dashboards, you can set this one to your landing page when you log in (no more pinwheel).

From those reportlets, you can get to the main report by clicking on the name of the reportlet.

You can also change the date and the whole thing will rerun against the new dates.

Segmented Overview

If you want to see a particular reportlet using a specific segment without running the whole dashboard again, just click on the report suite name within the reportlet and a little popdown appears, allowing you to select not only the report suite, but also a segment to use.

When you select a new segment, it will re-run the reportlet, not the dashboard, against the new segment.

Of course if you want to run the entire dashboard against the new segment, then just select the segment in the main segment dropdown and you’ll get instant gratification.

Side by Side Segments

Ok, so you still need Discover to do comparative segmentation, but, there is a sneaky little way to show two segments at the same time, using a dashboard report.  Generate your base report, for example, I’ve used calculated metrics on the new Key Metrics report (see below for more info).

Then apply a segment, and add it to a dashboard.  Apply another segment and add it to the same dashboard.

Then go to the dashboard layout editor (which is also new), and just put both reportlets into the new dashboard.

What you get is a visualisation of the two (or more) segments, plotted over time.

Key metrics report

One of my personal new favourites here is the Key Metrics report, which allows you to put multiple metrics on a time-based report.  This was always one of the big challenges before, but, they listened to us, and here it is.  And there’s a bunch of nifty things about this report that I just love!

Firstly, as shown above, you can add multiple metrics, or calculated metrics to the report – something you could never do before.  For instance, you can add Visitors, Visits, Page Views, Conversion Metrics, Conversion Rates and so on.

Now that you’ve got the key metrics trended side by side, every worthy analysis ninja will look to segment that information – so, just go ahead and apply those segments.

One of the great things about this report is you can do calculated metrics too…side by side (see the image for side by side segments above).

Normalization

Ah, what a great idea this was.  Normalize your data on the Key Metrics Report.  As you can see from the above screen shot, the page views data is overwhelming the trending report, making it difficult to view some of the trends for the other metrics.

But that’s ok now – just normalize it.  Same report as above, just normalized.  And the other metrics pop up.

Ok, so those metrics that I’ve used don’t really tell us anything.  How about this one then?

I’ve highlighted dates when certain metrics weren’t in line with the norm.

Now you have something to go and look at.  Why, on those dates, did those metrics “pop”.  What did you do?  Did they vary by segment?  And what can you do to nudge those that didn’t behave similarly?  You can learn from that.

Love this report.  One of my new best friends.

Visits Visitors & PageViews

Yes, these are now available across most conversion reports.  You used to be able to get Visits and Visitors by special request, but now, they’re enabled and you get Page Views as well.  And they’re particularly useful across things like campaign reports, referring domains, search engines, keywords etc.

Full Sub Relations – multiple breakdowns on eVars

Remember the days when you had to think carefully about setting up subrelations on eVars…should you use Full or Basic?  And remember the impact if you got it wrong, or realized later that you needed Full Sub Relations on a key eVar?

Well, those days are gone.

You now get full subrelations on all eVars (although I don’t quite understand why the admin asks you still for the type of subrelation you want when you create an eVar – Omniture…can you add any info here?)

In this example, I’ve used two eVars, both are set up in the admin as Basic Subrelations, but I’m able to now break one down by the other, and then I’ve added a filter to remove the keyword “murdoch” from the report.  As you can see, it’s a conversion report, with Unique Visitors, Visits and Page Views, as well as success events.

Once again, you can sub-segment this report against any of your existing (or on-the-fly) segments. Yay!!!

Traffic Prop breakdowns

Another great feature is the ability to now breakdown key traffic props, such as the referring domain report.  It used to have basically “instances” and allowed you to put in success events.

No more.

Now you can not only see Visits, Visitors and Page Views, as well as success events, but you can also now break it down by things like Average Time on Site, and, all eVars…wow!  Couldn’t do anything like that in v14.

And you can flip it too…start with a conversion report, such as our Figure Out Your Life segments, and you can break them down, by Referring Domains, or Time Spent per Visit etc.

Ok, long post I know, but, there’s so much going on that I’ll probably add more posts in the future on the new features.

Just to touch on some of the other new things as well…

Login as another user

Do you have a user that’s experiencing problems?  Well, now you can log in as that user.  Go to the admin and views the users, then click on “Login as this user”.

Report specific calendar events

One of the problems with SC14 calendar events was that they showed up across every other report suite…which made it very unhelpful to users that had somewhat restricted views, such as global sites and regional sites.  The UK office didn’t really care that you ran a specific promotion, but they saw it anyway.

Now you can apply a calendar event to a specific report suite only.  To be honest, the link for it is a little tough to spot, so I’ve put a whopping great big arrow to it:

Ok, now I’ve covered the things that I’m excited about, there are a few things that new users need to understand with this version.

This has all come about because of the way that data is stored.  It used to be pre-processed into the reports, which resulted in the limitation on segmentation and other capabilities such as breakdowns.  But Discover always worked off the original unprocessed data, which also led to some differences in the data, especially around unique visitor counts and classification deduping.

SiteCatalyst v15 now runs off the raw data – the same as Discover, so the datasets are the same, hence the reports are the same.

Key Differences

There are some key differences between v14 and v15 that you need to be aware of:

Visits for Non-Cookied Visitors

All visitors, regardless of them accepting a cookie are now included in Visit counts and pathing data.  But this increases your Visits metric, so your conversion rates will likely go down a bit.  In testing, the increase of visits was about 0.5% for first-party cookies, and 5-12% for third party cookies.  Another great reason why you should be on first party cookies (contact your account manager if your tracking server has 2o7.net in it).

Time Spent metric 

Both the time spent per visit and the average time spent on page metric now use all server calls to generate the metric, which is much improved on SC14.  What this means is that non-page view data is included in time spent reports, such as custom links etc.  And, they’re no longer bucketed.  It now works off an average for each and every individual page view.

De-duplicated Visits and Visitors in Classifications 

Classifications are now correctly de-duplicated, meaning that when you group things using classifications, they are now de-duplicated, whereas before, they would count each instance of a visit or visitor.

Ben Gaines wrote an excellent blog post about these and a couple of others which is definitely worth reading and getting to grips with.

Summary

What can you say?

Thank you, Omniture Business Unit within Adobe, for listening to us.  These changes make the platform even more useful than it already was and clearly makes it a powerhouse in the web analytics space.

