Evolution of behavioural targeting

SAE's Hiba Hassan looks at the evolution of behavioral targeting and its future in advertising

The era of personalised advertising is upon us, enabled by advancements in behavioral targeting technology. Imagine switching on a device and being greeted by a personalised shopping assistant that caters to your tastes and preferences. This scenario is no pipe dream but the realisation of behavioral targeting—an advanced marketing technique that zeroes in on individual online behavior. Born out of the internet boom in the 1990s, third-party cookies allowed advertisers to tap into users’ data, opening the digital floodgates to unprecedented audience targeting opportunities. Companies like Tacoda, Revenue Science, and Blue Lithium capitalised on these developments, refining their algorithms for finely honed ad delivery.

Fast forward to the early 2000s: social media giants Facebook and Twitter handed advertisers a treasure trove of user data never before seen. The rise of mobile devices subsequently paved the way for mobile behavioral targeting; however, as with most technological progression, concerns naturally arose—chief among them were privacy issues. Consequently, regulators introduced cookie usage constraints and FTC guidelines aimed at establishing transparency around behavioral targeting while safeguarding consumer privacy.

Given increased public interest in privacy protection—and following the Cambridge Analytica scandal—the advertising sector investigated new ways to engage consumers ethically. Browsers began phasing out third-party cookies—the very cornerstone of behavioral targeting—while Apple’s App Tracking Transparency (ATT) framework prompted developers to obtain user consent before tracking data for advertising ends.

The global COVID-19 pandemic sparked not only increased online activity but subsequent privacy issues too. Consequently, marketers found themselves increasingly reliant on first-party data collections—those acquired directly from consumers—to fine-tune their strategies. Collaborative platforms such as Google are also focused on limiting tracking capabilities for consumer security purposes.

As advertisers begin exploring alternative means of reaching audiences—contextual targeting based on webpage content, demographic profiling, keyword targeting, and influencer marketing –integration has emerged as a popular solution for maximising ad efficacy. By 2023, experts predict that combining strategies like demographic and behavioral targeting will ensure that marketing efforts remain both effective and respectful of consumer privacy.

Enter Web 3.0—or the “Semantic Web”—the latest incarnation of the internet that prioritises consumer control over personal data. This paradigm shift holds immense potential to revolutionise the behavioral targeting landscape further. Alongside emerging technologies—such as Non-Fungible Tokens (NFTs), Virtual and Augmented Reality (VR/AR), 5G, Artificial Intelligence (AI), Machine Learning (ML), and edge computing—Web 3.0 promises to usher in a new era of advertising where users dictate the content they consume.

For instance, uniquely collectible, opt-in advertising via NFT subscription boasts well-defined audience demographics, furnishing advertisers with accurate user behavior information for exceptional ad targeting. Meanwhile, AI and ML, when combined with VR and AR technology, have the potential to generate incredibly targeted ads driven by complex user patterns. Furthermore, 5G networks and edge computing can facilitate real-time data transfer for unparalleled behavioral targeting precision.

Thus, behavioral targeting is a dynamic field shaped by both privacy concerns and technological advancements. As user-centric marketing continues to gain traction amid emerging trends such as Web 3.0, NFTs, AR, VR, and ML technologies, the future of advertising looks set to reinvent itself as an increasingly personalised experience.

In particular, the MENA region stands poised to benefit from this ongoing evolution as stakeholders embrace change and invest in industry-transforming solutions. By doing so, companies operating within this region will likely find themselves at the forefront of advertising innovation while maintaining a strong focus on ethical consumer engagement.

In conclusion, behavioral targeting has come a long way since its inception in the late 1990s. As privacy concerns continue to drive regulatory changes and new technologies emerge to enable more robust and personalised approaches to advertising, businesses must adapt to remain competitive. By staying abreast of these developments in Web 3.0, NFTs, AI, VR, AR, and other technological frontiers, marketers can ensure their ad campaigns resonate with target audiences in an increasingly crowded digital space.

Key trends shaping the future of behavioral targeting include hyper-personalisation, AI chatbot integration in messaging platforms, visual recognition technology coupled with natural language processing (NLP), and voice-enabled chatbot technology. As customers seek tailored experiences, AI chatbots can offer personalised recommendations based on individual preferences and real-time interactions. Retailers and content creators alike can leverage AI for product suggestions and relevant content delivery.

Organisations that harness AI chatbots integrated into messaging platforms can streamline communication flow by providing prompt answers to frequently asked questions or resolving issues efficiently. This approach saves time for both customers and employees while enhancing convenience and user satisfaction.

By combining AI chatbots with advancements in NLP and computer vision, advertisers can enrich customer interactions by understanding and responding to images or videos alongside text-based queries. This synthesis expands communication possibilities between users and AI-powered platforms.

Finally, as voice assistants like Alexa, Siri, and Google Assistant continue to rise in popularity, so will voice-enabled chatbot technology. The fusion of NLP with speech recognition technologies will enable AI chatbots to support seamless voice interactions and cater even better to the needs of users. Adopting such technologies will allow for more natural communication while providing personalised experiences across various interaction channels.