By Brian Thoman, WideOrbit CTO

In today’s rapidly changing media industry, organizations are challenged with finding ways to gain efficiencies in ad campaign planning, sales, and trafficking processes, while meeting the increasing demands of advertisers. Advertisers demand increased precision, better results, and improved campaign efficiency, so media companies must find innovative ways to deliver the outcomes advertisers want as competition grows. From streamlining the creation of campaigns and automating sales proposals, to optimizing the delivery and placement of advertisements, Artificial intelligence (AI) has the potential to bring unprecedented efficiency.

The Challenge of Modern Advertising Campaigns

Today’s advertisers are not just looking for impressions; they expect media companies to deliver results, whether it’s increased website traffic, higher brand engagement, or improved return on ad spend (ROAS). But for media organizations, the effective and efficient delivery of these results across linear and digital media is a challenge. Traditional campaign planning, proposal generation, and ad trafficking processes are time-consuming and often prone to error, making it difficult for media companies to remain competitive in a fast-paced and data-driven environment.

When applied to media campaign planning, sales, and trafficking processes, AI can address these challenges by automating processes, analyzing massive amounts of data in real-time, and optimizing campaigns to maximize results.

Traditional campaign planning has involved significant manual effort, involving the analysis of historical data, market trends, and consumer behavior to create proposals that meet advertiser needs. This process can take considerable time, frustrating advertisers and opening the door to competitors.

With AI-powered tools, sales teams can streamline and enhance the planning process. AI can quickly analyze historical data, predict campaign outcomes based on past performance, and tailor sales proposals to each advertiser’s specific objectives. Predictive analytics—using techniques such as retrieval augmented generation (RAG) based on historical campaign data—can help sales professionals generate optimized proposals, accelerating sales and increasing accuracy and confidence in the projected outcomes.

Sales teams can use AI to analyze trends and consumer behavior to offer advertisers more tailored, data-driven recommendations. Instead of relying on intuition or anecdotal evidence, AI allows sales professionals to make data-based predictions about how a campaign might perform across various platforms, which ads will yield the best results, and where advertisers should allocate their budget for maximum impact. These deeper insights can also improve alignment between advertiser goals and the media company’s internal business objectives, ultimately leading to improved sales performance.

AI in Ad Trafficking: Automating Precision and Reducing Errors

Ad trafficking, a critical but often cumbersome part of media advertising, involves manual ad placement, tagging, log reconciliation, and campaign optimization. This work is highly labor-intensive, error-prone, and often inefficient. AI can significantly lighten the load for trafficking staff by automating these processes, making them far more accurate and effective.

With AI, algorithms analyze real-time campaign performance metrics such as airtimes, impressions, and audience engagement. Based on the data, AI can dynamically adjust targeting parameters, impression goals, and budgets, ensuring that campaigns are continuously optimized to meet desired outcomes. This also reduces the workload for traffic teams and allows for greater accuracy in ad placements.

The Role of AI in Makegoods

The kind of real-time optimization AI can provide can also reduce liability for media organizations. By optimizing unit placements, AI can help minimize both over- and under-delivery. And minimizing under-delivery will reduce the need for makegoods – ad placements given to compensate for pre-empted spots or campaign underperformance.

Makegoods have long been a thorn in the media industry’s side. They’re highly manual and time consuming, and can disrupt campaign planning and eat into profit margins. But AI-driven, proactive optimization can significantly reduce the need for makegoods, increasing advertiser satisfaction and lowering media companies’ costs. And when a makegood is unavoidable, AI can quickly and efficiently identify appropriate placements for the makegood spot, ensuring compliance with advertiser requirements while significantly speeding up the process.

AI: Revolutionizing Media Advertising

The integration of AI into media advertising campaign planning, sales, and trafficking processes has the potential to revolutionize the industry. By leveraging AI-powered tools, media companies can offer advertisers more precise, data-driven proposals, reduce the manual workload involved in ad trafficking, and optimize campaign performance in real time.

AI’s ability to analyze large datasets, predict campaign outcomes, and adjust targeting strategies on the fly helps media organizations deliver better results to advertisers. However, it’s important to note that AI is not without its limitations. AI may not always accurately predict consumer behavior, and there’s a risk of over-reliance on AI, which is why AI can never fully replace the knowledge and experience of human beings. A balanced approach that combines the expertise of people with the power of AI data analysis – historical, real-time, and predictive – can help ensure the accuracy and quality of AI outputs while preventing over-reliance on the technology. While AI may automate specific tasks, it also creates opportunities for employees to focus on more strategic and customer-facing activities – activities that generate more business.

Investing in AI can be a game changer for media companies looking to gain a competitive edge in meeting the needs of today’s advertisers. This will translate to higher satisfaction for advertisers, fewer makegoods, and more efficient use of resources. As AI technology continues to evolve, its potential in media advertising will only grow, offering even more opportunities for innovation and optimization.

This blog post grew out of a session of the same name that took place at the TVNewsCheck Local TV Strategies conference at NAB Show New York 2024. The session featured Brian Thoman, WideOrbit CTO, and Brad Epperson, CEO of TCB Media Advisors.

View the session video on demand.

WO Q brings AI to WideOrbit software with a chat-based interface that leverages a Large Language Model (LLM) trained on language specific to the media industry. Building context through user prompts combined with previous campaign data (Retrieval Augmented Generation), WO Q leverages Natural Language Processing (NLP) techniques to automatically create optimized advertising proposals.

Download our WO Q brochure or contact us to learn more.