With these, and many other changes made, we’re able to provide our organizations with even more insights that lead to more business optimization capabilities, a better ROI and hopefully more analysis ninjas.

Thanks guys!

So I promised that I would finally put fingertip to keyboard and talk a little bit more about using Visitor Scoring…to finish up the series that I started a while ago.

If you’ve read my previous posts, you’ll know that we implemented a series of metrics for engagement measurement, culminating in a per-visitor score.

I wanted to share with you some of the insights and benefits of doing all of this, particularly in Discover.

An Engaged Segment

If you remember from the previous post, we talked about using a total of seven different metrics to try to ascertain engagement, or, when used in combination, could identify an engaged user segment.

The final metric was a measure of interaction and there were two ways to implement; either by just counting the number of visitors that participated in specific actions on your site, or the second way; by creating a scoring methodology at the visitor level and leveraging that as the final metric.

A little tougher to do, as it involved some additional code across the site, but definitely implementable, and now, we’re able to use that information to gain even greater insights.

The reason you should segment is because not everyone on your site converts – in fact, probably only around 3-5% actually do what you want them to do.  So you use segmentation to analyse those that have converted and try to understand what made them different, what their different behaviours were, and if possible, try to use that as predictors of future behaviour by the other 95-97%, so that you can lift conversions.

Creating the engaged segment

Using Discover we created a Visitor container that contained the following rules, which we had previously determined worked best for our business.

The rules applied are from 6 of the original 7 indexes that defined engagement for us:

1. Click Depth Index – which captures the contribution of page and event views
2. Duration Index – capturing the contribution of time spent on site
3. Recency Index – which captures the visitor’s “visit velocity”—the rate at which visitors return to the web site over time
4. Loyalty Index- the level of long-term interaction the visitor has with the brand, site, or product(s)
5. Brand Index – the apparent awareness of the visitor of the brand, site, or product(s)
6. Interaction Index – visitor interaction with content or functionality designed to increase level of Attention

Notice the counts at the bottom of the segment creator – those seem to be a bit confusing as the number on the left (216 Visitors) represents the number of visitors that meet the criteria of the segment, but the number on the right (1,071,458 visitors) does not appear to be a count of visitors for the timeframe (for some reason I have yet to figure out).

Additionally, for this particular segmentation example, I wanted to ensure that our engaged visitors have actually become a lead (one of our primary goals).  So our segment includes that, as well as them having a score of greater than 200, which means they’re interacting with a fair amount of content or doing a fair amount of activity (such as applying to come to study with us).

Saving the segment, it’s then ready for use.

Using the segment

One of the first things I looked at was where our engaged users were coming from, their average scores, conversion rates and application rates (or purchase rates), compared to all visitors who are not staff or students.

And I fell off my chair.

While the number is quite small comparatively, due in part to the timeframe I’m using, the score, lead conversion rate (leads/visitors) and application rate (applications/visitors), are significantly higher.  This is to be expected due to the segment, but I wasn’t quite expecting the numbers to be that high.

Visual Site Analysis

I was keen to understand, visually, where these highly engaged users go on our site, so I used the Pathing Site Analysis report in Discover (one of my personal favourites).

Firstly I started with all visits to get a sense of what they do across common pages that we want them to interact with:

Fairly widespread usage of key pages.  The thickness of the arrow indicated volume of traffic from one page to another – the bulk of traffic goes to the Courses homepage, from the site home page.

Next, I applied the same engaged segment:

Ok, I must need velcro pants because I fell off my chair again.

It appears that our highly engaged users take a whole different path.  The colour indicates their propensity to become a lead.  The big red steps are basically the lead capture process through our main tool – Figure Out Your Course.

And they seem to browse around first within courses then become a lead – which is also good to know.  But once they become a lead, they tend to leave the site – which means we need to ensure that we’re effectively communicating with them through other channels, such as email at a later date.

Breaking down the traffic sources

In breaking down the Organic Search by time spent, we also see that a significant portion of highly engaged visitors spend more than 10 minutes on the site, and are more likely to convert to leads having done so, as compared to all visitors – another significant insight for us to use.

In summary

This is just one example of many that could be used on your site.  Once you’ve identified your engaged users, you can segment them further by demographics, or by customer type, or by content viewed, or by member/non-member and so forth.

You can view revenue by engaged/non engaged (bound to be vastly different), or average order value etc. 

If you use Test&Target, you’ll be in a great position to leverage the engaged user segment, targeting non-engaged users differently to increase their engagement levels.

All of this will help you gain better understanding into their behaviours, so that you can then further optimise your site to improve those conversions.

I’m keen to hear from others that have used Visitor Scoring, or Engagement Metrics across their site, coupled with Test & Target to lift conversions.  Let me know what your thoughts or successes are.

When it comes to searching across the web, we all know that Google is king, but does this still hold true across your own internal network?

Over the past 12 months we have wrestled with this question, particularly in an environment with multiple search mechanisms, manually maintained indexes, and masses of sites that were created when metadata was primarily used to categorise instead of search.

In a series of posts, I’m going to go through our experiences in improving search across our internal network – I’m not suggesting we have found the magic search bullet, or that we’re anywhere near finished tweaking and tinkering, but I do know we’re a hell of a lot closer than where we were at this time last year.

The problem

In our travels across campus, we kept hearing “I can’t find what I’m looking for!”  – not surprising, given that we had;

1. 500+ individual sites, ranging in age (earliest was 1997), metadata (none to Dublin Core to ‘something’) and ownership
2. Inconsistent use of key SEO elements, such as title, headings, tags and meta descriptions across the majority of our sites
3. Multiple sources of content and internal search mechanisms, each with their own set of search results
4. Manually maintained indexes, all categorised and sub-related, together with an in-house redirect mechanism
5. An internal audience of staff and students with heavily cyclical search requests – a search for ‘physics’ at the beginning of semester is more likely to be for text books, and at the end of semester past exam papers

Image: Multiple source-centric result sets;

Given Google’s dominance in search, we quickly went down the path of a Google Search Appliance, or ‘Mini’, which is a self contained rack mounted system that gives you God-like powers over the Google algorithm. We were bringing in a little bit of Google in to magically transform our disparate set of sites into a cohesive set of search results.

Once plugged in, the Mini worked really well – for pages that were properly formatted for organic search.

