The average consumer is probably not familiar with the term “conversational commerce”. But chances are most, if not all, have experienced it in some way if they have recently engaged with a brand.
Located at the intersection of technology, e-commerce, and customer service, conversational commerce enables brands and consumers to engage in ongoing and increased dialogue enhanced by AI and natural language models. Consumers experience conversational commerce through messaging apps and chatbots within websites and mobile applications when they buy products and services, send and receive money, and resolve issues.
For a textbook case study of conversational commerce in action, look no further than the stunning ubiquity of WeChat. More than one billion users in China and throughout the Asia-Pacific region spend an average of four hours a day within the platform ordering food, arranging rideshares, paying for products, and sharing information. All this activity occurs within a messaging-driven ecosystem powered by AI.
This level of advanced and all-encompassing conversational commerce has yet to take hold in the United States. Laws governing anti-competitive behavior may prevent the emergence of a singular powerhouse service like WeChat. However, aspects of conversational commerce have certainly begun to permeate consumer and brand interactions, particularly in the customer service context.
We know that brands are already deploying chat and other messaging solutions to address and, ideally, resolve incoming customer inquiries. However, the initial response from consumers has been somewhat cautious. Surveys suggest that most American consumers today prefer to speak with human agents instead of machines.
However, studies also reveal that more than half of American consumers are optimistic that AI will advance customer service experiences as technology and natural language capabilities improve, resulting in greater comfort and less frustration. Brands should take note of these sentiments and begin orchestrating internal systems to introduce AI-powered conversational commerce capabilities that will lead to higher-quality interactions with customers.
It starts with reengineering back-office systems to ensure that enterprise data sources, especially billing data, are readily accessible to the natural language AI models and applications that use it.
Billing data has a critical role to play as a driver of enhanced customer support in the conversational commerce dynamic. In the telecommunications industry, for example, 40% of incoming customer inquiries are related to the monthly bill. Any surprising charge or slightest deviation from what is expected typically compels a reaction. Management of these inquiries by human call center agents can be expensive. In some cases, just one customer service inquiry in a month can render that subscriber unprofitable to the company.
By connecting real-time billing data to chatbots, co-pilots, and other natural language applications deployed as the first line of customer service engagement, brands can quickly and efficiently answer questions and provide explanations that satisfy inquiries, all without the need for human intervention. Enterprises can reduce the quantity of call center agents needed and allow those who remain to manage the more complex and pressing issues.
Additionally, as these conversations are taking place, insights from billing data can be utilized by either machines or humans to upsell and cross-sell products and services based on that customer’s profile.
Aspects of WeChat’s success offer a glimpse into the AI-driven, conversation-first interactions that will become second nature for consumers and brands in the future. Integrating real-time billing data will enrich these interactions. As the quality of information improves, consumers will gain greater comfort with messaging platforms and the instinct to immediately request a human agent will fade.
To see how enterprises can use Aria’s data service to enrich and enhance customer interactions using real-time billing data, book a demo.
