Hot off the heels of our Trupath measurement announcement, today we’re covering measurement in more depth.
A bit of background, digital media is the only channel where the advertiser has presence at the point of delivery and where the advertiser actually participates in the advertising process rather than handing it off to a media provider. It should therefore be regarded as strategic and considered carefully by both agencies and advertisers. For context, it is important to understand the full needs of the advertiser and the scope of measurement and data integration before fully embracing any model. Against this strategic context, we are then better able to determine the viability of the models in the market place.
Distributed use of First-Party Cookies overcomes many of the core limitations in the current third-party model regarding deletion, audience measurement and authenticated multi-device association as well as utilization of audience variables at point of delivery. Stability and user volumes are also impressively high. However while the framework is a substantial improvement on the traditional third party framework it also requires integration of alternate measurement techniques to provide a fully validated model to support all media, marketing and channel analytics effectively.
While the traditional third-party measurement model continues to degrade the incremental models also require careful review as their ability to be scalable, secure, provide adequate privacy control and align to targeting needs to be carefully understood.
Ubiquity: Advertisers need a measurement standard that can be deployed universally across media. However, a centralized model in a predominantly sell-side company seems a stretch. It could push us back to the days of having 25 different measurement models. The pro here is of course having effective consolidation across the Chrome environment will alleviate some of the impacts.
Server-Side vs Client Side: is also an important part of this discussion. The model where the data is pushed from the browser and re-compiled at the server side creates some very interesting scenarios. It is outside any user control, but it is also outside that view. This is not unique to this model as Locally Shared Objects (LSO) and, to some extent, finger-printing have similar characteristics; however, the sheer concentration of data and the depth of associations will be of concern to regulators and consumer advocacy groups.
Constructive vs Constrictive: It has become clear over the last few comparative/competitive analysis exercises that the real meat in the data requires a lot of granular management to get to really actionable and meaningful information.
The embracing of the merely obvious actually constricts the advertiser and agency’s ability to deliver a differentiated and competitively advantaged solution: Hey Coca-Cola we’re going to pool your data with the same data as Pepsi and you can have the same vanilla reporting! Procter & Gamble, we’re going to level the playing field so your competitors are equally advantaged or disadvantaged as you, even though you spend $XX more!
Pooled Targeting: A further extension of the point above is that this opens up pooled targeting and proliferation of user behavioral data: your best customer just became your competitor’s best prospect.
New Blocking Techniques: The delays in Firefox 24 point to another area of significant risk in a server-side model, namely, a very proactive consumer advocacy group in the browser community. The proposed Stanford revisions identify cookie-level controls that could significantly disrupt this type of model (excluding Android and Chrome).
The association on measurement and its near neighbor targeting also has impact on an advertiser’s overall data strategies. The increased investment and management of all media data by advertisers is becoming a significant reality. Measurement is not confined to media consumption elements but is increasingly being integrated across channels and linked to brick and mortar. That’s why it’s so important to have the very best measurement based on the very strongest data.
This good data in, good data out philosophy was the guiding force for our development of Trupath. Using millions of data points spread among several big data clients, the initiative made us think of measurement in a next level manner. Building on our understanding of data and our First Party technology, we took Trupath to an evolutionary place.