Pages that were missing or incorrectly using titles, headings and metadata didn’t fare so well, and we found the search results were not the most relevant, as the Mini couldn’t make much sense of most of the content it crawled. We also found that there was no clear way to incorporate the feeds from other systems, with the “how do I…” answers primarily provided by a community of Search Appliance users and resellers, and not Google themselves.

Given the wide ownership of the sites we were working with, updating each with appropriate SEO friendly content was unrealistic. What we needed was a way to;

1. compensate for the lack of SEO content,
2. incorporating multiple sources/ formats of content,
3. allow for cyclical requests to ensure the most relevant results appear, and
4. combine all the different sources of search results into a single set of user-centric search results.

Enter Adobe Search&Promote

If you’re a regular visitor to this blog, it will come as no surprise that Tim is a power user of Omniture products, steadily working his way around the product wheel. We became aware of the Search&Promote product (then called SiteSearch) which promised to solve our key internal search issues.

Search&Promote uses a search algorithm to organically crawl your sites, in addition to ranking rules based on a wide range of configurable data. Once you’ve defined your rules, you can adjust the overall balance between your ranking rules and natural search relevance.

Where there is a lack of metadata, Search&Promote can be configured to dynamically inject metadata on crawl, based on a URL pattern. Additional custom metadata can also be injected to create facets (filters) that allow users to drill further down into predefined categories.

If your multiple sources of content can be transformed into XML feeds, then that content can be crawled, categorised, and integrated with the organic results by Search&Promote.

Yes, there are other internal search products on the market that will do the above, however there is one thing that Search&Promote has over its competitors – the ability to tightly integrate with SiteCatalyst and Test & Target.

We’ve known for some time that internal search terms follow highly cyclical patterns as our student (and staff) needs change over the semester. We’ve helped them find what they’re looking for using of real-time SiteCatalyst data in search-as-you-type and tag cloud mechanisms, however with Search&Promote we now have the opportunity to take internal search to the next level.

In the report below (7 day moving average) you can see two popular search results across three semesters peaking at different times during the semester;

Notice how ‘bookshop’ peaks at the beginning of semester, then dies down, only to peak again at the beginning of the following semester. No surprises here, but it does coincide with a significant increase in page views across the Bookshop website.

Then look at the results for ‘timetable’ – there’s a peak at both the beginning and end of semester. The difference here is that people are actually looking for two different pieces of content – their semester timetable at the beginning, and their exam timetable at the end – using the same keyword. Again, the rise in search terms coincides with increased page views across each piece of content.

So, in theory, by looking at the last week’s worth of traffic across our group of sites, we should be able to determine what content students are looking for, then re-rank the search results accordingly. For example, the term ‘timetable’ at the beginning of semester will push results related to the semester timetable to the top, and at the end of the semester push results related to the exam timetable to the top.

Exciting stuff!

Next post – the implementation.

This is the third (but not final) post in the series on Visitor Engagement.  One of the problems with the Visitor Scoring method that I previously described, is that, at the end of the day, you’re still somewhat limited to viewing scores at the “average” level, by segment.

That presents a number of challenges because the average is precisely that…and the underlying scores vary dramatically within each segment.

But there is a way to see what each and every visitor score is, or even within the different segments…and it’s called the Unique Visitor ID.  You can see at the visitor level, how many times they’ve returned, how many “things” they’ve done, such as searches, product views, revenue etc.

This is really the icing on the proverbial cake.

Visitor ID

There’s a seldom used implementation method in SiteCatalyst called Dynamic Variables which easily enables all of this capability.

All you need is a spare eVar and you’re in heaven.

Step 1

Simply populate an eVar with “D=s_vi” and the result is that SiteCatalyst will populate the eVar with the unique visitor cookie ID.  If you test it with WASP or something similar, you’ll only see that D=s_vi got passed, but the magic begins.

s.eVar56="D=s_vi";

Step 2

Go into you Admin console, and on the report suite where you want this collected, go to Conversion > Unique Visitor Variable section.

You’ll be presented with a dropdown of available eVars that can be selected to use as the Visitor ID…simply select the one that you’re using and hit save.

That’s it – you’re done!  All there is to it.

Rest assured, it does not impact anything to do with the way that SiteCatalyst measures unique visitors – its completely separate and safe to use.

And now it pops

So, the first thing you’ll see is a new report on your eVar. The report is a list of all Visitor Cookie ID’s captured during the timeframe selected.  As it’s a conversion variable, you can add in whatever success events you like:

Add in your Visitor Score and you can see what their points are.  As you can see from above, I’ve highlighted an interesting visitor (#7)…they were on their 2nd visit, they searched 12 times, viewed 16 courses and submitted an application.  Their score is currently 542.  I’m wondering if visitor #5 is a candidate for an app too…similar behaviour is forming.

Segment-based Visitor ID’s

Ok, now open up a report such as Campaigns (we use the Unified Traffic Sources VISTA rule so it may look a little different if you don’t use it).

Remember the challenge I mentioned at the top of this post…with average scores.

We see below that the average visitor score for Organic Search is 7.88.  But, that’s just an average.  We know that from our scoring methodology, you have to have a higher score than that if you’re interacting with content.

So, when we break down the Search Organic by Visitor ID’s, we see a very different story.

Look at that…the top 10 all have scores above 300!

So to get to an average of 7.88, we must have an awful lot of really low end scores that come in through organic search.  And actually, that makes sense…we have a lot of reference content across our site, such as Library and School content that is very popular and that’s likely where these users with low scores are going.  Not the ones we’re really interested in from this scoring standpoint though.

Extracting Visitor ID’s

The next fancy bit of functionality, once you’ve enabled the Unique Visitor ID, is the ability to extract those visitors that have met a certain criteria (success event).

If you now click on an item in, for example, the Last Campaign report (at the individual campaign tracking code level), instead of heading off to the summary page for that item (which by the way, is useful also), you’ll get a new menu:

If you select the success event you want the visitor ID’s for, you’ll end up in a data warehouse data extract screen, where the report can be scheduled for immediate delivery:

Here we’re extracting visitor ID’s for those that submitted an application having come to us from Google Australia.

The Icing

I think the real icing on this cake is the ability to look at all visitor scores at the individual visitor level, irrespective of the segment, so that you can get an idea of what their scores are, to help better gauge those low/medium/high engagement buckets.  It’s also really useful to see at the segment too.

