With the rapid shifts in the Asian digital consumer journey, it’s critical for brands to understand their digital data analytics strategy is evolving also. To support this change, we’ve prepared a snapshot of four areas that changed the way our consumers navigate digital, and with that share tips for how data analytics can leverage these areas –

Part 2: 

  • Connected consumers & data platforms 
  • Personalization & machine learning in marketing




There are more smart connected devices in the world than ever before and that number is growing exponentially with the emergence of Internet of Things (IoT) and 5G. From smartphones, to fitness trackers, smart TVs to smart appliances, there is more data being generated by connected consumers than ever before. With every data point generated, there is a possibility for brands to better understand the complex journey of the consumer. 

With every new media and shopping option that emerges, the connected consumer behaviour changes at an unpredictable pace. From choosing products based on streaming video ads, social influencers, online reviews or quirky customer experiences, the consumers’ decision making process has evolved. Brands need access to data beyond basic campaign data such as sales data, call centre data, survey data, email data, CRM data and more, to drive sales. 

Thankfully, customer data is more accessible than ever before via cloud based systems that can be integrated to a single source of truth.


Where data analytics helps brands: unify connected touch points in one platform


Bringing together all customer data into a single view using a data platform is one way for brands to unify, analyse, enrich and activate data across various disparate platforms and get actionable analytics. This can help brands set up a strong foundation for data analysis and be ready for new data sources that may come in the future. 

  • Brands need to always be thinking about data collection first so that they can reveal more insights about their customers. Data platforms can help unify multiple data sources so they can get a more holistic view of their customers. 
  • With as many data points unified under one single view, brands can start mapping out the various customer journeys, to improve and optimize customer experiences as well as convert new customers. With this clearer picture into the journey, better models can be built and machine learning can predict and execute personalized engagement of customers at scale.




With connected consumers’ increase in digital content consumption across most Asian markets, digital touchpoints are the first gateway for consumers to learn about products. It does not matter whether the actual purchase is made online or offline. In fact, 51% of all offline purchases are influenced by digital, shoppers are actively looking at digital information while they are shopping in the vicinity of physical stores. This means brands that don’t have online stores still have a lot to gain by engaging shoppers meaningfully on multiple platforms, including digital. 

Boston Consulting Group’s study on digital maturity identified a key attribute of sophisticated marketers — they found the best-in-class marketers engaged with consumers “to anticipate outreach, interaction, and even personalized offers—online and offline—from brands and retailers” through managing dynamic customer journeys in a cohesive and personalized manner.

Brands have seen positive returns from developing more seamless orchestration of customer experiences in the path to purchase. Bain indicates a ballpark average uplift of up to 30% in returns for brands’ investment in automation/machine learning. Cebu Pacific Air’s work which tailored real time pricing and creative automation while scaling automated personalizations across publisher readership base yielded uplift in return on ad spend. Sands China’s prediction model increased conversions after utilizing machine learning to deliver personalized ad creatives and copy automated based on customer price sensitivity across luxury or value shoppers.


Where data analytics can help brands: automate, scale and activate insights for personalization


  • Brands need to understand their customer in the moment and focus on relevance and helpful assistance in real time. It’s key to start integrating analytics and advertising variables in a central repository within privacy-safe data platforms for a  unified customer view. This allows the brands to see the whole consumer journey and provide a more unified experience. They can quickly surface insights, anticipate customers needs, then seamlessly apply those insights as they build a customer experience that is relevant and compelling. 
  • Brands also need to actively evolve business intelligence towards being more predictive to surface insights quickly in a self-learning system – whether using pre-built statistical models or developing their own. It’s critical to start considering automating processes like audience segmentation, campaign bidding and cross-channel budgeting. More importantly, it will be key for brands to ask if they can remove any friction that prevents discovery, consideration and purchase across each stage of the funnel.
  • Brands who invest in a test-iterate-learn culture across analytics, marketing, sales, creatives, and developer teams, ultimately create internal partnerships to deliver more cross-functional work with the customer-centric approach. A central data analytics team can provide the backbone for these profiling and predictive activities.  Orchestration of data-activation channels for marketing and sales supported by the customer-centric approach, whether offline or online is key. 
  • Brands benefit from exploring external partnerships to jumpstart their marketing personalization journey. They can collaborate with platform providers and publishers in their analytics strategy so that they can enhance the Return on Investment (ROI) by increasing efficiency and effectiveness of their creative messaging.

It is clear that the proliferation of connected devices and increased availability of cloud-based solutions for connecting and mapping customer behaviors and engagement across channels and devices is leading to an unprecedented understanding of consumer choice. This, in turn, is powering and rapidly evolving the dialogue between brands and consumers in more relevant, more effective ways.

Personalization, powered by better understanding of consumers, is also rapidly changing business outcomes with multiple examples of improved business efficiency manifested in significant ROI gains.

The journey however needs to start with careful and thoughtful planning – from the (1) deployment of platforms and systems allowing a single unified view of consumer behaviour across devices and channels, to (2) automation of predictive analytics processes and, ultimately, to (3) the adoption of a broader “test-and-learn” culture across the entire organization.


The first of this 2-part series covers the following:

  • Digital payments & eCommerce 
  • Chatbots & conversational commerce
IAB SEA+India Data & Analytics Committee
  • Catherine Candano, Head of Data Platform Partnerships, Google
  • Nikhil Bhardwaj, Marketing Science Agency Partner, APAC, Facebook