Don Springer and Evan Hanlon Discuss How Big Players Use Social Data to Drive Performance through Programmatic Ad Spend
What is Programmatic advertising?
Programmatic is meant to enable an advertiser to bring as much data from the buy side and the sell side to make the best buying decision possible. This includes determining things like what amount should be paid for an impression, which channel should the ad be placed in, what type of content is delivering ROI, etc. What makes programmatic unique is that the buy and sell pieces of technology can communicate and make decisions without human judgement, allowing for the automation of media buying.
Programmatic advertising starts with data, then layers in insights, analytics, and finally targeting actions. There are a growing number of data scientists focused on getting insights to convert into action to inform audience identification. As a brand, if you know your consumer well—where they are in terms of life stages, what their interests are, etc.—the better you can target them with a relevant and compelling offer.
There are a couple the challenges in this area: in the current model of media buying, the pieces are as connected as you’d expect them to be; to date, programmatic has thrived on the idea of efficiency and tactical execution; and it is still hard to accurately measure ROI and determine exactly which data sets are valuable in which campaigns.
— mark a. chaves (@MarkAChaves) June 16, 2015
Brands also don’t always know what they want to do with data, but they recognize the power of data, and specifically social data. The enthusiasm to know more is there, but the ability to structure campaigns isn’t, so having tool providers to consult and educate brand customers about what is possible is an important piece.
Perhaps the most controversial challenge to programmatic is the ability for these systems to make decisions based on data that is considered personally identifiable to a user. Some view this data as private, some view it as public, and many companies (especially start-ups) are operating comfortably in the grey area. As Don said, the genie is going to be tough to put back in the bottle as an entire industry is using this data. Regardless of the stance on using this data a challenge exists right now to use this data for a unified view of a consumer.