How To Create Hands-Off Display Campaigns That Deliver Results
There are literally billions of possible combinations an advertiser can choose from to reach a target user, but not every company has the time nor the resources to take a trial-and-error approach to campaign strategy. For instance, what combination of audience demographics, gender, geographic location, occupation, interests and age range will deliver the most impressions? And, […]
There are literally billions of possible combinations an advertiser can choose from to reach a target user, but not every company has the time nor the resources to take a trial-and-error approach to campaign strategy.
For instance, what combination of audience demographics, gender, geographic location, occupation, interests and age range will deliver the most impressions? And, what type of ad works best for a specific target audience?
Even for large enterprises, attempting to answer questions like these is not a smart way to maximize the returns on deep investments in advertising. Relinquishing some control to machine-learning technologies that adapt and evolve over time not only delivers stronger results, but does so more quickly and with fewer wasted dollars than campaigns that lack the help of artificial intelligence (AI).
What Advertisers Need To Know About AI
Most of the numbers around digital advertising are going in one direction: up. Analysts expect digital ad spending to hit $50 billion in the U.S. this year — and real-time bidding (RTB) for digital display ad spending is expected to “see double-digit growth rates through 2017, when it will total $8.5 billion.”
However, all that growth is cause for concern when we consider other statistics, namely those related to awareness about automated, intelligent technologies.
For example, Forrester recently found that only 23 percent of marketers it surveyed understand programmatic buying, which lifts the burden of manually sifting through data to create the most effective campaigns.
Ad fraud is making this an even more pressing issue; more than 30 percent of today’s ad impressions are assumed to be fraudulent, according to MdotLabs. That means that human-driven ad campaign strategy is not only time-consuming and difficult, it’s also a drain on financial resources.
AI, by contrast, automates decision-making based on the most relevant data, and it learns as that data changes. Advertisers can use this self-learning technology to rapidly respond to changing market conditions, embrace what’s working and kill what’s not.
While the human team establishes campaign and target goals related to age, gender, location and other parameters, the technology processes the massive amount of data required to optimize efforts. It makes it easy to spot poorly performing campaigns so they can be fixed, as well as to identify successful campaigns that deserve to be scaled up.
AI For Defense Against Fraud
AI can solve many other problems standing between ad teams and successful campaigns. The fraud threat is a prime example. Perpetrators are constantly shifting their tactics, and they’ve become skilled at committing acts on such a small scale that their actions often go undetected, particularly by brands that have yet to embrace machine learning.
Fraudsters are pulling in billions of dollars each year, and they’re siphoning ROI from ad campaigns.
AI solutions attack this problem by analyzing the data, piecing together fraudulent traffic from multiple locations, and continually learning and improving based on past and current experiences. The work that machines do on the fraud front can’t realistically be replicated by human professionals. The task requires analysis of impressions, clicks, conversions, behavior based on IP addresses, relevant customer data, and many other factors.
Once AI tools spot the fraud, the system will automatically blacklist those sites as well as similar sources so that advertisers don’t spend another dime on them.
A Partnership Between AI & Ad Teams
Consider the example of a large European game development company that began using programmatic advertising on a small scale to test the waters. Within six months, the initial campaign resulted in 1,000 percent more leads with minimal work on the part of the company’s marketing team.
Over time, the company gradually increased its programmatic ad spend and now allots more than half of its advertising budget to programmatic campaigns. As an added benefit, the incidence of ad fraud has declined far below the industry average of 30 percent to less than one percent of the overall budget.
Machine-learning technologies are not about to put advertising professionals out of work. On the contrary, AI works alongside human teams to remove the data burden from their shoulders allowing them to focus on other priorities that AI can’t perform, such as creative and content.
AI tools give advertisers the information they need to shape effective campaign strategy and shift tactics to maximize results. By speeding the data analysis process, AI helps brands spot ad fraud before it undercuts campaigns, evaluate key performance indicators and make adjustments on the fly.
And, through continuous learning, AI technology is constantly building on what’s working to identify new opportunities and deliver campaign conversion rates that will wow stakeholders.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.