Author

Arthur du Passage

Partner at Artefact Singapore

In today’s digital age, the way businesses interact with consumers is rapidly evolving. With the advent of generative AI and tools like chatbots, the volume of conversational data is poised to skyrocket. Already a multibillion-dollar business, the global conversational artificial intelligence market is expected to soar from $10.7 billion in 2023 to $29.8 billion in 2028. This shift is about more than technology; it’s about improving the consumer experience by making it more personalized and meaningful.

The rise of conversational data

To succeed in today’s highly competitive business environment, companies must deliver carefully curated experiences to consumers. Transactional exchanges are no longer enough; instead, companies want to better understand their customers and engage with them in ways that resonate on a personal level. This commitment has led to the creation of first-party data assets that provide a comprehensive 360-degree view of each consumer. This view includes everything from membership information, to past transactions, to website behavior, to app interactions.

To make sense of this vast amount of data and gain valuable insights, companies have turned to consumer data platforms (CDPs). CDPs centralize, store, process, enrich, and activate data so that it can be integrated across multiple consumer touchpoints to create a seamless customer experience.

But there’s an important piece missing from this 360-degree view: conversational data. Despite its rich potential, data from call centers, virtual assistants, chatbots and platforms like WhatsApp Business Accounts has often been overlooked.

Generative AI: the game changer

Generative AI will change that. Not only is it driving the growth of conversational data, it’s also revolutionizing how we analyze and use it. One of the reasons conversational data has been sidelined is its inherent complexity. Because it is primarily text data, it requires specific natural language processing techniques. Earlier models struggled to extract meaningful information from conversations.

But the landscape has evolved. With the emergence of large language models (LLMs), the capacity to comprehend message context has improved dramatically. These models can now extract relevant information with a level of accuracy that was previously unattainable. Consider this: brands have devoted significant resources to developing product recommendation engines. These engines rely on consumer profiles, past transactions and browsing histories to make their suggestions. Now, imagine the depth of understanding achievable by integrating conversational data into these models. The insights gained could result in even more precise and personalized recommendations.

Designing the right conversational journey

Harnessing the power of conversational data begins with designing the right conversational journey. There are two critical components to this journey:

  • Consumer centricity: At its core, the journey should be designed to understand and meet the needs of the consumer. It should provide the information or service they’ve come to expect during the conversation. In addition, in an era where privacy is paramount, the journey should also be in compliance with data collection regulations.

  • Technical integration: On the technical front, the key is to integrate the right tools to facilitate the conversational journey. This includes template messages, generative AI interactions, and sales advisor communications. The goal is to seamlessly collect data and integrate it with the broader consumer 360 information.

Recognizing the potential of conversational data

Ultimately, as businesses strive to deliver more personalized experiences, integrating conversational data into their consumer-centric use cases is not only beneficial, it’s essential. With advances in generative AI and LLMs, the barriers that once made conversational data difficult to leverage are rapidly disappearing. It’s time for organizations to embrace the potential of conversational data and integrate it into their strategies, ensuring that they remain at the forefront of consumer engagement in the digital age.