Richard Sadowsky was named Acting CEO of Voltari in December 2013 after a year serving as Chief Administrative Officer and General Counsel. Previously Richard was a Partner at SNR Denton US LLP in New York City, where he specialized in corporate and securities law. This is his first contribution to The Makegood. Look for his post the fourth Wednesday of every month.
The numbers continue to indicate the growing stock marketers place in mobile. In a new report on the mobile ad market, Gartner said it expects global mobile advertising spending to reach $18 billion this year, up from the estimated $13.1 billion in 2013. It’s projecting the market will rise by 2017 to $41.9 billion. We may debate what to spend, which vendors to use, or which additional media we should leverage within the mix—but mobile has a growing place in our plans and our budgets, which begs the question: why so much waste? Mobile advertising placement is largely scattershot. It’s as if many of us are 21st century John Wanamakers, content to say “Half the money I spend on [mobile] advertising is wasted, the trouble is I don’t know which half.” The shame is that, in mobile, the science and the systems exist to greatly limit waste.
If you are running a mobile campaign without a data driven strategy, you are wasting impressions. If you are operating without a plan for audience targeting and discovery, or not using predictive modeling to determine which audiences will yield the best return—more waste. If you are not utilizing the systems and talent available to you to test, learn and optimize – well, you get the picture: waste. Imagine how much the marketplace and our collective return on investment would grow if we were so efficient that we gained enough spending confidence to scale without being encumbered by the fear of wasting money.
Targeting and Expanding Your Mobile Audience
Cross-platform digital marketers will find that targeted buying and reporting systems now available for mobile allow them to use consumer, content, location and device data to predict the right audience for a brand’s advertisement and then improve the predictions quickly and automatically based on user actions. This ability to start with better, deeper targeting, immediately puts the buyer at a more precise starting point. Real-time machine-learning capabilities can continually improve upon those starting efforts through the life of the campaign. These machine-learning capabilities allow the marketer or her agency to test, identify and optimize the best audience segments, often uncovering additional productive audiences in the process. This approach helps expand the universe of consumer opportunity efficiently and effectively. This quality of planning and execution of mobile advertising reduces the cost of customer acquisition and ultimately allows a brand to target at scale.
The Power of Predictive Modeling
Let’s take a closer look at what’s happening with targeting and prediction within the marketing or media plan. Marketers and their media planners have relied only on basic demographics and publisher reported stats to figure out where their audiences are which allows only for best guesses on how a consumer might respond to any given ad. Now, we can go beyond demographics and, segments, to focus on behavior as well as apply more sophisticated data within a dynamic environment at the moment of engagement. Able to model our best, most productive potential audiences based on data points across time, location, device and content, and real behavior vs. just presumed characteristics, we are by definition at a more efficient place.
Still, too many marketers settle for basic targeting and barely educated guesswork based on broad demo descriptors: age, income, education and the like. Is this because they don’t know that the capability for using models to predict outcome more precisely is there for mobile? If we spend the time to model audience based on the quality behavioral and pathway data available to us, we can be tighter and more assured on our execution with our audiences, more quickly in a position to optimize, with little time and spend wasted in the meantime.
Our perspective opens, now that mobile offers the opportunity to apply, learn from, and reapply big data—and the optimization options expand. The combined power of today’s predictive analytics, high-speed tech and mobile systems allow for on-the-spot consumer engagement optimization. This also has implications for more precise and continually improved localization and personalization of mobile advertising, boosting the potential for resonance and consumer engagement.
The arrival of data driven strategies and options to mobile has been a welcome evolution for the efficiency-minded marketer. And for those of you who are spending on mobile but not yet doing all that you can too assure efficiency, the timing is good. There is no need in this evolved (and still evolving) environment to waste impressions. The options for audience targeting and discovery, predictive modeling and optimization are available. If you think we are seeing growth now—imagine the scale when we’ve all equipped ourselves for efficiency in these areas.