- Why are seasonal spikes, such as travel periods, becoming a major stress test for digital advertisers?
Seasonal travel periods are stressful because demand does not simply increase – it becomes more volatile. Search interest, destination preferences, booking windows, prices and consumer confidence can all change within days. For advertisers, this means that a campaign planned around last month’s assumptions may quickly become inefficient if it cannot adapt in real time.
Seasonality has always influenced advertising performance, but AI is changing how advertisers respond to these shifts. Rather than treating peak travel periods as isolated campaign moments, advertisers are increasingly planning around changing demand patterns throughout the year. Our latest Clear Signals Report also found that marketers are already operating across more channels and datasets than ever before, making execution — rather than simply reaching audiences — the key differentiator during periods of rapidly changing demand.
This is also reflected across Yandex Ads’ own advertising ecosystem, where AI continuously processes behavioural signals across search, maps, commerce, and content environments to help advertisers respond to changing demand as it emerges rather than after it peaks.
Travel is also no longer a simple awareness-to-booking journey. Consumers spend more time researching, comparing destinations and evaluating options before making a decision. This creates a longer consideration phase, where brands need to remain visible and relevant well before peak booking periods.
AI helps advertisers respond to these seasonal fluctuations by continuously optimising campaigns as consumer demand changes. By analysing behavioural signals and adjusting campaign delivery in real time, AI enables advertisers to maintain relevance throughout the customer journey while optimising performance against business objectives and return on investment. AI is becoming less of a post-campaign optimisation tool and more of a continuous decision-making layer that helps advertisers adjust while demand is still forming.
- How is AI shifting AdTech from audience targeting to predictive advertising?
The fundamental shift is that advertising has moved from identifying who a customer is to anticipating what they are likely to do next. Traditional audience targeting focused largely on demographics or recent actions. Today, AI enables advertisers to build a richer understanding of user behaviour by analysing signals over time to predict future intent, whether that is the likelihood of travelling, preferred destinations, or preferred booking timing.
Today’s systems don’t just segment audiences; they continuously optimise campaigns in real time. AI dynamically adjusts creative, bidding and budget allocation, while personalising messaging based on context such as location, search intent and device. This reflects a broader shift towards precision, where marketers use AI to continuously refine campaigns based on evolving customer signals rather than relying on broad audience segments. Our Clear Signals Report found that precision is increasingly becoming a stronger driver of performance than scale alone.
This evolution is already visible across modern advertising platforms. At Yandex Ads, this shift is closely connected to the development of recommendation technologies. Modern advertising systems increasingly need to analyse long-term behavioural patterns, predict conversion probability and select the most relevant message from millions of possible options within milliseconds.
Today, more than 80% of advertiser budgets on Yandex Direct are managed through AI-powered bidding and optimisation systems, reflecting how rapidly decision-making is shifting from manual execution to machine intelligence. For advertisers, this means moving from reacting to consumer behaviour to anticipating it. Instead of simply reaching audiences after interest has been expressed, predictive advertising helps brands engage customers earlier in their decision-making journey, improving both campaign efficiency and business outcomes.
- How are behaviour-based and contextual signals becoming increasingly important in a cookieless environment?
As traditional tracking signals decline, behaviour-based and contextual signals are becoming the foundation of modern digital advertising.
Rather than relying primarily on demographic attributes, advertisers are increasingly combining privacy-compliant first-party data with behavioural and contextual signals to better understand customer intent. Search behaviour, navigation patterns and content consumption provide a much richer picture of what users are interested in and where they are in their decision-making journey.
However, the real value lies not in the signals themselves, but in AI’s ability to connect and interpret them at scale. This enables advertisers to segment audiences based on real intent rather than proxy demographics, while AI models continuously identify patterns that improve targeting and campaign performance. At the same time, first-party data can be used to build lookalike audiences and personalise messaging without exposing raw user data, helping advertisers balance relevance with evolving privacy expectations.
As advertisers strengthen their first-party data strategies, AI will play an increasingly important role in turning behavioural and contextual signals into measurable business outcomes. This is particularly important as consumers engage across an increasing number of platforms, making it essential for marketers to connect fragmented signals and translate them into actionable insights.
Looking ahead, we believe the competitive advantage will increasingly lie not in owning the most data, but in extracting the most meaningful intelligence from high-quality, consent-based signals. In a cookieless environment, the advantage shifts from tracking users across the internet to understanding intent within trusted ecosystems. We believe this combination of AI, first-party intelligence and responsible data practices will define the next generation of digital advertising.
- How is AI helping advertisers navigate fragmented Southeast Asian markets and evolving consumer behaviours?
Across Southeast Asia, advertisers are navigating increasingly fragmented consumer journeys, with audiences engaging across multiple platforms, languages and cultural contexts. As these behaviours continue to evolve, AI is helping marketers make sense of this complexity by turning fragmented data signals into actionable insights.
Rather than relying on broad audience segments, AI enables advertisers to analyse behavioural and contextual signals, optimise campaigns in real time and deliver more relevant experiences across channels. This is particularly important in Southeast Asia, where consumers are active across an average of seven digital platforms and precision has become a stronger driver of performance than scale alone. AI also helps marketers make better use of first-party data, improve measurement and adapt campaigns more quickly as consumer preferences change.
Our latest Clear Signals Report found that while 77% of marketers are already piloting or implementing AI, the real difference in performance comes from how well AI is embedded into day-to-day marketing workflows. As adoption matures, we expect advertisers to move beyond isolated AI use cases towards integrating AI across audience insights, campaign optimisation and measurement. Ultimately, AI’s greatest value is not simply automating marketing tasks, but enabling marketers to make faster, more informed decisions that drive measurable business outcomes across Southeast Asia’s diverse markets.
This is especially visible in markets such as Thailand, Vietnam and Indonesia, where consumers often move between social platforms, short-form video, messaging apps, marketplaces and search before making a purchase or booking decision.
- What does the future of AI-driven advertising look like as marketers seek more measurable ROI and real-time optimisation?
The future of AI-driven advertising is moving towards intelligent, end-to-end orchestration, where marketers define strategic business objectives and AI manages execution in real time.
Current AdTech frameworks are already shifting beyond manual optimisation towards autonomous systems that continuously optimise bidding, budget allocation, traffic distribution and creative delivery across channels. Rather than managing individual campaign components separately, AI enables marketers to run integrated campaigns that adapt dynamically based on real-time performance.
At the same time, advertisers are shifting from volume-based to value-based marketing. Instead of optimising for clicks alone, AI enables campaigns to optimise towards measurable business outcomes such as bookings and purchases, concentrating investment on audiences most likely to convert and deliver stronger return on advertising spend.
Looking ahead, predictive analytics will become even more central to campaign performance. By analysing post-click engagement and customer behaviour, AI can continuously refine campaign decisions, helping marketers make faster, smarter decisions while delivering more measurable ROI.
During 2025, Yandex’s AI technologies improved overall advertising system efficiency by 38%, demonstrating how intelligent automation can generate measurable gains without requiring proportionally higher advertising investment. This is the direction the industry is moving in: advertisers will increasingly expect AI systems not only to reduce manual work, but to improve the economics of growth.
The next stage of AI-driven advertising will not be defined by more automation alone. It will be defined by better prediction, deeper measurement and stronger integration between media, data, creative and business outcomes.
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