You’ll need to use Excel to process the data (and one thing I should point out is that an eVar can only have a maximum of 500,000 unique values in a given month – so if you get more visitors than that, then this might not work for you).

Other ways to use this is to extract emails that have responded to a campaign – if you capture the memberID in the eVar you can then extract the data for later use.

The next thing we need to do is re-configure our Discover segment for the interaction index and amalgamate these metrics into the overall engagement scoring methodology.

This is a follow on post to my previous one about measuring that elusive engagement.  This post focuses on the aspect of applying a score to visitor interactions, as they interact with your content and applications.

Visitor scoring is fairly simple – especially in SiteCatalyst, and by leveraging the data in Discover through segmentation, (and ultimately in SiteCatalyst 15), it’ll give you even more insight into visitor engagement.

Visitor scoring measures and assigns a relative value to individual customers and prospects based on their actions and behaviors over time. You can determine intent and engagement – even before visitors convert.

Once you’ve identified your most valuable visitors, you can dissect their actions to determine the campaigns, keywords, referring sites and offline touch points that engage them – and invest more on these efforts.

Scoring

The basic premise of a visitor score is to give them “points” for certain activities.  What score you decide to give them is entirely up to you…the important thing is that they accrue points over time, and you can use those points to create segments of visitors that have exceeded certain thresholds.

In doing so, you’ll be able to compare different visitors, for example, those with a high score, to those with a low score, from different traffic sources, or across different campaigns.

The theory behind this is that visitors with higher scores are likely to be more engaged – they’ve accrued more points over time, by doing the things you want them to do.  It’s not just looking at conversions, although they will probably feature in your scoring methodology.

Imagine for a moment that you have a number of visitors:

Visitor 1 – comes to your site from Organic search, looks at 5 different products, watches 2 videos, signs up for your newsletter, but doesn’t buy anything.

Visitor 2 – comes to your site from Organic search, looks at 1 product and buys it.

Visitor 3 – comes to your site through an email campaign, and views 8 of your products.

Visitor 4 – comes to your site through Paid Search, looks at searches for a product, views it once and then looks at 3 FAQs.

Which visitor is more valuable to you?  Who is more engaged?  Visitor scoring will assist you to better understand the answer to that – and when you slot this Visitor Scoring methodology into the Interaction Index, and use Discover, you really do get a good proxy for engagement.

Ultimately most sites exist for a specific reason, whether it be to convert the visitor to a purchase, or allow them to self serve, or engage them with your content etc.   And we all want them to do that.  But only a small percentage will.  In fact, in general, it’s around 3-5% of visitors that actually “convert”.

First things first

What you need to do initially is agree on a set of interactions across your site, and then apply an arbitrary score to them, either between 1 and 10 or 1 and 100.

For example:

1. Home page viewed or Landing Page Viewed = 1 pt
2. View a general content page = 5 pts
3. View a Product = 10 pts
4. Search = 20 pts (I tend to think that if a visitor is going to take the time to search for something they can’t find, they’re more engaged, hence the higher score)
5. Watch a video = 30 pts
6. Use an interactive tool = 40 pts
7. Sign Up for a newsletter = 50 pts
8. Provide feedback/rate and review/comment = 75 pts
9. Buy a product or conduct a self service transaction = 100 pts

Don’t worry about the actual scores, but do leave room for additional interactions that you can add in if you need to (but remember to communicate it out if you do add in others, as scores will go up).

Now that you’ve scored various activities, you need to implement the code to measure those interactions.

Implementing the scoring

In SiteCatalyst that’s easy enough to do – you need an eVar and a success event.

Set up the success event as a Counter Event. 

Note that you can now have counter events where they take a value other than 1, which is what is needed for the  scoring.  However, I believe you’ll need version H23 of the s_code to support this. 

Create the eVar as a Counter eVar, not a text eVar.

Visitor scoring is done by simply passing the score value as a +number into the eVar and the success event is set with the =number (yes, you no longer need to pass the event through the s.product string).

So, for example, on your homepage, you simply add the following:

s.events="event1=1"; s.eVar1="+1";

On your search page, you’d include:

s.events="event1=20"; s.eVar1="+20";

(The above obviously assumes you’ve used event1 and eVar1 for Visitor Scoring.

Complete that process for each of the key interactions and you’re set.  We implemented ours directly into our s_code through a variety of s.pageName value matches, or product views, or other success events occurring.

To put this in context, we’ve implemented the following scoring:

1. Homepage view = 1pt
2. Searched for something = 5pts
3. Viewed a course = 20pts
4. Completed a form = 30pts
5. Used one of our interactive tools = 50pts
6. Opted in to something = 70pts
7. Submitted an application = 100pts

Making it legible

First thing you need to do when you implement this type of thing is make it all a bit more usable.

When you look at the raw data (your eVar against conversions), you’ll see something like:

Remember that the success event (Application Submitted) is associated back to the value seen in the eVar, when the success event was applied – so, in the example above, 37 apps were submitted when visitors had a score of 190; 22 had a score of 211, etc.  Notice also that as we’re showing both Apps and Leads, we also see that they don’t correspond; Leads has a very different scoring result (see below).

Overall, it doesn’t tell you much – you actually want to group the eVars together.  So, use SAINT to classify them into buckets. 

Important Tip: When you use Excel to classify your eVar, the key column will be the interaction score.  You need to keep that column showing 2 decimal places – when you export from SAINT, it has the decimal places on it, when you open in Excel, the decimal places disappear, and if you re-save without decimals and upload again, they keys will be different, and the reports won’t work.  So, put the key column to 2 decimal places and then classify it.

That brings about the problem of what your buckets should be…well, that’s up to you.  Only your data will tell you that.  But, you can experiment with different buckets to see what works best.  To be honest, it’s best to do this in Excel, rather than SAINT, as you’ll want instant gratification.  And you’ll obviously need data collected before you can classify…so it’s best to run it without classifications for a while.

We classified ours following a bit of analysis using Excel to figure out the best buckets for Applications Submitted and Optins.  As it turned out, after a lot of playing around, we chose a logarithmic scale, as it seemed to group everything the best:

What we see in Excel is that with these buckets, 80% of applications occur before a visitor has a score of 320.  We also note that most apps occur when visitors have a score of between 160-320…our sweet spot.

So, using SAINT, we classified our Engagement Value eVar into our buckets and uploaded back to SAINT. 

