Artificial Intelligence and Programmatic Advertising: Taking a Next-level Strategic Approach to Targeting and Personalization using AI ML
Thanks to the newly established synergy between Artificial Intelligence and Programmatic advertising technologies, we are witnessing an eye-watering scenario in the cookieless era. Despite the cookie phaseout, advertisers and publishers are confident they can successfully navigate the new fabric of direct native and programmatic advertising using new-age AI tools. That’s why, the focus has shifted toward delivering a one-to-one ad experience to an audience with a broader aim to reach a larger demographic across TV, OTT, Connected TV, Live streaming, and social media.
Sources report that in the next 10 years, the spending on AI-powered advertising campaigns and software will reach $1.3 trillion. The lion’s share of these adtech innovations would happen on the supply side where brands can deliver personalized, hyper-targeted ad experiences to their audiences.
At its core, programmatic advertising is an intricate combination of programmatic algorithms, automation, and predictive intelligence for buying /selling digital ad inventories. Often considered a complex mesh of adtech solutions, programmatic technology for publishers and advertisers can become simplified and more effective for targeting and personalization with the infusion of AI and Machine Learning capabilities. There are four main areas in programmatic advertising where AI is widely used:
- Content curation
- Real-time ad placements
- Customer data mining
- Measurement and reporting (Analytics)
In the last few years, a fifth element has emerged in the Artificial Intelligence and programmatic advertising campaigns — Personalization.
We spoke to leading Adtech executives to gauge the current pulse of paid media activation and personalization in the cookieless era. Our panel of speakers include:
- Filippo Gramigna, COO, Onetag
- Anders Lithner, CEO, Brand Metrics
- Hailey Denenberg, VP of Strategic Initiatives at GumGum
- Gary Mittman, CEO, KERV Interactive
- Gareth Holmes, Vice President of Commercial Strategy & Media at SeenThis
- Kevin Geffray, VP of Paid Media Jellyfish
- Sara Vincent, Managing Director, UK, Utiq
Using AI and Data Analytics for “Paid Media Activation” in the Cookieless Era
Filippo Gramigna, COO, Onetag
Brands should build direct relationships with customers, respecting their privacy. With consent, collect and leverage your own first-party data, including customer preferences, behaviors, and transaction history, then use platforms with a strong focus on AI to derive meaningful insights from this data, and from user interactions, to drive more effective paid media activation strategies.
Brands should also consider contextual targeting, where ads are placed based on the context of the content being viewed, rather than relying on user behavior. AI semantic algorithms analyze content to ensure better alignment with a brand’s message and exclude contexts that are not brand-suitable.
Use machine learning algorithms for real-time ad optimization, where ad placements, formats, and targeting are adjusted based on ongoing performance data, ensuring that ads are delivered to the most relevant and qualitative audience through the most efficient supply path.
Invest in privacy-centric technologies that adhere to regulations, while still allowing for effective targeting. Technologies that use cohort-based targeting or consumers’ first consent mechanisms can enable personalized experiences without compromising user privacy.
Finally, given the evolving landscape, adopt a test-and-learn approach to continually experiment with different strategies, channels, and technologies, as no one solution on its own is likely to be enough to replace cookies.
Using AI to Optimize Campaign Outcomes is a Game-changer in the AdTech Industry
Anders Lithner, CEO, Brand Metrics
Having adopted an algorithmic approach from the get-go, we note that the distance between keynote presentations and reality has shortened since the public introduction of generative AI.
Previously, many companies were claiming to use AI, when in fact they were just running models based on the four operations of arithmetic – addition, subtraction, multiplication, and division. Today, companies are actually training their models using AI and deep learning. The key to success is having access to vast amounts of consistent data.
The old saying, that data is the new oil, is finally coming true. To create a self-driving car, you don’t need petrol, but you do need millions upon millions of rows of data from cars driven by humans. The same goes for ad tech.
