Today's customers move between websites, social media, marketplaces, and physical stores, often changing their preferences and priorities along the way. Meanwhile, privacy restrictions and the growing adoption of AI are reshaping how businesses understand customer behaviour and measure marketing performance.
At Digital Marketing Asia (DMA) Singapore 2026, three presentations explored a common challenge: how can marketers make better decisions when customer behaviour is constantly changing and the data behind those decisions is not always complete?
Here are the key takeaways from each session, along with how these ideas can be applied to Google Analytics 4 (GA4).
Customer surveys and feedback can tell us what people say they want, but their actual behaviour may tell a different story. A customer might express interest in a product without buying it, while another may quietly compare options across multiple platforms before making a purchase.
The panel discussion that includes panellists Candyse Yip, Head of E-Commerce (Singapore and Malaysia) at Fossil Group; Catherine Loh, Digital Experience Director at JLL; and Kelvin Koh, Managing Director at Tricor Singapore., highlighted why relying on a single data source can lead to an incomplete understanding of customer intent. Website interactions, marketplace activity, social conversations, and customer enquiries each reveal different aspects of the customer journey.
The challenge is not necessarily a lack of data. It is understanding how these signals fit together and what they mean for the business.
Traditional audience segmentation often relies on demographics such as age, gender, and income. While these attributes remain useful, they do not necessarily explain what a customer needs at a particular moment.
Someone browsing a product for inspiration may behave similarly to someone actively comparing prices before purchasing. Yet their intentions—and the messages most likely to resonate with them—are very different.
The panel emphasised the importance of identifying changing customer intent through behavioural patterns and conversational intelligence. AI can help analyse large volumes of customer conversations and identify emerging needs, pain points, and shifts in sentiment.
However, the process should begin with a clear business question. Without one, marketers risk finding interesting patterns that do not translate into actionable insights.
Understanding intent becomes even more challenging when customer interactions are spread across different channels.
In Southeast Asia, marketplaces can play a significant role in product discovery and sales, but brands may have limited access to the underlying customer data. Connecting these interactions with website visits, social engagement, and offline activity is therefore difficult.
There is no simple solution to this fragmentation. Businesses must make the most of the data available to them, improve collaboration between teams, and recognise that each channel serves a different purpose. Rather than forcing customers towards one preferred platform, brands should focus on delivering a consistent and relevant experience wherever customers choose to engage.
GA4 helps businesses understand what happens across their websites and apps, from product views and internal searches to add-to-cart actions and purchases. Funnel analysis and traffic-source reporting can reveal where users drop off and which interactions contribute to conversion.
However, GA4 does not automatically capture every marketplace, social, or offline interaction. Combining GA4 insights with CRM data, campaign tracking, and other available sources can provide a broader understanding of customer behaviour.
The takeaway: data becomes more valuable when we understand the context behind it—not simply when we collect more of it.
The second presentation by Vijay Anand Kunduri, Managing Director – APAC, Audience Path challenged a long-standing marketing assumption: that knowing who a customer is helps us reliably predict what they will do next.
Traditional strategies often depend on fixed audience profiles and predefined customer journeys. But human behaviour is rarely linear. Interests and priorities can change within days—or even hours—depending on immediate needs, current events, and changing circumstances.
For example, someone reading about haze conditions today may be interested in air quality, but that does not mean the same topic will remain relevant to them a month later.
The implication is clear: marketers need to look beyond static audience definitions and pay closer attention to what people are doing right now.
The presentation also highlighted the limitations of relying on the “average customer”.
A broad audience profile can help guide planning, but it may hide meaningful differences in customer needs and intent. Two people with similar demographics can behave very differently, while the same individual can have entirely different motivations depending on the situation.
Consider two travellers. One is searching for flights and accommodation for an upcoming trip, while the other is browsing destinations for a future holiday. Both are interested in travel, but one signals urgency and the other aspiration.
Treating them identically risks missing the opportunity to deliver more relevant content, messaging, and calls to action.
The presentation advocated a more adaptive approach to marketing, built around three practical shifts:
This does not mean abandoning long-term marketing plans. Instead, businesses should combine strategic direction with the flexibility to respond as customer behaviour evolves.
AI can help identify patterns and process behavioural data at scale, but human judgement remains essential to distinguish meaningful intent from coincidental activity.
GA4 provides useful behavioural signals through events such as product views, internal searches, form interactions, add-to-cart actions, and purchases. Audience creation and funnel explorations can help marketers identify patterns and understand where users are in the conversion process.
These insights can inform campaign optimisation, remarketing, and content strategy, particularly when connected to relevant marketing platforms.
However, behaviour should be treated as an indication of intent, not definitive proof. Multiple product views might signal purchase consideration—or simply browsing. The value lies in interpreting these actions alongside business context and other available evidence.
The takeaway: effective marketing is not just about reaching the right audience. It is about responding to the right signals at the right time.
In the third presentation, Ayudante APAC CEO Naohiro Yamaura explored a critical question: can businesses trust the data powering their AI investments?
AI can help predict behaviour, personalise experiences, and optimise advertising, but its effectiveness depends on the reliability of the underlying measurement. Missing conversion events and misattributed traffic can distort campaign performance and leave AI systems working with an incomplete picture.
The presentation covered approaches such as Consent Mode, Enhanced Conversions, Google Tag Gateway, and server-side Google Tag Manager, alongside the introduction of MATSUBA, Ayudante's first-party measurement service.
Another key message was that AI agents may automate technical tasks, but humans must remain responsible for defining business objectives, managing privacy, and governing how data is used.
What this means for GA4: Accurate event configuration, consent management, and regular tracking validation are essential for trustworthy reporting. Better measurement does not guarantee better outcomes, but it provides a stronger foundation for informed decisions.
For the full discussion, read [Ayudante APAC's original article on building a resilient data foundation for AI agents]
Across all three presentations, one theme stood out: successful marketing depends on more than collecting data or adopting new technology.
Marketers need to understand the context behind customer behaviour, recognise when intent changes, and ensure their measurement systems capture reliable signals.
For GA4 users, this means looking beyond surface-level metrics to understand how people interact with digital properties, where they encounter friction, and which behaviours contribute to meaningful business outcomes. It also means recognising where GA4's visibility ends and additional data sources or measurement solutions may be needed.
As AI becomes more prominent in marketing, the advantage will not necessarily belong to businesses with the most data or the newest tools. It will belong to those that ask better questions, interpret signals thoughtfully, and turn reliable insights into relevant customer experiences.
It was incredible meeting so many of you at our Digital Marketing Asia booth this year. The conversations we had—ranging from the frustrations of GA4 migration to the excitement of predictive modeling—confirmed that while the tools are getting smarter, the roadmap is getting more complex.
At Ayudante APAC, we don't just "set up" analytics; we help you find the "shouting" signals in your data. Our goal is to ensure that when your data talks, you have the infrastructure to actually listen—and the agility to act.
If your business is ready to better understand customer intent, adapt to changing behaviours, and build a reliable data foundation for AI-driven marketing, our data measurement experts are here to help you bridge the gaps and turn meaningful signals into actionable insights for better business outcomes.
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