• by Sarah Fay , October 28, 2016

There’s no arguing that programmatic audience targeting has resulted in chunky returns for advertisers and agencies across the industry.  

According to eMarketer, programmatic media buying has grown to account for 73% of digital media budgets (or $25.23 billion) in 2016.  Say what you will about the quality of experience this delivers to viewers, the reason for the wholesale shift in the buying approach is that even the most rudimentary of programmatic buying platforms typically produce around a 20% improvement on measured returns such as online sales or leads.  

There is yet another jump in results when AI is part of the programmatic technology used.  Choosing a superior algorithm can make a big difference in targeting accuracy and man-hours spent on campaign setup and inputting data.   Advertisers looking to squeeze even better results from their audience targeting programs are tweaking and improving their technology choices to deliver incremental returns.

The big question is, where does an advertiser turn for the next mother lode of ROI improvement?

Evidence would suggest that the secret to unlocking another avalanche of returns lies in the application of data and automation (brought to you via AI and machine learning technology) to the content and creative side of the equation.  

In my experience working with companies that use data to optimize creative experiences, the ROI improvements are not small.  In fact, if done right, there is at least another 20% percent to go after, and engagement metrics of sub-1% click rates on standard impressions can skyrocket to the high single digits.  That’s right: instead of .3, we are conceivably talking 3, 5 even double-digit percent engagement metrics.  Of course, the operative phrase is “if done right,” and that is tricky, especially given the way skill sets are siloed in the advertising industry today, and the fact that creative decisions are preciously guarded.  

Most digital advertisers have experience with A/B testing of two or possibly a handful of creative versions, but programmatic creative, or to use the more technical term, “dynamic creative optimization” (DCO) often involves leveraging hundreds of creative images or versions, using decision trees, and contextual signals such as weather, time of day, publisher, location and other variables to marry audience data with appropriate images and messages. 

The reality is that few advertising teams today have mastered this, even though the technology is available.  The devil is in the details of execution, which requires working across silos, and applying a whole new level of strategic planning and data measurement.  However, those who are doing it now are being rewarded significantly with improved campaign results…and more importantly are getting a head start in creating processes and developing the skill sets needed for DCO campaign executions. 

A CEO I know well (because I sit on Celtra’s board), Miha Mikek,  says, “The key is to marry the reams of data now available with dynamically relevant creative, providing a cohesive narrative that engages the user while promoting the brand’s objectives.

Echoing this thought at the ANA Masters conference, P&G’s Marc Pritchard said, “We spend too much time fiddling with measurement while consumers block our ads. We should work on ad quality."  

Creative and image relevance is a big step toward ad quality, and AI will play an important role.   Advertisers need to tackle structural and skill set needs in order to crack this.  Now is the time to address the challenges of bringing data and creative together — or you will be missing out on the next mother lode of returns.