We too are training our models, based upon the many million rows of survey responses we have from consistently measuring brand lift globally across the ecosystem. We are rushing towards a point where we can predict campaign outcomes and optimize towards given objectives. The impact of this will be an industry game-changer.
Transforming Advertising Experiences Using AI Critical to Efficiencies and Scale
Gary Mittman, CEO, KERV Interactive
“Programmatic adtech promises to automate the processes that should be automated, to drive value for advertisers, publishers, and consumers, and free up marketers and publishers to work on new ways to build their businesses. This is something that developments in AI are certainly helping support. We are seeing that AI can transform experiences, making ads more relevant and seamless, giving marketers the opportunity to reach viewers when they are receptive to messages and to making a purchase. Deep technological innovations in programmatic are making contextual placements more central to advertising strategies and opening up new revenue opportunities for publishers.
With hundreds of thousands of TV shows and movies available for potential ad opportunities, AI will become more critical in creating efficiencies and scale. Better data, better insight, and the AI-driven capability to automate functions are driving advances in ad approval and brand safety that build on the trust between consumers, brands, and publishers, increasing confidence in CTV and video ad channels. AI is also helping KERV power innovations in ad podding and moment marketing, so marketers can place ads based on content relevance that are also timed to coincide with peak moments within the content that resonate with viewers’ current engagement and interests.”
AI Helps CTV Content Analysis
Hailey Denenberg, VP of Strategic Initiatives at GumGum
“Advances in AI are facilitating the delivery of brand-safe and highly relevant ads to CTV audiences, both through scene-by-scene analysis of CTV video content and full transcription and analysis of audio content. AI is enabling advertisers to understand CTV content at a deeper, more granular level than ever before, giving them more confidence in the channel and enabling them to deliver more effective campaigns.
As cookie- and IP-based tracking fall out of favor, we will also see AI-powered contextual targeting being adopted in other key emerging advertising environments, including in-game and the metaverse.”
Ad Format Optimization Using AI to Remove Wastage in the Programmatic Supply
Gareth Holmes, Vice President of Commercial Strategy & Media at SeenThis
This is how I see the activation process could look like soon, with AI in the mix.
When a creative is delivered to the media agency, and to ensure it is optimized to the campaign goals, AI will provide adaptations & edits to ads to be shown to consumers; typically a media agency will receive an Image or Video asset and AI will suggest layers to be added ensuring the highest probability of goal achievement; ads will differ depending on whether they are branding or performance and AI-tailored to the specific product and offer being sold.
AI will be leveraged within DCO activations where varying data-set configurations for targeting to ensure historical performance data is included in the decisioning of each ad selected i.e. data-set + creative + AI = highest probability of a desired outcome.
Once ready to go live AI will minimize underperformance rapidly, preferably before, a campaign is activated; AI suggests where (which media placements to which consumers) to start delivering campaigns based on historical data sets, rather than activating then optimizing, removing wastage as buyers set campaigns live within the programmatic supply.
Live and delivering; AI optimizes formats to meet desired metrics, on the fly, saving resources and media to optimize the structure & format (CTA/colors/ad structure) of creatives providing material benefit to any buyer, and their campaign.
Gen AI Tools Setting a New Standard for Programmatic Campaigns
Kevin Geffray, VP of Paid Media Jellyfish
“AI is everywhere. It’s finally moved from abstract futuristic presentations, and now we’re activating solutions.
In the realm of modern media, AI has fundamentally reshaped the risk-versus-reward dynamic. Its integration into creative processes, bidding strategies, and performance measurement empowers businesses to achieve outcomes, particularly within the realm of programmatic advertising.
Despite the proliferation of AI-driven tools, we’re not quite at the stage of fully automated solutions that can be blindly trusted. A human-in-the-loop is still needed, acting as a vital checkpoint to ensure the quality and appropriateness of AI-generated outputs. Whether it’s maintaining a brand voice in creative content, upholding brand safety standards, or refining measurement frameworks, the human element is currently necessary.
