Tracking Search Epiphenomena: Why This Behavioral Anomaly is Vital in Web Analytics
I’m pretty sure that with this title I’m going to skyrocket to the number one position on Google — for “epiphenomena.” Laugh while you can, because epiphenoma are actually vitally important for understanding web analytics — especially if you are an ad-based web site or, indeed, any website that doesn’t have clear and obvious conversions.
In my last installment, I described a host of problems that beset sites when trying to use conversion as THE metric of choice. But no matter how many issues surround tracking conversion, it’s still a lot easier to deal with and more powerful than anything available to sites that don’t have conversions as a convenient yardstick for success.
For ad-based sites, there is no such thing a single conversion event. When a page is a billboard (banner-based), then the publisher most often makes money on a per-impression basis. In this model, page views are the primary driver of revenue. But there is a secondary dynamic — the more visitors click through on the banner, the more likely advertisers are to be happy. Yet every visitor that clicks through on a banner is lost to the site — so no further impressions get recorded (in that session). In other words, there is a built-in conflict in the basic site goals — the publisher benefits most from impressions without click-throughs but the advertiser benefits most from click-throughs.
For content-network ads placed on a pay-per-click basis, the publisher only makes money on the click-out. That’s straightforward enough, but if a site mixes banners and PPC links, then PPC links have the same impression loss that banners do.
The more engaging content is and the more tantalizing navigational paths that a publisher provides, the more page views (and impressions) a site will drive. But engaging content and tantalizing navigational paths may reduce click-outs — making advertising less successful and therefore less profitable.
So unlike ecommerce sites, ad-based sites have to struggle with a conversion event that is amorphous (revenue is driven by every page view) and contains within it at least two contradictory impulses (maximize views vs. maximize click-throughs).
For the most part, publishers resolve these conflicts sensibly enough by focusing on driving as much consumption of content as possible while selling to appropriate advertisers and letting “nature” take its course.
There is, of course, a usability tradeoff when it comes to maximizing impressions. The quicker and faster a visitor finds the content they are looking for, the better for them and the higher their likelihood of return. But the quicker and faster they arrive at a destination, the less impression opportunities they provide.
Most ad-based publishers recognize that optimizing the site experience — even at the expense of short-run impression maximization — is almost always the right strategy.
But here is where epiphenomena come in. Because in trying to figure out how valuable content areas are, publishers (or their web analysts) often completely miss the boat.
So just what are epiphenomena? Epiphenomena are effects or symptoms which are associated with a deeper effect or condition, but are not the primary cause of this condition. For example, rich people drive more BMW’s than poor people. But driving a BMW isn’t what makes you rich (quite the contrary)! Catholics are more likely to have a Christmas tree in their living room during the winter holidays. This doesn’t mean that setting up a Christmas tree in somebody’s living room will make them a Catholic.
On a website, epiphenomenal effects are incredibly common and surprisingly easy to misunderstand. In one recent analysis, we at SEMphonic segmented visitors based on whether or not they viewed some specific content areas on the site. The goal was to understand how effective these site areas were in driving same session impressions, overtime visits and lifetime impressions.
Reader Comments.
Many electronic publishers (and transactional sites) are struggling to address the issues that you outline, namely:
-Which statistics matter?
-Where are the actual “choices” occuring that can be affected?
-Given competing objectives (page views vs click revenue), how do I optimize?
There is a suprisingly simple answer to these questions. Properly structured tests enable the publisher to;
-Focus attention on the communication attributes that actually impact behaviour.
-Quantify the impact across multiple independent objectives.
-Weight the objectives based on their relative value.
This framework empowers a combined exploration, discovery and optimization cycle that can be used to optimize content across page views and revenue for different customer segments.
We are seeing this used with great effect for leading publishers and e-commerce sites to gain new insights and improve month-month results.
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