Digital ads are now a crucial component of business promotion, but they can be even more potent by incorporating automation.
Advertising is becoming a popular additional revenue source for many companies outside the traditional media sector, and a good reason. Advertising is a high-margin industry with significant revenue growth potential. The complexity of the ad operations process, which, if not handled correctly, could hinder a brand’s ability to maximize the revenue potential of its ad monetization strategies. It is something that is occasionally overlooked. Ad ops, generally the process that a business goes through to receive payment for ads purchased on its properties, can be derailed by highly manual processes and workflows that hinder productivity, increase the likelihood of errors, slow order cycles, and impede revenue growth.
The need to hone and enhance the ad ops process will keep expanding as advertising becomes a crucial revenue model for organizations and brands.
Brands’ ability to monetize their advertising through automation has been essential, and it will continue to be so as efficiency and accuracy rise.
The digital marketing and advertising field has much room for automation. Digital ads are now a crucial component of business promotion, but they can be even more potent by incorporating automation.
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AI’s Advantages in Ad Monetization
The use of AI has already begun to have a significant impact on ROI and ad monetization processes in the media and entertainment industry. This is because it enables brands to use ad monetization strategies to optimize campaign execution, improve client management capabilities, speed up revenue recognition cycles, and retain and attract talent.
Media brands that use ad monetization can improve agility and move more quickly by identifying areas where laborious manual tasks can be converted to keyboard-only operations within their current ad ops processes. With the help of AI, content brands like publishers, retail media networks, and streaming networks can easily run campaigns and give their clients accurate and timely reporting. With this reporting, advertisers can update their ads based on real-time user feedback, enhancing overall performance. Making better decisions is also made possible by the use of AI. To help brands and advertisers better understand the story the data is telling them and how to use it to optimize performance, it can help increase the accuracy of data analysis.
Companies in the publishing, media, and streaming industries can improve the services they offer to customers by leveraging AI. These content brands value their partnerships with their partners, which can be seen from their capacity to offer more comprehensive, white-glove services. AI also frees up significant time for teams implementing these ad monetization strategies. It gives them time to involve in more strategic discussions with partners about other opportunities for partnership enrichment and business growth, such as new ad formats, audience targeting techniques, and improved product offerings. The same applies to ad operations and client service teams, who will have more time to develop strategic insights and encourage additional spending from current client accounts. All of this ultimately results in happier, more devoted customers who remain for a long time.
Opportunities for Optimization
The high error rates within the current manually driven ad ops workflow represent one of the biggest areas for improvement. The human-based error rate is typically high when teams enter large amounts of redundant data. Additionally, the routine, repetitive nature of entering data is neither stimulating nor challenging, leading to teams’ eventual disengagement from their work and increased likelihood of errors. Additionally, human-driven workflows lack team-wide consistency in process, language, and solutions. Human nature makes it challenging to get everyone on the same page regarding procedures and how the team functions because different people approach problems uniquely. Automation and AI are useful in this situation. Each of the previously mentioned components contributes to a subpar workflow requiring optimization. Teams can now use their time to concentrate on higher-level tasks like strategy and leave the tedious tasks to the bots because AI has developed enough to offset these repetitive tasks.
Considerations for AI Implementation
The market for automation software is not segregated. No one solution works for all new technologies and tools because they are developed and used for a variety of purposes. As a result, there are a few things that those looking to incorporate AI into their strategies need to keep in mind.
The first thing to remember is that brand ROI can take some time to materialize after automation implementation due to its frequent longer lead times. However, integrations of AI and automation are long-term investments that will boost productivity, permit scaled growth, and guarantee quicker revenue streams for years to come. Start by keeping track of success indicators like time saved and increased productivity with teams freed up for other tasks.
Since AI software is so complicated, implementing it can be intimidating. Key team members who can effectively advocate for the tool, the company’s needs, and business goals are essential. Concentrate on education and introduce AI gradually so that any hiccups can be fixed right away. Professional development initiatives geared explicitly toward training teams in AI can be a great investment. This will increase productivity and create happier, more content teams that feel empowered to pick up new skills and advance their careers.
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The right team positioning is essential when starting an automation implementation journey. Employees frequently feel uneasy when AI is mentioned; understandably, they might think technology can replace them. The fact is that AI is still a long way from being able to completely replace people because it lacks the unique creativity that only people possess. As with any tool, AI is increasingly being incorporated into workflows, so teams must learn how to use it effectively to improve their capabilities. Given how quickly the advertising landscape changes, AI is essential to monetizing ads. Publishers and advertisers who use AI to boost productivity and enhance overall service will put themselves head and shoulders above those who don’t. They will fall behind if they decide against adopting AI and automation.
Advertising brands can remain competitive by attracting repeat business from satisfied customers and campaign successes. Their internal teams’ abilities and investment in top talent account for a significant portion of their competitive edge. In an ineffective ad ops team, top talent is frequently the most relied upon and most vulnerable to burnout because it is expensive to recruit and train. Automation and AI are scalability tools that give top talent more time and energy to focus on higher-value work. Each team member has at least an 80/20 split between core task-based work and project-based work in an ideal state. This ratio can eventually result in career growth for top talent, which boosts ROI and expands revenue streams for the company.
