The birth of the internet shook the foundation of many markets, in particular our own media industry. Over the past decade and a half, online advertising has blossomed into the potential genius alternative to the more traditional media outlets -- wooing media buyers with the promise of "real-time metrics" and the technology that allows better targeting capabilities. It's all these promises of marketing capabilities that has propelled online media into securing a larger slice of overall budgets -- stealing dollars from traditional media bucket. However, in order for online media to surpass traditional media budgets, the industry must further innovate in terms of scale and targeting. This will involve delivering value above and beyond what advertisers are currently capturing from TV.
One of the critical components that will enable the shift in budget allocation is in the one thing that makes online advertising so unique: the underlying power of ad-targeting data. As it currently stands, the digital industry is overrun with data: "Data, data everywhere, but no one has the means to truly leverage it." The digital ad industry is still a ways off from being able to leverage "data potential" from ad context and consumer behavior.
Reid Hoffman, the co-founder of LinkedIn, has often said that "Web 3.0" will center on data. However, if the next era of improving digital media involves "data," then there are going to be a few different phases within this era for us to test and refine. We've peeked into Pandora's Box and have just begun to scratch the surface of what intelligent data can deliver, but we have yet to truly understand the capabilities. Ad targeting has a lot of growing up to do in anticipation of the next phases of the web, and I have outlined a few potential life-stages of data below.
I have no doubt that we are rocketing through phase two, given the proliferation of cloud computing solutions. As a case-in-point, the number of ad firms built on top of Amazon's AWS platform is incredible. Simply put, the capability to access data at fast speeds is now becoming a commodity. However, if we look farther out to phase three, we see things fall off a cliff; the simple fact is that data currently lacks a means to measure quality.
As we know from Google and the retargeting industry respectively, speed and recency are key. However, given all of the different sources from which we gather ad-targeting data, who is keeping track of the quality? A huge opportunity exists in scoring the quality of data from the different sources that created the intent data. For instance, how are we answering questions like, "How many times has the data changed hands if I'm not receiving this data from the source?" I will concede that some of the quality of the underlying data will prove itself out in the performance of the associated ads, but there's tremendous waste in between. However, if we can't immediately solve with scalable data-scoring solutions, then the next best focus should be centered on consumer behavior.