Kirill Yurovskiy: Targeted Advertising Trends for 2024

As we cruise into 2024, the world of targeted advertising continues to evolve at a breathtaking pace. Emerging technologies, new data sources, changing consumer behaviors and tightening privacy regulations are radically reshaping the landscape. Brands willing to embrace new innovative tactics and channels will be well-positioned to build more authentic customer relationships and drive superior campaign performance.

Here are some of the biggest trends and developments poised to disrupt targeted advertising over the next year:

Embracing the Cookieless Future

By 2024, the final dismantling of third-party cookies will be a reality across all major browsers. This seismic shift will require advertisers to evolve far beyond the traditional reliance on these digital trackers for audience targeting and measurement. Those caught flat-footed will face major challenges reaching desired audiences effectively and efficiently. 

Brands need to urgently prioritize building robust first-party data strategies. That means laser-focusing on capturing and activating authenticated, permissioned customer data from owned properties like websites, mobile apps, smart devices, CRM databases, surveys and more. Diversifying data partnerships and embracing collaborative “data clean rooms” with publishers and industry consortiums will also become essential data sourcing tactics.

Early cookie obsolescence pioneers report benefits like increased transparency, reduced ad fraud, and enhanced measurement precision when fully disconnecting from third-party cookie data. Granted, these transitions won’t be seamless, but the rewards of investing in new audience targeting and identity resolution solutions will be well worth it.

The Multiverse Opportunity

On a related note, 2024 and beyond will witness the continued fragmentation of the digital universe, spanning an ever-expanding realm of platforms, channels, devices and experience spaces – says expert Yurovskiy. The metaverse, Web 3.0, ambient computing environments, new immersive realities – all represent enormous emerging audiences for brands to target.

For instance, you may need to deliver personalized messaging, promotions and experiences that translate consistently across mobile apps, a customer’s AR glasses, their connected home or vehicle, and inside virtual worlds or gaming environments. That creates a massive data interoperability challenge, requiring high-precision cross-platform targeting, seamless identity resolution, and expansive inventory access.

Already, forward-thinking brands are exploring options like collecting viOMErs (viable identity components) like email addresses and device IDs to enable omnichannel consumer recognition. They’re locking down data onboarding partnerships and inventory access rights with emerging publishers and platform operators.

Immersive commerce in the metaverse alone is already estimated to be an $8 billion market opportunity this year. Advertising in these virtual realms is still in its infancy – imagine dynamically changing billboards to promote products based on a user’s avatar, location, preferences, digital assets owned and more using both contextual and identity-based methodologies. Plenty of fertile ground for trailblazers.

Hyper-Personalization Goes Aggregated

One potential bright side of third-party cookie elimination? An end to over-reliance on excessive individual user targeting and retargeting that feels interruptive and invasive for consumers. While one-to-one personalization remains valuable when done right, we’ll see the pendulum swing toward more intelligent, aggregate-level targeting that promotes relevance while enhancing privacy.

Cohort targeting tactics that bucket users into privacy-compliant groups or clusters based on collective traits, behaviors, interests and intents (without singling out individuals) are rapidly gaining adoption. Advertisers can deliver tailored audiences modeling using advanced machine learning, differential privacy methods, K-anonymity techniques and more to optimize relevance while protecting personal data.

This aggregate approach mitigates challenges like frequency capping and creative fatigue, yields more effective segmentation, and importantly, satisfies consumer privacy demands. It can be combined effectively with contextual channel and publisher targeting based on anonymized data signals.

Contextual, Semantic Targeting 2.0

Speaking of context, this traditional tactic for matching ads to relevant web content environments is evolving rapidly thanks to innovations in AI and machine learning. The new age of semantic targeting allows far more precision by leveraging natural language processing (NLP) and computer vision to deeply comprehend the context and nuanced sentiment of pages, videos, images and more. 

For example, an AI engine can identify emotions displayed in visual media using facial and image analysis and serve ads accordingly. Content-targeting can now be automated to recognize relevance based on text semantics, context beyond just keywords, sentiment/tone levels, and more. Considering more than 75% of the internet is image and video-based (but not crawlable for context like text), this computer vision capability has transformative potential. 

NLP enhances contextualization further by revealing consumer interests and intent signals for more granular personalization – even generative AI like GPT-4 engines could theoretically suggest copy customizations or spin content variations for different audiences. The future state is full-funnel, cross-channel semantic targeting using centralized data models trained by first- and third-party features.

Predictive Analytics Powering Segmentation

Another exciting area is the ability to leverage advanced predictive analytics, propensity modeling, and machine learning to vastly enhance audience segmentation, targeting and forecasting capabilities.  

