With how fast technology evolves, it can be difficult to remember what the state of ecommerce was 15 or even 10 years ago. Logistically slower, disorganized, impersonal and oftentimes insecure, it’s remarkable how exponentially better it’s gotten in the past decade.
Of course, the online shopping experience has been distinctly digital at every point in its history. This means that the process of shopping online was differentiated from the in-store experience with relatively minimal crossover. Even the notion of buying online and picking up in-store didn’t see its renaissance until just a few years ago.
But things are changing. As technology — namely AI and large language models (LLM) — has begun infiltrating the ecommerce space, we’re beginning to see an online experience that more closely resembles that in-store experience. This technology is enabling brands to provide a level of interaction and personalization at a scale that reflects the white-glove experience of in-store shopping.
It’s called conversational commerce and it’s rapidly altering the way brands and customers interact online.
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What is Conversational Commerce?
Conversational commerce is the intersection between brand-customer dialogue and the shopping experience, and it can come in multiple forms.
Picture the normal online shopping experience: a customer browses a site, perhaps leaves and is served retargeting ads, and then returns to make an eventual purchase; the customer may complete the entire buying journey without ever interacting with a representative. Conversational commerce, by contrast, is more rooted in the in-store shopping experience of a concierge or salesperson individually selling to a customer.
Conversational commerce can operate through several platforms and take on multiple modes. First, brands can deploy a live chat window to their ecommerce store, where customers can ask questions in real time with a salesperson.
Live chat also can be integrated into social selling platforms like Instagram or third-party chat apps like WhatsApp or WeChat, providing one place where customers can browse products, speak directly to a brand representative and complete purchases.
Conversational commerce has exploded in recent years due to the rise of AI and LLM-driven chatbots that enable one-to-one customer interactions without the need for a dedicated customer service representative, 24 hours a day, seven days a week.
Additionally, conversational commerce can be a sophisticated virtual assistant engine that combines voice prompts and on-screen filtering to guide users through the buying process and offer robust, personalized recommendation options. Major retailers like Walmart have been experimenting with this brand of conversational commerce for years at this point with the hopes of transforming the customer experience.
Most, if not all, retailers with an online presence are beginning to view conversational commerce as table stakes. And though it can seem daunting, there are manageable ways to get in on the action.
How to Implement Conversational Commerce into Your Digital Strategy
First and foremost, brands need to examine the various channels on which they interact with their customers and decide which are the most active. These touch points could be on an ecommerce site, through social media or via messaging apps like WhatsApp or Facebook Messenger. Mapping user journeys can pinpoint the most impactful areas to integrate a conversational sales tool.
With the most popular channels identified, a brand needs to decide which mode of conversation to deploy. For more high-value touch points — and if a retailer has the personnel to accommodate it — it might make sense to use a live agent to interact with customers. In most cases, an AI-powered chatbot will likely be the best option, though it’s also a solid strategy to utilize both: have chatbots handle simple queries while live agents tackle complex issues.
Many leading LLM platforms can be tailored to meet a brand’s specific business needs. To maximize the impact of investment, it’s best to choose a chatbot capable of handling a wide range of inquiries, from product recommendations to customer service issues.
It’s also important to integrate chatbots across all your digital channels to provide a seamless and consistent customer experience. Many social selling sites and third-party vendors offer API integrations for LLM programs that can create a uniform interaction for a customer no matter where they’re interacting with a brand. For this process to work effectively, the chatbots need to access the same customer and product data across all platforms.
Launching a conversational commerce initiative isn’t a set-and-forget project. Let your customers test out these new touch points and gather feedback to identify areas for improvement. Your team also should be tracking and analyzing key metrics such as response time, conversion rates and customer satisfaction scores to ensure that you can track the project’s success.
Lastly, it’s crucial to regularly feed new and updated data into the LLM to continuously improve its responsiveness and ultimately, the customer experience, which leads us nicely to our next point.
The Key to a Successful Conversational Commerce Strategy: Data
At the heart of any successful conversational commerce strategy is data. In order for an LLM to efficiently assist a customer, there needs to be a foundational understanding of both the customer and the product.
Customer data is crucial because it enables a chatbot to deliver personalized responses, understand individual preferences and anticipate needs. By leveraging data on past interactions, purchase history and browsing behavior, AI-powered conversational technology can offer tailored product recommendations and proactive support, significantly enhancing customer satisfaction and engagement.
Perhaps more important than customer data, however, is a unified product record. Building a personalized product conversation on a foundation of shaky product data is a recipe for disaster. Customers may not be served the products they’re looking for, the recommendation engine may not include the right product to motivate the sale; there are any number of issues that can stem from a faulty product record. Accurate, detailed and up-to-date product data ensures that AI solutions can provide precise and relevant responses to customer inquiries, including real-time inventory updates, detailed product descriptions and current promotions.
Building a conversational commerce initiative on a foundation of accurate, up-to-date customer and product data helps ensure that the quality of customer interactions is compelling, accurate, and personal.
The emergence of AI and LLM is creating the conditions for a new era in ecommerce, one that enables customers to speak to brands using natural language and receive the same hands-on service typically reserved for the in-store experience. When executed successfully, a conversational commerce program can increase customer satisfaction and drive sales, all without impacting overhead.
Jesse Creange is pivotal at Akeneo as the Head of Supplier Data Onboarding. In his capacity he oversees the processes that allow for the efficient collection, cleansing and enrichment of supplier data, streamlining its integration into Akeneo’s Product Information Management (PIM) system. Before joining Akeneo, Creange was the CEO and Co-founder of Unifai, an AI company focused on automating data onboarding for PIM systems through innovative data collection, cleansing and enrichment solutions. The acquisition of Unifai by Akeneo marked a significant milestone, bringing together Creange’s AI and data management expertise with Akeneo’s comprehensive product experience solutions.
