In 2023, everyone is being challenged to do more with less. Budget cuts and external pressures have caused nearly half of U.S. brands to freeze or reduce media spending in the past year, but still, the demands for results put on marketers have never been greater. In response, many brands and publishers are seeking new strategies, like ad automation, to increase efficiencies and move ahead of the competition without increasing ad spend.
For Jay Kulkarni, CEO and founder of Theorem, a digital marketing solutions provider, the answer is clear. Kulkarni is one of a growing number of thought leaders encouraging the use of advertising optimization and automation as a way to modernize the traditional ad revenue model. Rather than relying on highly-manual processes and workflows, he believes brands should look at ad automation as a way to speed up order cycles and decrease errors.
“Efficiency has always been a good thing. Recession or not,” Kulkarni says. “When you increase efficiencies, brands can produce more campaigns. When you produce more campaigns, you generate more revenue. It becomes a win-win situation as advertisers appreciate the maximized brand exposure, and publishers appreciate the incremental revenue.”
Automating traditional advertising models reduces order-to-cash process time, which means invoicing can happen more quickly. Faster revenue reconciliation means more cash-positive businesses and a faster ability for businesses to reinvest in new initiatives.
“The processes that are used to execute the production side of the business in brands that are leaning into ad monetization strategies rely heavily on manual interventions and outputs which almost always stifles the ability to scale,” Kulkarni says. “Teams can only manually output so much, and leaving the operational workloads on teams and individuals takes up a majority of their time and attention. This precludes them from focusing on bigger-picture elements of the business, like new ad monetization strategies and optimizations or enhanced account management solutions.”
To implement automation into their advertising routines, brands and publishers need to understand what elements of their existing processes are the most feasible for automation, and then transition those elements to be managed by AI or machine learning technology. That allows for scale, faster revenue recognition cycles, and optimization of current offerings to expand monetization strategies.
One way the ad industry is putting AI to work is by using generative AI tools, like ChatGPT and DALL-E. Not only can AI bots mimic human workflows in media campaigns, but they can also tap into first-party data from customers to generate insights in real-time. Already, brands are using AI to automate digital advertising’s “dirty jobs” — things like manually entering the same information across multiple platforms or typing information into a CRM or publisher ad serving system.
Kulkarni believes it won’t be long before AI and ad automation become a natural part of the MarTech stack.
Kulkarni says AI could soon be utilized for ideation as well as content moderation. He says the utilization of automation tools to pull data sources into a CDP, analyze data, and implement hyper-personalization strategies will be critical to business growth.
“Too much time is spent on manually inputting data and it’s leading to errors that are ultimately hurting a business’s bottom line. Automation can curb those errors and free time for employees to focus on their own professional development and have a bigger contribution to their overall company,” Kulkarni says. “As we see more and more teams leveraging AI tools within their strategies we will see automation and AI play significant roles in marketing and campaign strategy and execution.”