Many businesses outside of the traditional media category are turning to advertising as an additional revenue stream. There is good reason for this shift. Advertising is a high margin business that has the potential to increase revenue substantially. What can sometimes be overlooked is the complexity of the ad ops process, which if not done well, could hamper a brands ability to maximize the revenue potential of their ad monetization strategies. Highly manual processes and workflows that hinder productivity, increase probability for errors, slow order cycles and impede on revenue growth can all derail the ad ops process, which generally speaking is the process that a business goes through to actually receive payment for ads bought on its properties.
As advertising becomes a key revenue model for publishers and brands alike, the need to refine and improve the ad ops process will continue to grow. Automation has played a key role in ad monetization for publishers, and it is becoming increasingly critical in terms of increasing efficiencies and improving accuracy.
The Benefits of AI in Ad Monetization
AI utilization has already started having a significant impact on ROI and ad monetization processes in the media and entertainment industry as it enables brands utilizing ad monetization strategies to optimize campaign execution, improve their client management capabilities, speed up revenue recognition cycles and retain and attract talent.
By identifying areas where media brands utilizing ad monetization can reduce tedious hands to keyboard tasks within their current ad ops processes, it allows them to enhance agility and move faster. With AI, content brands like publishers, retail media networks and streaming networks are able to run campaigns seamlessly and provide clear and timely reporting to their clients. This reporting enables advertisers to get real-time feedback on ads and update them as needed, thus improving overall performance. AI utilization also enables better decision-making. It can help increase the speed and accuracy of data analysis to help brands and advertisers have a better understanding of the story the data is telling them and how to use the data to optimize performance.
Leveraging AI enables publishing, media and streaming companies to enhance the service offerings they provide to clients. The ability to provide more robust, white-glove services demonstrates that these content brands value their relationships with their partners. Additionally, AI frees up a lot of time for teams executing these ad monetization strategies, providing them with more bandwidth to have more strategic conversations with partners that can center around other areas for partnership enrichment and business growth, such as new ad formats, audience targeting strategies and enhanced product offerings. In the same vein, ad ops and client service teams will have more bandwidth to increase strategic insights and drive incremental investment from existing client accounts. All of this ultimately leads to happier, more loyal clients that stay for the long haul.
Optimization Opportunities
One of the largest areas for improvement within the current manually driven ad ops workflow is the high level of high error rates. When teams are tasked with inputting high volumes of repetitive data, the rate of human based error is typically high. And the repetitive, mundane nature of inputting data isn’t challenging or stimulating, creating boredom and exhaustion which ultimately drives teams to be less engaged in their work and more likely to make mistakes. In addition, human driven workflows lack consistency in process, language and solutions deployed across teams. It’s human nature to do things differently, using methods that best suit an individual team member’s way of working and mean it is challenging to get everyone on the same page when it comes to processes and the way in which the team works as a whole. This is where AI and automation come into play.
All of the previously mentioned elements are contributing factors to a less than ideal workflow that needs optimization. AI has advanced enough to offset these repetitive tasks, allowing teams to utilize their time to focus on higher level functions such as strategy and leave the mundane tasks to the bots.
Things to Consider when Implementing AI
The automation software industry is not a neatly defined space. New technologies and tools are created and exist for a variety of purposes and there is no one-size-fits-all approach. As a result, it’s important for those looking to implement AI into their strategies to keep a few things in mind.
The first thing to consider is that automation implementation often has a longer lead and it can take some time before brands start to see a return on their investment. However, AI and automation integrations are long term investments that will increase productivity, enable growth at scale and ensure faster revenue streams for years to come. When starting off, keep track of success metrics such as time saved and productivity improvements with teams freed up for other tasks.
AI software is complex and the idea of implementation can feel overwhelming. It is critical to have key team members that have a robust knowledge of the tool, the company’s needs and business goals to evangelize across the organization. Focus on education and rolling AI out slowly so that any bumps in the road can be addressed immediately. Professional development efforts focused specifically around training teams on AI can be a great investment that will lead to improved efficiencies and happier, more fulfilled teams who feel empowered to learn new skills and grow professionally.
When embarking on an automation implementation journey, the right positioning with your teams is critical. Oftentimes the mention of AI creates unease with employees, resulting in a feeling that “robots are coming for my job“ or “I am going to be replaced by technology” and understandably so. The truth of the matter is, AI is nowhere near the point that it is capable of replacing humans, as it lacks the distinct ability of creativity that only humans can bring to the table. In fact, recent research suggests that a nine-month old baby is more intelligent than the most intelligent form of AI. AI is a tool that is becoming more integrated into workflows and just as with any other tool, it is important for teams to learn it and use it professionally to enhance their capabilities.
With the current landscape of the advertising industry constantly evolving, it is clear that AI is going to play a pivotal role in ad monetization. The publishers and advertisers who implement AI to increase efficiencies and improve overall service are going to position themselves to be leaps and bounds ahead of those that don’t. If they choose to not go the way of AI and automation, they are going to be left behind.
Happy clients and campaign success leads to reinvestment in the company and continued partnership, which helps advertising brands stay competitive in the market. A big part of that competitive edge lies in their internal teams capabilities and investing in top talent. Talent is expensive to recruit and train and oftentimes, in a non-efficient ad ops team, it is the top talent that are most relied on and most prone to burnout. With scalability tools like automation and AI, top talent has more time and energy to focus on higher value work. In an optimized state, each team member has at least an 80/20 ratio for core task based work versus project based. Having this ratio can ultimately lead to career growth for top talent which creates increased ROI and enhanced revenue streams for the business.
