For years, the advertising industry has adopted machine learning (ML), a type of AI, to be more efficient and targeted. All of us in the industry remember when the rise of programmatic ad buying marked a significant shift towards AI/ML-driven ad purchasing, which eliminated the drudgery of manual research and budget allocation. Whether buying or selling ads, everyone in the advertising ecosystem has benefited in some way by the adoption of AI/ML. The industry is now on the brink of making an even bigger breakthrough thanks to the convergence of generative AI and machine learning. Let’s look at how.
A Quick Snapshot of How We Got to Where We Are
AI/ML and digital go hand in hand, going back to the days when Amazon began using AI-driven recommendation engines to provide personalized product suggestions. It didn’t take long for the advertising industry to catch on to the power of AI, even before the advent of programmatic advertising. Data mining and basic ad optimization techniques emerged in the early 2000s to help target ads based on demographics and audience browsing behavior.
Soon, AdTech companies were emerging to refine the way AI and ML were creating more targeted ads by predicting consumer behavior. Successes were being reported in publications such as Harvard Business Review, which noted how ML could boost advertising ROI threefold by predicting whether a person would click on a particular ad.
This was all well and good. But it was only a start.
Generative AI is like a vaccine that has cracked a code for changing the entire advertising process down to the level of how a media planner responds to requests for proposals.
How Gen AI and ML Change Things
The explosive growth of generative AI combined with ML is taking AI and advertising to another level by permeating every aspect of advertising, from workflows to personalization. Generative AI is like a vaccine that has cracked a code for changing the entire advertising process down to the level of how a media planner responds to requests for proposals. Here are three examples (and I’m really not even scratching the surface):
- AI-based proposal generation: an AI system automatically generates customized ad proposals for clients based on targeting preferences and inventory availability, reducing manual effort.
- Demand channel optimization: using AI to analyze market trends and audience data, a company dynamically adjusts ad pricing and placement across various platforms, maximizing revenue.
- Generative AI in assisted sales workflows: AI tools assist sales teams by providing real-time insights and recommendations during negotiations, improving decision-making and efficiency.
Consider proposal generation. Right now, if you are a media planner, and you want to respond to an RFP, you’re going to endure a largely manual process of matching inventory availability with advertiser targeting preferences. But generative AI makes it possible to automate the process of identifying the optimal cross-platform inventory for the advertisers, then creating proposal content to better communicate options available to those advertisers. Generative AI can draw upon templatized data from source material while also helping you write more creative content as needed.
Generative AI extends its influence into all aspects of managing an advertising campaign. One of its benefits is the ability to help media planners update and revise advertising programs. Say a media planner wants to update their pricing for a specific agency or launch a campaign. Generative AI Reduces the amount of manual work required to access and action on specific information needed to do either. And who doesn’t want to make workflow easier?
Businesses need consistent data interpretation for AI to accurately analyze and derive meaningful insights because AI algorithms rely on data consistency to perform effectively.
But for AI tools to be used effectively across workflows, foundational requirements need to be in fulfilled.
First, advertising platforms – streaming, digital, linear, etc. — need to be unified (or connected). Advertising platforms each have unique characteristics and data formats. Unifying these platforms ensures that AI systems can access and process data across all channels. This integration is essential for creating a cohesive advertising strategy that applies the strengths of each platform.
Once the platforms are connected, the data (such as ad inventory) needs to be connected and normalized. Normalizing data means standardizing the format, structure, and semantics of data across different sources. In the context of advertising, this could include metrics like impressions, clicks, and conversions. Businesses need consistent data interpretation for AI to accurately analyze and derive meaningful insights because AI algorithms rely on data consistency to perform effectively.
Connected platforms and normalized data unlock the potential of AI to deliver the outcomes that advertisers are being promised. For example, unified platforms enable cross-channel measurement, providing a comprehensive understanding of campaign performance across the entire customer journey. AI can then analyze this data to accurately attribute conversions and optimize spend allocation for maximum ROI. Normalized data ensures consistent metrics and eliminates data silos, allowing AI to accurately track campaign performance and identify areas for improvement across all channels.
Vet Your Generative AI Tools Carefully
Any business adopting generative AI needs to be mindful of its challenges and choose generative AI tools carefully. This is an emerging technology, and businesses choosing a generative AI platform should vet their partners against criteria such as:
- Data privacy and security: handling large amounts of consumer data requires stringent data privacy and security measures. A partner with a generative AI platform should be transparent about how they protect privacy of their clients’ data.
- Integration with existing systems and processes: integrating AI into existing workflows can be complex and resource intensive. An AI platform partner should make it clear how much upfront integration is required. They should also specify how much training is needed to apply their own platform.
- Reliance on quality data: AI’s effectiveness depends on the quality of the data fed into it. Ensuring quality of data requires a strong partnership between a business and the provider of its generative AI platform.
Crawl, Walk, Run
We believe that generative AI is progressing along a crawl, walk, run journey – and as an industry we are just learning to crawl. This means generative AI is being used to make workflows more efficient and to manage tasks such as persona creation. As generative AI evolves into the walk and run stages, generative AI platforms will learn on their own and help businesses make more intelligent, complicated decisions about how to manage advertising across all platforms, ranging from television to digital. The future will be here sooner than you think.
To learn how Operative can help you optimize advertising with generative AI, contact us.