Tip: Due to the size of the file, we typically use an FTP upload now – it took less than 10 minutes to classify the data in the reports.

Now re-run the above SiteCatalyst report, this time using your buckets:

Remember above I said that leads obviously have a very different score?  You can see that in the above report, that Lead Completes tend to happen when the visitor has a score of between 40 and 160.

What we see from Excel with Leads is that 80% of leads occur before a visitor has a score of 80 – which means that those that become leads, do so pretty quickly; they haven’t visited much other content (otherwise their score would be higher) – which is great news for us!

Another way to classify the scores is to set “low, medium, and high” buckets for engaged visitors.  I’m still trying to figure out what we should use as those buckets, as we have a very large spread.  Standard Deviation will probably assist in that one eventually.

Calculated Metrics

At this point in time, you probably also want to create a few calculated metrics to get some averages out.

Ones that I’d recommend are:

1. Score per Visit = [Engagement Score] / [Visits]
2. Score per Search = [Engagement Score] / [Instances (Report-Specific)]
3. Score per Referrer = [Engagement Score] / [Instances (Report-Specific)]

Why is Instances in there twice?  Because a) Visits are not available across every report type and b) the naming convention just makes it a bit easier to understand (IMHO).

Did you know you can also trend calculated metrics?

Now that you’ve got all this wonderful new capability, what do you do with it…?

Segment it (of course)

There’s all sorts of different ways you can use this…all basically segmentation based.

You can look at traffic sources, campaigns, keywords, geographics, user type segments, etc.  You can trend and compare your segmented values over time.

In the example on the right, we see that while Australian visitors have an average score of 5.82, the highlighted items have much larger scores, indicating that they are engaging with more of the things we want them to do.  The example on the right was extracted using Discover, as in SiteCatalyst 14 the capability with geo-demographic reports is somewhat limited to country/visits.

In the next example, we’re looking at scores by course interest.  The course category is actually an eVar that is set in various places across the site – similar to a product category.  When a visitor browsers different bits of content, we’ll set their course category differently, which is then used for Test & Target purposes…but works well here too.

We see that while Undergrad had more course views during this short time frame, they scored actually slightly less than Postgrad visitors.  This would indicate that PG visitors tend to read more, which would also make sense, as it’s a bigger purchase decision for them.

And as a final example, scores by different campaign types (Organic, Paid, Campaigns, etc).

Hmmm…seems Paid Search is doing really well; not a huge amount of traffic during this period, but they are interacting a lot.  Break that down by keyword or Adword group and you’ll get even more insight.

Here we’re looking at Branded vs. Non Branded keywords (and their top keywords):

Seems that branded search terms generate a higher interaction, and in fact when they type in the full name of the university, they have the highest interaction.

Multi-scoring techniques

You’re not limited to having just one scoring methodology.  We’re actually in the process of implementing a second one for a completely different reason.  All you need is a spare eVar, another success event, and bit of time.  Rinse and repeat!

Comparative and Trending

Of course, you can also trend on the calculated metrics to ensure your overall interaction score is going in the right direction, and you can trend by different segments too.

Likewise, you can do comparative analysis using different dates to compare the interaction scores.

Extending the scoring

In SiteCatalyst 15, you can leverage the segments for visitors with scores higher than X – you can either use the value of the event, or you can use the SAINT classifications. 

Additionally, you can use these in DataWarehouse and Discover – or start from Discover and put the segments back into SiteCatalyst.

And, you can leverage the segments and scores in Test & Target to further optimise user journeys and conversions.

There’s just a whole heap of different ways to use this information.

Next post

In my next post, I’ll combine the above interaction Visitor scoring into the Discover segments for a full on Engagement Analysis, replacing the previously described interaction score with this one.

I thought I’d share a real humdinger with you.

It’s campaign time again.  Not just any old campaign, it’s our main recruitment campaign of the year.

What we normally do on this campaign is behaviorally target content to users based on their application stage.

Why?  Because we know from previous tests that behaviorally targeting content for re-engagement purposes not only lifts our application completion rate, but provides more relevance to the user when they visit our site – instead of just seeing a standard campaign message each time.

Given the value of an application, and the lift we see, it’s well worth the effort to go down this path.

But this time, the gates of hell opened and out rode the fifth horseman.

We typically use 4 different stages, with one experience (content) per stage.  The challenge we always faced was that for each stage, we always wanted to run A/B tests to see which converted better – but could never figure out how to do it when it’s coupled with a behaviorally targeted campaign.

A few months ago, I worked with one of the Omniture Test & Target genius rock stars, implementation engineer Randall, who worked through a nested mbox example with me, which could be used to nest A/B tests within a single Landing Page campaign.  Unfortunately at the time we didn’t get to implement anything (or even try it – but it looked great on paper).

Knowing that our primetime Mid Year recruitment campaign period was nearly on us, I dusted off the email and had a good ‘ole read, as I felt it had some opportunities for us in a variety of ways.

And so the madness begins…

Always up for a challenge, I strapped on my Test & Target boots, and suggested that we could perhaps, possibly, maybe, ‘ish, try, have a crack at, putting together a behaviorally targeted campaign, where at each stage of the process we not only change the offer (easy) but run an A/B test (difficult) to test how well different offers convert people to the next stage.

Be careful what you wish for…because not only did they like that idea, they compounded it by throwing in another campaign on top of that one.

Mid Year campaign is normally restricted to Undergrad recruitment.  At least, that’s been the (now) historical position.

But no, this year, we’re doing simultaneous Undergrad and Postgrad Mid Year campaigns.

Yikes!!!  Go big or go home!  In for a penny, in for a pound.

So, to summarize, we want a simultaneous 4 stage behaviorally targeted campaign with a series of A/B tests within them.  And we need to switch users between campaigns based on whether they meet certain criteria, or whether they’ve viewed specific types of content.

Hmmm…a real humdinger!

Test & Target to the rescue

It gets a bit tricky to do, so I’ll do my best to explain it.

Before I do though, here’s an illustration of what was actually created.  Each of the rectangles with the little red circles is a separate Test & Target campaign.  You’ll see there are 11 of them.  The final bottom row is content…you’ll see there are 12 of them.  And you might also notice a total of 10 nested mboxes!

