AI in programmatic advertising: Rise of real-time bidding and personalized ads

Imagine a world where ads can predict your every desire before you even know it. It may sound like science fiction; but the reality is that such a scenario is a possibility, thanks to artificial intelligence. Remember the days when ads seemed to pop up at random, often feeling irrelevant and intrusive? Those days are numbered. AI is ushering in a new era of targeted advertising that’s not just more effective, but also more personalized and less annoying.

Real-time bidding

AI is revolutionizing the advertising landscape, particularly in the realm of real-time bidding (RTB). This process involves the auctioning of individual ad impressions in real-time, allowing advertisers to bid on specific ad placements based on user data.

Here’s how AI is transforming RTB:

• Enhanced Targeting: AI algorithms can analyze vast amounts of user data, including browsing history, demographics, and real-time behaviour, to identify the most relevant audience for a particular ad. This precision targeting increases the likelihood of ad engagement and conversion.
• Optimized Bidding: AI can predict the value of an ad impression based on various factors, such as the user’s likelihood to click or make a purchase. This enables advertisers to bid more strategically, maximizing their return on investment (ROI).
• Fraud Detection: AI can detect and prevent ad fraud, such as click fraud and viewability fraud, ensuring that advertisers are paying for genuine ad impressions.
• Personalized Ad Experiences: AI can deliver highly personalized ads based on individual user preferences and behaviour, creating more relevant and engaging experiences.
• Predictive Analytics: AI can predict future trends in user behaviour, allowing advertisers to anticipate demand and adjust their bidding strategies accordingly.

AI will soon revolutionize ad delivery by creating tailor-made, personalized ads with interactivity at their core.

Enter AI and real-time bidding in programmatic advertising undergoes drastic changes, says Sahil Chopra, Founder & CEO, IcubesWire. He feels that it’s not just faster, but now more accurate and highly efficient.

“While it takes a blink of an eye for a web page to load, AI has already worked its magic by identifying the value of an ad impression – from dissecting a user’s profile to the ad’s relevance and competing bids. And this allows advertisers to secure the most strategic spots at the best possible price. With AI, the chances of missing your target are slim. Features like user behaviour prediction help create more personalized ad experiences. As AI continues evolving, its role in interactive ads will increase. We need to stay tuned for more dynamic and engaging ads that can be tweaked in real-time based on user interactions for a more immersive experience,” says Chopra.

With AI and machine learning models being integrated into RTB and Open RTB, publishers can achieve better optimization and automation of ad placements with minimal manual intervention, resulting in higher fill rates and maximized revenue from available ad inventory, says Saurabh Gupta, CEO and Founder, VeriSmart AI.

For advertisers, Gupta adds, these advancements enable ad placements that are informed by past data, ensuring their ads appear in the most relevant publisher slots, ultimately reducing Customer Acquisition Costs (CAC) and improving Return on Investment (ROI).

AI is revolutionizing RTB in India by optimizing bid strategies and inventory allocation in milliseconds. This brings advertisers improved targeting precision and cost efficiency, especially crucial in India's price-sensitive market, points out Russhabh R Thakkar, Founder and CEO, Frodoh.

Ethicality

The use of AI in programmatic advertising brings several ethical concerns, including data privacy, algorithmic bias, and transparency. Addressing these issues requires implementing robust data protection measures, ensuring algorithm fairness, and maintaining clear communication with consumers about data use and ad targeting practices.

Sahil Chopra reckons that data privacy and transparency are two key concerns for every marketer. He feels that marketers need to be vigilant when it comes to consumer privacy.

“AI functions on heaps of user data, which may include sensitive information like browsing history, location, and purchase behaviour. If not managed strictly, this data collection can lead to privacy violations and loss of consumer trust. Marketers must prioritize transparency and data protection at all costs. They need to be clear with consumers about how their data is collected and used. In addition, AI algorithms should be regularly audited for bias to ensure that they don’t unintentionally discriminate against gender and user groups, as seen in the past,” Chopra adds.

There are many ethical concerns associated with the use of AI in programmatic advertising, points out Saurabh Gupta. “First, ethical use of AI in programmatic advertising requires legitimate data sourcing methods, including strict compliance checks like user consent management, data privacy audits, and data anonymization models to prevent misuse or de-anonymization in the name of hyper-personalization. Since AI models operate on derived or probabilistic intelligence, using accuracy checks like the Gini score can help ensure data is handled according to best practices without risking individual privacy. Additionally, preventing third-party data access is crucial,” explains Gupta.

He further suggests that in order to mitigate these risks, pre-model checks on data sources and frameworks like Distributed Ledger Technology (DLT) can help manage user consent and ensure compliance. “Implementing stronger legal frameworks and binding agreements with robust indemnities for data processors working on behalf of data fiduciaries can further protect data. Finally, ensuring that AI models are used for programmatic intelligence at the audience cohort level, rather than targeting individuals, will help safeguard user privacy,” he adds.

In India’s programmatic landscape, key ethical concerns include potential biases against regional languages and rural audiences. Mitigation strategies should focus on diverse data sets and regular audits to ensure inclusive ad delivery across the subcontinent, says Russhabh R Thakkar.

Machine learning

In a highly competitive landscape, marketers need to harness machine learning to enhance audience targeting and personalization.

From refining targeting to personalizing ad experiences, ML can give marketers the much-needed edge in a competitive landscape, points out Sahil Chopra.

“Analysis of vast data sets can help underline patterns in user behaviour, preferences, and engagement, enabling marketers to distinguish audiences more precisely and create tailored ads that resonate with them. AI and ML will continue building on hyper-personalization, targeting audiences based on demographics, real-time interactions, and contextual aspects. What’s more, the rise in AI-led creative tools will allow for the rapid churn of personalized ad content, allowing brands to maintain relevance and engagement,” concludes Chopra.

In a post-cookieless world, marketers are seeking better methods for audience discovery and targeting, notes Saurabh Gupta, adding, while some have experimented with first-party data enrichment and data exchange through a data clean room approach, this method is often slow and lacks scalability. According to Gupta, a more effective solution is the Distributed Ledger Technology (DLT) approach.

“With DLT, multiple first-party platforms don’t need to share or transfer data with each other directly. Instead, they use a data intelligence layer that enables secure collaboration, allowing for real-time audience discovery and hyper-personalization at the cohort level. This method is highly scalable, supporting hundreds of matches with numerous permutations and combinations of data attributes, while maintaining data privacy and compliance. Marketers simply need to enter a relevant Generative AI prompt into the framework, and the DLT approach enables real-time audience discovery across hundreds of user attributes (digital, financial, behavioral, etc.) without the need to exchange data between organisations, thereby ensuring user privacy. We have developed a decentralized data intelligence framework called VeriSmart.AI, which has helped large organisations achieve data interoperability for over 528 million unique users while enhancing user privacy,” Gupta says.

Indian marketers are leveraging machine learning for innovative audience segmentation, moving beyond traditional demographics, says Russhabh R Thakkar. As CTV adoption grows, albeit slowly, it offers a new frontier for experimental targeting, allowing planners to explore household-level personalization at scale.

In conclusion, AI is revolutionizing the advertising landscape by enhancing targeting precision, optimizing bidding strategies, detecting fraud, and delivering personalized ad experiences. With real-time bidding (RTB) and programmatic advertising, AI has ushered in a new era of dynamic, data-driven marketing that is more effective and engaging for consumers. As AI and machine learning integrate more deeply into advertising strategies, marketers must balance innovation with responsibility, prioritizing consumer trust while maximizing the potential of AI for targeted, personalized, and efficient advertising.

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