Let’s face it. Programmatic has always been a data-driven process that uses a potent mix of manual and automated intervention. As such, programmatic advertising could be gauged as always having a fundamental element of artificial intelligence to it.
But with increasing proliferation of data-driven and algorithmic approaches to programmatic, the space is poised to become all the more AI-driven. This shift has made data-driven advertising strategies more accessible and comprehensible, ultimately leading to enhanced performance when working with data-driven advertising platforms.
The shake up caused by the recent burst of AI into the scene has led to a massive proliferation of AI-driven tools and workflows. Within the marketing industry as a whole, AI tools are used for everything from content generation to targeting and analytics.
Global revenues of AI tools used in marketing are expected to grow an estimated $80 billion from 2023 to 2027, with AI-enabled ad spending anticipated to reach $1.3 trillion in the next decade. These numbers speak volumes about the impending indispensability of AI-powered tools across nearly all programmatic advertising platforms, and the profound impact of AI on the industry as a whole.
As such, in this scenario, it becomes important to understand how AI models are exactly being implemented, and the possible implications and risks to keep in mind while executing an Ai-first programmatic strategy, and what is the exact mix of “Real” and “Artificial” needed to drive programmatic success.
At a basic level, AI has sought to take away some of the manual redundant work involved in implementing a campaign and started doing the heavy lifting with data. The most popular, and perhaps most important involvement of AI models has been in achieving more efficient ad placements and targeting, as well as accurate and thorough data-driven analysis and campaign optimization.
For instance, machine learning algorithms run behind the scenes to optimize data across a number of dimensions, including:
- Ad format
- Ad environment
- Browser
- Device type
- Fold placement
- Geography
- Operating system, and more.
In order to clearly distinguish between AI use cases, we can segment them into the following examples
Precise Audience Targeting
AI algorithms analyze vast datasets, including user demographics, behavior, and preferences, to create detailed audience profiles. For example, platforms like Simpli.fi that utilize raw data formats can quickly multiply these benefits. This precision in targeting ensures that ads reach the most relevant audiences, significantly increasing the likelihood of engagement and conversions. According to a study by eMarketer, targeted ads driven by AI can improve click-through rates by up to 300%
Real-time Bidding & Optimization
Machine learning algorithms evaluate bid opportunities in real-time, determining the optimal bid price based on factors such as audience relevance, historical performance, and campaign objectives. This dynamic bidding process ensures that advertisers maximize their return on investment (ROI) by only bidding on the most valuable opportunities. For instance, The Trade Desk reports that its AI-driven bidding strategies have led to a 52% increase in cost-efficiency for their clients.
Ad Creative Optimization
AI tools and machine learning algorithms continuously analyze the performance of different ad creatives, automatically optimizing them for maximum impact. By leveraging these powerful algorithms, advertisers can refine and iterate their creatives, improving advertising performance and overall campaign effectiveness. Modern DCO tools have been increasingly using AI to iterate creative strategies in real time.
Performance Analytics & Insights
Algorithmic approaches to data analysis provide advertisers with comprehensive data and actionable insights to assess performance, understand behaviors, and optimize campaign strategies. With these insights, advertisers can make data-driven decisions to refine their campaigns for better results. A survey by Salesforce revealed that 70% of marketers using AI for performance analytics experienced faster and more accurate decision-making processes. At the same time, AI is poised to be a digital double edged sword, as the concurrent risks with regards to brand safety and traffic are likely to proliferate with increasing AI-fication in programmatic environments.
Increasing Prevalence of Made-for-Advertising (MFA) Websites
The MFA problem is poised to worsen with the advent of generative AI . MFA websites, designed solely to attract ad revenues, can now churn out content with minimal human oversight using genAI. This approach often sacrifices user experience, which is already poor on these sites. According to a study by the ANA (Association of National Advertisers), nearly 15% of ad spend is wasted on low-quality MFA sites, highlighting the magnitude of the issue. This number will only increase with AI proliferation for maladvertising purposes.
Misaligned Incentive Structures in Ad Tech
Current incentive structures in the ad tech ecosystem exacerbate the problem. Ad tech providers earn a cut of the transaction regardless of impression quality, incentivizing the use of low-quality inventory. When brands are offered low costs per thousand (CPMs), it often results in the inclusion of MFA or other low-quality inventory to reduce average costs. A report by Forrester reveals that 56% of marketers have encountered transparency issues related to ad placements, underscoring the need for better alignment of incentives.
Existential Threat to High-Quality Publishers
High-quality publishers are facing an existential threat due to AI advancements. They are already experiencing reduced traffic from social media platforms. Data from Similarweb, cited by Axios, indicates a decline in referral traffic to major publishers. With the rollout of generative search tools, clickthrough rates are expected to drop further as these tools provide direct answers, reducing the need for users to visit publisher websites. This trend could significantly impact publishers’ ad revenues, making it harder for them to sustain high-quality content production.
The Question of “To What Extent”
One of the questions that demand partners often have for DSPs and Exchanges that they transact through, is the question of the extent of AI intervention that will be considered right. Many a times, advertisers do not want AI to completely dictate the optimisation roadmap for their campaigns, but would instead want some level of control and transparency into what prompted a particular optimisation logic, and if that logic holds good in their particular case. As such, no programmatic ecosystem would ever become completely AI-driven, but would need a carefully measured amalgam of both. While the risks exist, the benefits stand to far outweigh the risks. The greatest advantage that accrues from using an optimum mix of Artificial Intelligence in driving campaign success is that you get to consolidate your campaigns onto a single platform by eliminating data redundancies, and achieving consistency in optimisation decisions across channels. If making data-driven decisions can support business outcomes, then consolidating that data into a single platform is a power move. When running your omni channel campaigns across multiple platforms, your valuable audience data can be spread thin. This data collected across channels is critical to informing the learning models that help drive performance. Furthermore, you can work toward unlocking the full potential of your advertising budget with advanced cross-channel frequency management and frequency savings reporting. At Nexverse.ai, we are forever in pursuit of achieving this optimum mix of AI and human intervention to drive the best returns for stakeholders on both sides of the spectrum. Our (ML) technologies, feature over 60 sophisticated recommendation models and algorithms that are continuously trained on unique features, all focused on optimising ad placements and driving optimum Ad experiences. As our models continue to evolve and train on campaign data, we will keep you updated on how close we are to achieving the perfect mix of AI, data, technology and human centricity to achieve the best possible RoI for advertisers as well as publishers. The future of programmatic is bright, and the future of programmatic is certainly AI-driven!