In a bit more detail, we’ve ended up with:

• 1 primary campaign as a Landing Page campaign that switches between an UG or PG experience, with a default offer for those we know nothing about.  The UG or PG experience then calls on (via a nested mbox):
• 1 of the 2 recruitment campaigns (UG or PG) as Landing Page campaigns so we can target one of the four  stages of the application, via nested mboxes, which calls on:
• 1 of the 8 A/B..n campaigns to determine the offer displayed

There’s a total of:

• 10 nested mbox offers…
• 12 content offers (the things people actually end up seeing)…
• 5 expression targets…
• And 6 profile targets…

Holy moly, I can hear you say.  Why so complicated?  It’s complicated because of the need to switch people between the campaigns based on what content and stage they’re at.

So, how does it all work?

The top campaign:

As we want to target initially by user interest (they’ve previously viewed Undergrad or Postgrad content, or they’ve visited an Undergrad or Postgrad course, or they’ve visited the Undergrad microsite or Postgrad site, or they’ve come in from an Undergrad of Postgrad email campaign, or they’ve visited the Figure Out Your Course tool or the Postgrad tool), we need to be able to get them into the relevant Undergrad or Postgrad campaign.

So, we need to use a Landing Page Campaign, which is set to re-evaluate the rules (and hence the offers) each time it displays.  This allows a user to see different experiences each time, based on the rules above.  It’ll switch a user to the opposite campaign if they go, say, from an Undergrad course to a Postgrad course.

But, we don’t want to show them an offer just yet.  We’re only doing this to determine which campaign they need to get into – and change that decision on the fly.

Hello nested mbox

On our home page, we already have an mbox (marketing box) defined, with default content.  So we start off by creating a campaign to use that mbox.

The following shows the top level campaign, targeting the default mbox on the page – HP_lowerleft.

Instead of serving content back, we want to serve back a different campaign (Undergrad or Postgrad), so we actually need to serve back a nested mbox as our offer.

A nested mbox is just an mbox served into an existing mbox, which then makes another call to Test & Target to get more content.

So, if I meet the criteria for Undergrad, then the content offer that Test & Target serves back to me contains a new mbox.  The content offer looks like:

<div class="mboxDefault">
</div>
<script type="text/javascript">
mboxCreate('UndergradNested');
</script>

I repeat that type of content offer for the Postgrad campaign offer, but call the other mbox ‘PostgradNested’.

From a conversion standpoint, we set the Conversion as an SiteCatalyst:event…meaning that whatever the user does, the campaign thinks they’ve converted, and as they restart with the same experience, it re-evaluates them each time, to determine which offer to show them.

That’s campaign #1.

We’ll call it the Mid Year Parent Campaign – and it’s the one at the very top of the illustration.

Now that I have that, I need to create two sub campaigns; one for Undergrad, the other for Postgrad. These campaigns will serve something back into the UndergradNested mbox, or the PostgradNested mbox.

Main Sub Campaigns

So, as I also want to serve back behaviorally targeted content, based on their stage of application, I need to repeat the above type of scenario, but have the experiences based on the users stage.

Stage of application

Once again, a Landing Page campaign allows a user to see different experiences each time, based on the rules evaluated.

So, we created another Landing Page campaign, with 4 offers, each offer targeting the stage that the user has completed:

1. They’ve started an application but haven’t completed it yet
2. They’ve qualified for entry, but haven’t started an application yet
3. They’re a repeat visitor and haven’t qualified yet
4. They’re a new visitor and haven’t qualified yet

But, again, we don’t want to show them an offer just yet – we want to show an A/B test for offers 3 & 4, and different content for offers 1 & 2.  So, again, we serve back a nested mbox offer, that aligns with the users stage.  In the example below, I’ve shown the nested mbox content for Experience D:

<div class="mboxDefault">
</div>
<script type="text/javascript">
mboxCreate('1stTimeVisNested');
</script>

A/B..n Sub-Campaigns

Now that I have the Undergrad main behaviorally targeted campaign created, I need to create the 4 sub-campaigns which will serve back real content that the user will see.

So, I create a new A/B..N campaign, target the mbox 1stTimeVisNested created above, and create 2 different experiences for it.  That’s the A/B test for the 1st Time Visitors who haven’t qualified.

I repeat that for Repeat Visitors who haven’t qualified, but this time I target the campaign at the mbox RepeatVistNested.  This one also has two offers (the A/B test for this campaign).

Then I create two more A/B campaigns which will serve back content into the respective mbox defined during the other two stages of the application.

Complicated I know – hope you’re still with me.

Rinse and Repeat

Once all that’s been created, it’s a fairly simple matter to duplicate each campaign for a Postgrad version, modify the offer to create new distinct mboxes for each one, and set up the A/B campaigns, with their respective offers.

In doing so, we now have a total of 11 campaigns and 10 nested mboxes, with 12 content offers.

Now to the targeting of it all.

Targeting Expressions and Profiles

Interestingly, this turned out to be the hardest part.

The very first campaign needs to determine whether to ultimately display Undergrad content or Postgrad content.

So, as we have OR’s in our rules, we need to use an Expression Target (basically Javascript that sits in the T&T target rules and returns a true or false).  We have to use an expression because the rules at the campaign level are all AND-based rules…which don’t work for us.

So, we have two Expression Targets on the main top level campaign…one to determine Postgrad the other to determine Undergrad.

By way of explanation, the Undergrad target expression basically says:

1) Get the value of the last campaign that the user clicked on (that’s our campaign tags)
2) Return true if the users last:
a) coursecategory was Undergrad, or
b) tool used was Mid Year, or
c) LastMicrosite visited was Undergrad, or
d) campaign tag contained “undgrad”, or
e) the current URL query string contains a special one for us to test it with!

The same is basically true for the Postgrad target, just different values.

These two expression targets are then used in the campaign, highlighted below.  Despite the fact that it says “…is present”, what it actually means is “… is true”.  Never understood why that is, but there we go.

Then we need to set up our Application Stage targeting – basically telling us if the user has Qualified for Entry and hasn’t started and app, or if they’ve started one but haven’t completed it yet.  So we added more targets as follows:

You’ll notice in both that there are a combination of both user and profile based rules.  User rules are script based and are set up in the Profiles area of T&T and profile based rules are values passed into T&T from an mbox.

We use a lot of mboxes across the site and pass quite a few profile parameters in through the mboxes – which makes it easier for us to target later.  All of our key stages have been pre-tagged with mboxes and various parameters.

