Image credit: patrimonio / 123RF Stock PhotoA while back I published a post on finding ways to monetize testing on media-based and publishing sites. In that post I discussed the basic “page views per visit” engagement metric baked into Test&Target and then briefly touched on the concept of Page Score. In today’s post I want to dive a little deeper into Page Score to address how publishers can leverage this type of metric and when it might make sense.

For context, let me share an example we can all relate to. Let’s suppose you’re trying to gain weight to become a sumo wrestler.  To track your progress you decide to measure the number of meals you eat. Three meals a day isn’t doing it so you step it up to five. Eating five meals a day gets taxing rather quickly so before long you find yourself eating smaller meals with more vegetables and less meat. Soon you notice you are actually losing weight.

You discuss this with your weight-gain coach, who recommends you track the amount of calories you consume, rather than the number of meals eaten. With this approach you are able to eat three high-calorie, but otherwise manageable meals. As your total caloric intake increases, so does your weight and before long you’ll be on your way to sumo stardom. In this rather simple example, measuring meals is akin to measuring page views. While the meals we eat often correspond with weight gain or loss, it is not the meal itself that drives that change. Rather, it is the calories inside that meal that move the scale. In our example calories represent the weight of the page, as captured by page score.

Not All Pages Contain Equal Calories
While the standard “page views per visit” metric excels in its ease of implementation (essentially requiring a uniform global mbox on every page) it assumes all pages have equal value. This may be a good assumption for some, but probably not for most in today’s complex media business. Let’s assume most landings on your site are on the homepage. Let’s also assume that due to the very wide but shallow pool of content on that page, ad impressions sold on the home page have a lower CPM than category pages like “Fashion” or “Sports” where advertisers can place more targeted ads, resulting in a more lucrative CPM. A flat-rate page-views-per-visit metric won’t be able to distinguish between a five-page visit that made up of four home page loads and one category page load and a five-page visit made up of one home page load and four category page loads. All this model sees is five uniform pages. To make matters worse, what if you test a change that makes the home page more difficult to scan so visitors end up back at the home page or load the home page more often because they can’t find the content they want. In this case, page views may be increasing but they aren’t the higher-value page loads you are hoping for. Your business could actually be losing significant money in this scenario!

With a Page Score metric you have the ability to work around this problem. With a Page Score parameter you can tell Test&Target that the homepage is worth 0.1 and the category pages are each worth 0.8. Now you can construct a weighted average.

Setting Page Score
Setting Page Score is easy to do. You simply add a parameter to your mbox call on every page like so:

mboxCreate('global_mbox', 'mboxPageValue=10');

Note that while mbox parameters can take any name, the page score parameter is one of those reserved mbox parameters that must be set exactly like so: “mboxPageValue=VALUE”. This is similar to other reserved parameters like: orderId, orderTotal, and productPurchasedId. The value used would change based on the relative value you would want to assign to the page. For a more comprehensive example, you can read more about it in Test&Target’s help section.

Piggybacking on Your Analytics Classifications
To make this metric effective, you should have a page score assigned and set on every page. Setting the mboxPageValue on every page sounds tedious. It is tedious. It’s the equivalent of manually writing your return address on 200 thank you cards instead of buying a stamp. If you’re like most publishers, you have dozens – if not hundreds of new pages born every day. Fortunately, there is a good chance you already solved this problem when you nailed down your analytics implementation. Hopefully each page has a page type or some sort of classification. This may be the DART zone or the sales category of that page. That may be the axis on which you can assign page score values. If you’re defining the page type variable somewhere on the page, you may be able to dynamically inject logic that assigns the page score based on the page type (home page, article page, category main, etc.)

var pageType= "article"
if(pageType= "home") var pageScore=1;
else if(pageType= "article") var pageScore=4;
else if(pageType= "categoryTop") var pageScore=8;
mboxCreate('global_mbox', 'mboxPageValue=' +pageScore);

Are You Selling Ad Impressions or Ad Inventory?
These two may sound the same, but what I really mean is if your site has a number of unsold ads (pages loading with potential ad impressions that have no buyer), driving up page views isn’t necessarily driving up revenue. A metric like page score may be able to help if you can get clever with your implementation. Specifically, if your sold ad units have a unique class name that differentiates them from unsold ad units, you may be able to employ a script on your page (or a Test&Target profile script) that loops through all the divs on the page and counts the number matching your sold ad unit class. If that number can be set in a JavaScript variable, it could then be dynamically populated into an mboxPageValue parameter. For example:

var divs=document.getElementsByTagName('div');
var adCounter = 0;
adCounter = adcounter + 1;
mboxCreate('global_mbox', 'mboxPageValue=' +adCounter);

Please note that I am not a developer. The code above will probably need tweaking for your use case, but it should get the point across. The result is an mbox on every page that communicates the number of sold ad impressions that appeared on that page. If you have multiple ad sizes or types with differing values, your scripts can get more elaborate to pass in relative values. For example a right-rail ad unit with class “ad_rr_300x260″ may be worth 0.3, but a featured banner ad with class “ad_featured_1000x260″ may be worth 0.7.

Don’t Get Paralyzed by Perfection
I will wrap this up by reiterating what I said in my original blog about monetizing engagement. It is easy to get side tracked with the idea of perfection. If the perfect metric is going to require you to wait a full year to get it implemented – it’s not the perfect metric. Start with something you can get working in the next 4-6 weeks. Anything beyond that is likely to never happen. I have seen test programs stumble and then fail completely because they were paralyzed by perfection.

There are going to be flaws with any monetization model you work out. Pick something that is an improvement from where you are today and work on that. Page Score may be the next level for you. If so, start determining where the calorie-dense pages are on your site and plug this into your monetization model.  As always, reach out to your Adobe Test&Target consultant for more ideas on how you page score can help you.