For audience prospecting and segmentation, AI systems can ingest myriad datasets spanning demographics, psychographics, transactions, media interactions, CRM data, IoT sensor streams and more. The resulting analytics can make highly educated predictions about future consumer interests, purchasing behaviors, churn/retention probabilities and micro-segment them accordingly for targeted marketing.

AI also enables “next best experience” targeting whereby the optimal channel interaction, content asset, offer and communication cadence can be intelligently predicted and served. This is rapidly evolving from rules-based systems toward deep reinforcement learning models that improve performance through iterative self-learning feedback loops. I.e. the targeting gets progressively “smarter” and personalized to the individual the more data it ingests.

Machine learning likewise powers real-time predictive forecasting for targeting and content optimization. Algorithms can be trained to instantly generate a spectrum of targeting scenarios and simulate forecasted outcomes. This arms brands with agility to quickly pivot media spend, messaging and activation tactics to pursue desired business goals.

Connected Data Illuminating the Offline World

In the past, targeted ads were confined to digital channels because of limited offline audience intelligence. Today, a rich techscape of location data, mobile movement data, connected sensor signals, IoT datasets and identity mapping solutions are enabling brands to bridge the great physical/digital divide. 

Already we’re seeing the emergence of O2O (online-to-offline) audience targeting tactics like:

• Geoconquesting – Using mobile location datasets to identify real-world visitation patterns at competitors’ locations and serve tailored ads to try and poach customers. 

• Location analytics – Leverage movement data to identify when potential customers are near relevant retail environments, then deploy proximity targeting with timely offers or wayfinding experiences.

• Offline attribution – Close the loop by onboarding point-of-sale systems, payment card data, and other transaction datasets to tie digital ad exposures to physical world conversions.

• AI audience modeling – Aggregate multiple data streams spanning mobile, IoT, vehicle telematics, payments and more to not only identify audience clusters for targeting, but predict future real-world behaviors to influence through advertising. 

Clearly, the future of targeting is centered around using technology to create robust unified views of audience behaviors, motivations and contexts that span the digital/physical realm.

Content Relevance Trumps Format

Another key theme emerging is that targeting priorities are centering less on specific creative formats or delivery mechanisms, and more on tailoring messaging relevance holistically to audience mindsets.

For example, instead of launching separate “video ad campaigns” for various targets, brands can optimize tailored story sequencing and experience arcs via dynamically-assembled content combinations – short/long videos, interactive, augmented reality, games, etc. The content adapts to resonate with different motivations, content affinity signals, buying stages and personas.

Cadence, format and context all dynamically adapt based on predictive intelligence, not rigid legacy ad format parameters. It allows brands to transcend linear advertising toward curated, multi-dimensional content experiences designed to captivate specific audiences across the entire customer journey.

Ad products like conversational interfaces, shoppable content hubs tailored per persona, AI-generated influencer promotions, immersive virtual pop-up shops and digitally-rendered spokespeople are all indicative of where the experiential future of targeted advertising is heading.

Even on traditional channels like CTV, brands are exploring more personalized, contextually-relevant ad experiences with dynamic content optimization capabilities based on real-time audience data – think automated product recs, weather-focused messaging variations, tailored sequencing per location and more.

Privacy, Ethics and Transparency

Finally, as advanced targeting practices progressively push boundaries on personalization and consumer monitoring, responsible brands will need to make holistic privacy protection, consent governance, ethical data practices and transparency core business priorities.

Consumer sentiments around data sharing and tracking remain nuanced and constantly evolving. While some are open to sharing more personal information in exchange for more relevant advertising and enhanced experiences, others are staunchly committed to privacy preservation and skeptical of brands’ data stewarding motives. Governments and watchdog authorities too are growing increasingly protective of consumer interests in this area as the regulatory environment continually shifts.

In 2024, it will be imperative for brands to reevaluate their data sourcing and consumer consent methods, ensure stringent ethical data vetting practices, and maintain unswerving transparency about data policies and downstream uses of personal information. Privacy-enhancing technologies like differential privacy, federated learning, secure enclaves, homomorphic encryption and more will go from niche to norm.

It will clearly be challenging for marketers to navigate this ever-changing landscape for targeted advertising. But those who champion responsible data stewardship while continually innovating will be able to both build trusted relationships AND unlock transformative performance. The key will be embracing all the emerging trends and disruptive forces as opportunities versus impediments.

The future is poised to deliver more relevant, assistive and enriching ad experiences for consumers when executed properly. As brands master these new realities through sophisticated martech strategies, we’ll be able to engage audiences like never before.

© 2024