The only one to use a user-based profile is our lastcampaign profile.  Anytime a visitor comes to our site and the visit contains a campaign tracking code, the code is automatically passed into Test & Target and saved into their user profile, via a script, shown below.

You’ll also notice some of the profile.parameters that have been passed in – note that you can’t edit them.

In testing all of this, I just couldn’t figure out why I always ended up in the repeat visitor campaign for either UG or PG.

The reason was easy to overlook, and worth mentioning:

I’d actually structured the order of offers back to front in the second level campaigns…stupid me, I did know this, I clean forgot when I created the campaigns (Omniture: it would be great though to be able to re-order experiences – but you can’t you have to delete and start again).  The reason being, you have to put the highly targeted ones at the top and the broadest match at the bottom, as T&T evaluates in the order that the experiences are displayed.  And thanks to Veronica at Omniture for spotting a misplaced apostrophe…they’ll get you everytime!

Testing everything

Yes, it’s a challenge – this many campaigns and offers means lots of new sessions, clearing of cookies, and re-establishing the stage of the user journey, but after a few hours, confirmation was established that everything was as it should be.

The biggest problem was that as we were serving back nested mboxes, we couldn’t preview the content in-situ as you would normally do through On-Site Preview, as it looks for the mbox on the page…and they don’t “exist” yet because they’ve not been served back yet.

But we go there.

Performance?

With all of these nested mboxes flying around, I was concerned by performance.  But, I was really happily surprised…it’s virtually undetectable.  Using WASP we’re able to see the order that the mboxes are served in and they match up correctly.

In Summary

So, there we have it.  Two behaviorally targeted campaigns, with triple backflip A/B twists thrown in.  The horse is back in the stable, the gates of hell are securely closed again and Rapture didn’t happen.

Test & Target came to the rescue and allows us to do what we need to do from a campaign standpoint.

I can sleep now – the campaign is live, you can experience the different offers by viewing the homepage, then a Postgrad course, then an Undergrad course, then repeating your visit later, or starting an app and not completing it.  If you feel so inclined…

One of the most powerful ways to enable an audience connection is through behavioural segmentation.

Many companies today segment from a business standpoint.  Don’t get me wrong, this is a good strategy and aligns your measurement and optimisation strategy with your business segmentation model.

Customer / non-customer segments.  Product A owners / product B owners.  Mosaic-based segments.  Geographic segments.  Lead / Non-lead segments.  These are all typically business-based segments, and you should definitely be segmenting using this methodology if your overall business does.

But I think there’s a higher level of segmentation – behavioural segmentation.

Behavioural segmentation in action

Every time someone visits your site they’re telling you what they’re interested in.  These behavioural segments are measurable and usable.  And moving from business-based segmentation to behavioural segmentation truly allows you to relevantly engage with your customers based on what they’ve told you about themselves, through either their activity on your site, or through forms they’ve completed.

At Murdoch, we recently re-developed our primary lead generation tool, called “Figure Out Your Life”.

FOYL, as it was affectionately known to us, was the University’s primary lead gen tool – it essentially asks you a number of questions and then suggests courses or careers that may be of interest to you.

Overall, the redesign was based on user feedback from focus groups but we took the opportunity to introduce behavioural segmentation into it, to not only measure those segments over time, using a combination of SiteCatalyst and SAINT classifications, but to enable more refined and relevant content targeting to each segment for optimisation and engagement purposes.

The new FOYL, now called Figure Out Your Course (or FOYC) asks 6 personality-type questions, and allows the user to rate their answer to each question, on a sliding scale, from 1 to 10.

The combination of their answers to the 6 questions results in 3 recommendations for courses they might consider.  

I’m not going to reveal the methodology behind tool – that’s confidential IP and very complex, but I will talk about the segmentation opportunities it allows.

How we achieved behavioural segmentation

From a segmentation standpoint, we decided that we wanted to firstly measure the segments to see what “types” of users are completing the tool, and secondly, through integration with Test & Target, we can then customise content to each user, based on how they answered each question.

To do so, we needed to capture each of their answers in an eVar (conversion variable), which must be remembered over multiple visits.

To enable more realistic clustering of segments, we decided that while the tool works on a rating between 1 and 10, we’d actually work our segments on a rating of 1 to 5.  So we “bucketed” each answer accordingly:

Moving the slider to position 1 or 2 makes them segment 1,
3 or 4, segment 2
5 or 6, segment 3
7 or 8, segment 4
9 or 10, segment 5

In doing so, each user now scores (from a segment standpoint) between 111111 and 555555, which makes a total of 15,625 combinations (111111, 111112, 111113, etc through to 555555), or 5^6.

By combining their scores for individual answers into a single variable, we end up with a per-person score, where each digit in the variable represents their chosen response to each question. 

So for example, if a person answers question 1 with a rating of 2, question 2 a rating of 10, question 3 a rating of 7, question 4 a rating of 10, question 5 a rating of 6 and question 6 a rating of 1, their overall segment score would be 154531.

We store this in an eVar and a persistent s.prop, and pass them to SiteCatalyst.

Results

What we end up with is a list of scores in a SiteCatalyst report as follows:

Not a lot of use, except that we can see the raw scores and the number of instances of each score.  You can see as well that they’ve started to cluster.  50 people have been through the tool and selected 141515 as their answers to each question.

Using SAINT classifications, we then created every possible combination and classified every result.  Despite it being 15,625 combinations, this only took a few minutes – gotta love Excel for this.

We’ve classified each digit location as the question, and each digit value as the score from 1 to 5.

For example, the classification for digit location 1 is “Indoors (1) or Outdoors (5)”.  The values range from 1 to 5.

Our classification file looks like:

We set “No FOYL” as the default for people who haven’t been through the tool yet – which also allows us to use T&T to engage them into the tool.

Using the classifications, we can now see a report that shows all responses to digit location 1…i.e. how did people answer that question:

So we can see that most people are answering question 1 with a segment rating of 3 (mid way).  Unfortunately, we can’t order the values numerically, they are ordered highest to lowest on their instances.

If we chart that as a pie chart, we see:

Now we repeat that for every question asked and pop it into a dashboard for an overview of results, and we can see:

We can also now sub-relate those individual segments with the courses they’ve viewed:

Our true measure by segment is lead generation, so we just add in those conversion metrics too, Lead Start and Lead Complete and we can see conversion by segment.