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The widespread adoption of AI-generated assets for programmatic campaigns is imminent, particularly for mid- and lower-funnel content. We’re working with many clients, activating GenAI tools like Pencil, which helps them create efficiencies and improve business outcomes. This shift to Gen-AI-generated assets will necessitate a re-evaluation of traditional workflows, fostering closer collaboration between creative, paid media, and measurement teams. Expectations include a heightened focus on quality assurance and a streamlined process for asset creation and adaptation.
Furthermore, this year is poised to witness the widespread adoption of AI-powered bidding algorithms, potentially establishing a new standard for programmatic campaigns. Products such as PMAX, DemandGen on Google, or Advantage+ on Meta are already paving the way, unlocking additional reach and performance through DSPs. Guiding these tools with a strategic blend of KPIs will be crucial in maintaining quality control while maximizing campaign effectiveness.
Would Third-party Cookie Deprecation Impact Retail Brands?
Sara Vincent, Managing Director, UK, Utiq
Retailers, like all other brands, are going to have to face up to the reality of life after the third-party cookie, but rather than fear it, they should embrace this as an opportunity to test and learn new cookie-less solutions. One of the best safeguards against cookie deprecation is focussing on building a first-party data strategy that is fully consented to accept marketing comms from the brand.
Retailers, especially those with an established loyalty program in place, are in a better position than most to leverage this. Moreover, the data they hold on their customers, including granular transactional data, is a rich source of insight that can be mined to personalize their communications and present their customers with offers that should be of interest to them. Retail brands that get their first-party data strategy right will reap the rewards.
Why Should AdTech Companies Deepen Innovations in AI and Deep Learning Technologies?
75% of Marketing and Advertising professionals in North and South America use the ChatGPT AI tool for drafting personalized ad content, brainstorming, analytics, and researching.
AdTech companies use AI-powered tools and solutions to deliver programmatic ads across live, linear, and VOD streams. Driven by the advanced AI in programmatic adtech solutions for the CTV marketplace, publishers and advertisers can create refined monetization strategies with predictive analytics and automated self-service reporting.
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In the adtech industry, AI and deep learning capabilities deliver these benefits:
1 . Real-time Bidding (RTB)
AdTech companies use reinforcement learning (RL) algorithms for RTB advertising. Advanced AI solutions for RTB in programmatic advertising automate the process of picking demand-focused ad spaces for a pre-defined target audience. All this happens in real time. In the RTB programmatic adtech space, AI can further improve audience targeting outcomes based on ICPs and engagement data from past campaigns. The ultimate goal is to bring transparency and efficiency to the RTB algorithms while reducing ad wastage.
2. Creatives Pipeline
In addition to specialized audience targeting, AI in programmatic adtech plays a big role in auto-adjusting the text, images, banners, and CTAs for better results.
3. Ad Fraud Detection and Prevention
Advertising fraud or ad fraud affects experiences and ROI on the web, mobile, and display devices. Ad fraud detection mechanisms using AI can save the adtech industry from programmatic wastage, yielding up to 3.6% higher ROAS compared to traditional routes. Embedded AI in programmatic adtech solutions is helping publishers and advertisers stay ahead of the ad fraud challenges. With improved reporting, AI and deep learning can mitigate ad fraud difficulties with more transparent and targeted strategies.
4. Smart Automation with Personalization
According to research, 13% of American marketing and advertising professionals used AI for ad personalization. Automation for personalized adtech campaigns can propel creativity in the programmatic ecosystem. This transformation empowers adtech customers for personalization campaigns with AI and programmatic advertising.
What’s Next in Artificial Intelligence and Programmatic Advertising?
Our analysis shows senior adtech leaders would continue to use AI and programmatic advertising tools for Measurement, Targeting, and Data Applications.
According to Statista, 73 percent of the UK-based senior brand marketers stated they used artificial intelligence (AI) to target digital ads; for measurement (48%); and for data application (45%). As we head into another challenging year in adtech, we should expect marketing and advertising companies to increase their investments in AI and programmatic adtech solutions for a wide range of applications in the digital advertising ecosystem.
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