The big "opportunity" (problem!) in online display advertising is aggregating empirical data showing display's benefits. For now, they are fairly hidden. Yet, the inevitable success of the ad exchange model and evolution into premium inventory trading depends, in part, on better engagement analytics with standards for attribution.
Can you build the analytics platform of the future which includes measured attribution of campaign elements (display ads, search, a billboard on the NJ Thruway) that leads to a final conversion whether online or offline?
Currently, value of display is being established using the lowest common denominator of metrics - did the last click from the display unit cause a conversion or not? Often, not. Though, as we discussed recently in regards to Adgregate Markets, technologists are bringing the store to the media unit in hopes of improving conversion ratios.
But, the reality is that the entire engagement of the user - from display on the Long Tail to search - is not easily linked to any one conversion as each stage of the purchase funnel produces an effect. Display creates interest. Search fulfills the demand, post-interest.
Here's the marketing material from Atlas:
"Engagement loops are a powerful concept all by themselves, and they can help you to make improvements to your product or service in order to optimize the drivers of growth for your business. But I think the value in this framework is that it can help make overall business decisions that require thinking about the whole rather than just one of the parts."
In this case, Ries, a programmer by trade, is recognizing the same need that advertisers, technologists and publishers of the exchange want solved: show the unique benefit of any one element of a campaign.
Here's Ries' presentation:
Ries goes so far as to provide a formula and metrics for attribution with his "engagement loop" theory.
In the comments of one of Ries' posts, Jesse Farmer writes that he's been using loop measurements, but that an engagement can still be lost and the math which Ries proposes to quantify engagement gets murky.
As an example, Farmer suggests a teenager who visits on a social media site may not click through on an email (even though it's the key driver of action), but skip the click and go straight to the social media site. The attributable benefit of the email gets lost in the data.
The same challenge holds true, of course, with the bridge to attributing offline actions from online campaigns. Frenemies WPP and Google recently announced a partnership that will award 11 grants "to MIT and Harvard to support research into how online media influences consumer behavior, attitudes and decision making." One must wonder - how is anyone going to really know that a campaign had, say, a 15% positive affect in encouraging the consumer to go to the store and buy a new powersaw.
Let's hope the brainiacs over at MIT and Harvard can figure it out at least in part. To be sure, they are not alone in pursuing this Holy Grail of analytics: the appropriate attribution of all parts of a campaign to a specific goal.
It would appear that analytics will stairstep in its effectiveness for showing ROI for each campaign element and that a more precise form of engagement mapping and loops is still a ways away.
Sooner the better for display ad exchanges.