Discover

Using Discover, we can create those segments individually and compare them.  Here I’ve compared search terms against anyone that answered the Behind Scenes/Lead a team or Indoors/Outdoors with a 5:

The same segment can be used in SiteCatalyst 15 to segment your reports.

We also pass these segments into our email marketing platform so we can segment our ongoing communications further, generating a more personalised and relevant experience for the user.  We can now customise parts of the message dynamically, based on the segment (or segments) that they told us they’re in.

Test & Target

Now the really useful stuff.

Because Test & Target can also see those segments and values, we can target content to each segment as they re-engage across our content.  For example, we can vary messaging to people who prefer to work outdoors instead of indoors.

We can target course content to them based on their selections.

We can build multiple targets and combination targets to get the content to them.  If they haven’t been through the tool, we’d also know that too, and we can target messaging to try to engage them that way.

In summary

Every company has the opportunity to segment. And that’s how you move beyond reporting to insight.  Understanding how your segments interact with your content or how they convert differently allows you to further optimise their experience. 

There’s plenty of opportunity to segment for measurement purposes, but when you combine Test & Target with those segments, and start to target information based on those segments, it’s really powerful.

When you move away from just reporting, to segmentation, and beyond to segmentation based on user behaviour, you can really start to generate user engagement and relevance of content.

You just need to open your mind to the segmentation possibilities and find out what works for you.

Let me know how you’re segmenting your site visitors and what you’re doing around optimising their user journey based on their segments.

In many cases homepages are either relatively static, or promotional driven.  The problem is that homepages are often still the starting point of a users journey on the site and not every user should see the same content.

In our case, at Murdoch University, despite the fact that we use SEO and SEM tactics to drive clicks to deeper content, we know from our metrics that many users either bookmark our homepage, or search for “Murdoch University” or a derivative thereof, which means that they click through to our homepage.

The role of the homepage is to channel users into areas of the site as quickly as possible.  With many different audience groups, numerous campaigns, and many stakeholders, real estate is highly sought after.  So it’s crucial that we are able to address content relevance – make the content on the homepage as relevant to what the user is looking for when they visit, because relevance yields greater conversion.

Enter Omniture Test and Target.

Using Test and Target, we’re able to easily modify the content displayed on the homepage (and many other areas of the site), by using their sophisticated behavioral targeting technology, thereby making our content far more relevant to users when they visit, with the goal of optimizing their experience, and ultimately leading to more sales.

For example, if a user has previously been to our site, started an application but not yet completed it, why point them to the content that talks about how to apply, when you can prompt them to complete their application.

Likewise, if they have expressed an interest in a certain category of information, get them back into that stream with as few clicks as possible.

As Test and Target stores a profile of an individuals browsing activity on our site, coupled with SiteCatalyst data, and various parameters that we set on specific events throughout our site, we can use that as a kind of category or activity affinity, and alter content display based on those parameters, on a user by user basis.

For example, our homepage has 4 key modules on it.  Currently the two left modules are T&T driven.  The left hand module is promotional based.  So we’re currently running a Postgraduate promotion.  If the user has been to a PostGraduate course previously, they will see the PostGraduate promotion.  If they have been to an Undergraduate course, they’ll see our Course Search module.

For the next module, it shows either a Future Students module (default), a Domestic Students module, or an International Students module.  If the user has been into the Domestic Students section, then in the future, when they see the homepage, they’ll see the Domestic module.  Likewise, if they are an International visitor, or they visit the International section, when they see the homepage, they’ll see the International module.  If we know nothing about them, i.e. they are within Australia but haven’t been to the site before, they’ll see the Future Students module.

Implementing Test and Target in this manner is very easy.  There are a number of key parts:

1. Put the code onto the page in the area where you want to display the content – this code is called an mbox.  It’s a combination of a <div> tag and a piece of javascript.
2. Put some default content inside the <div> tag.  This is displayed is nothing else can be displayed.  For us, the Course Search and the Future Students content is default.
3. Put mbox code onto other key pages and set specific parameters within those mboxes.  I call these “listeners” – they don’t actually display any content, they just pass critical information into the user profile when they are activated, such as on key pages, or key events like starting or completing an application (see below).
4. Put some alternative content into Test and Target (called offers).
5. Create targeted campaigns in Test and Target with rules about what content should be displayed (either default or the content created in step 4).
6. Activate the campaign and hey presto – behavioural targeting now works for you.

Ok, well, there might be a bit missing, like your content and targeting strategy, but you get the drift.

Category and Activity Affinity

Throughout our site, we pass a number of parameters through the “listener” mboxes, which are placed on strategic pages.  For example, in the above Domestic and International sections of the site, we have created a parameter called profile.sitesection and we pass in a value of either Domestic or International.  We can also pass in other values, such as “Research”, “Library”, “Student” or “Staff” on other pages, so that we can expand on the targeted content.  The strategy largely depends upon who

Our T&T campaign that we created in step 5 then looks for either of those values and depending on the value, displays the relevant content.  If it doesn’t find a value, it serves the default content – the Future Students content.

Another area that we utilize this is within our online application system.  We pass in a value when they start an application and another when they complete an application.  This allows us to look for those values, and depending upon a combination of them, we know the status of an application (not started, started not completed, started and completed) and can then display relevant content to either engage them to start an app, prompt them to complete an app, or other default content.

Within T&T we can also set conversion events.  These events are mostly used when you are testing different variations of promotions (I’ll post an entry about testing content in the near future) to see which one drives the best result.  For behavioral targeting we’ve found that we use these more for general reporting, rather than actual optimization.

Practical usage ideas

Content relevance is effective for every industry vertical.  For example:

• ISP’s could use it to target content to customers with accounts, versus potential new customers.
• Banks could use it to target product category affinity – those that might have expressed an interest in mortgages versus credit cards.
• Retailers could use it to target product category affinity too – if a user expresses an interest in TV, show them TV products on the homepage over other products.
• Tourism operators could target by region of interest or by activity, such as kayaking, or fishing.
• Media companies could use it to display news from a certain category, for example, finance news over fashion news.

One of the most successful strategies is re-engagement; get an abandoned user back into a process quickly.  You know they’ve abandoned; they’re back on your site; re-engage them and try to convert them.

Like everything though, it’s important that the content strategy is thought through first and the objectives and success measures are clearly defined, before trying to implement.