What is conversational commerce?

“Hey Alexa, find me a new pair of highly rated running socks.” This phrase is one of the thousands of examples of conversational commerce at work.

Conversational commerce is reshaping the digital shopping experience altogether. This type of e-commerce creates a seamless and interactive shopping experience for consumers through conversation tools such as chatbots and voice assistants. It uses the convenience and immediacy of real-time messaging platforms to engage shoppers in dynamic and personalized conversations, transforming these messaging channels into robust sales channels.

At its core, conversational commerce enables businesses to interact with customers in a more intuitive, contextually relevant way. This interaction transcends mere product promotion to provide customer support, offer personalized recommendations and streamline transactions. The technology driving this transformation includes advanced natural language processing (NLP) and artificial intelligence (AI), which empower virtual assistants to comprehend, interpret and respond appropriately to user queries, therefore simulating human-like interactions.

In a recent report from the IBM® Institute of Business Value, only 14% of surveyed consumers described themselves as “satisfied” with the online shopping experience. These results reveal a clear need for improvement in customer experience.

Separately, more than half of the consumers surveyed say they’d want to use new technology, including virtual assistants and augmented reality (AR), as they shop. These consumers are actively open to trying new digital shopping experiences such as conversational commerce.

Conversational commerce operates through a complex combination of technology and user interactions. The process is primarily driven by NLP and AI. These technologies and other emerging technologies enable a virtual assistant or chatbot to understand, interpret and generate human language, simulating a conversational interface.

• The process starts when a user initiates contact through a messaging application integrated with the conversational commerce platform. This application might be a brand's proprietary app or channels like Facebook Messenger, WhatsApp or WeChat.

• Upon user interaction, the system's NLP component processes the input, extracting meaning from the text or speech. This process involves tokenization (breaking down text into words or phrases), part-of-speech tagging (identifying grammatical components) and semantic analysis (determining the contextual meaning).

• Simultaneously, the AI component uses machine learning algorithms to draw upon vast datasets of historical interactions, customer preferences and market trends. These algorithms then help inform the chatbot's understanding of the user's intent and context, allowing it to generate relevant and personalized responses.

• After the chatbot interprets the user's request, it can prompt further questions to clarify details or offer options, guiding the user toward their wanted action, such as product selection or purchase. Throughout this interaction, the AI learns and adapts, refining its responses based on user feedback and outcomes, therefore improving future interactions.

• Post-transaction, conversational commerce platforms also enable post-purchase support, including order tracking, returns management and customer feedback collection. By consolidating these functions within a single, intuitive interface, conversational commerce simplifies the entire customer journey, from discovery to postpurchase engagement. These types of interactions are transforming traditional e-commerce into an engaging and dynamic customer-centric experience.

The conversational commerce type of e-commerce offers a range of features that all organizations might benefit from as they seek to build better relationships with customers.

With conversational commerce, businesses can create hyperpersonalized experiences that target individual customer preferences and steer customers to relevant product recommendations.

Conversational commerce technologies can help customers to always get instant support. Customers can receive live support through conversational tools, improving their overall shopping experience.

Customers can browse products or services and complete full transactions all within a single conversation. This feature makes for a streamlined and simple purchase process.

Conversational commerce allows customers interacting with an AI agent or assistant to also interact with human agents through live chat or messaging apps. This can build a stronger connection and increase the likelihood of customer retention and a better customer satisfaction score (CSAT).

All businesses can experience the benefits of conversational commerce, spanning from better retention and conversion rates to accessing more valuable customer data.

Conversational commerce fosters authentic and personalized customer interactions, treating each customer as a valued sponsor rather than a one-time transaction. By maintaining a continuum of dialog across various channels, businesses can enhance customer experience, leading to improved loyalty and a stronger bottom line. Roughly 90% of shoppers say that good customer service is a direct reason that makes them buy again. Recognizing and appreciating customer patronage through personalized conversations builds trust and encourages repeat business.

Strategic engagement with customers at optimal times can significantly boost sales. Through proactive notifications, well-placed web widgets and sophisticated AI chatbots, businesses can suggest products, promote discounts and highlight special offers. These tactics enhance customer experience and also drive incremental revenue through upselling and cross-selling.

Conversations with customers serve as a rich source of data, offering insights into preferences, behaviors and needs. This data can be used to refine product offerings, tailor marketing strategies and improve overall business performance. Furthermore, by training chatbots by using conversational data, businesses can enhance the accuracy and effectiveness of their automated customer service.

Advanced conversational commerce platforms now offer ready-to-use automation solutions, enabling small businesses to efficiently qualify leads and authenticate customers. By routing prospects to the appropriate team members and collecting essential information through the chat, businesses can ensure seamless, personalized customer experiences from the outset.

Conversational commerce employs various strategies to combat the issue of abandoned carts, which pose a significant challenge for online retailers. Proactive messaging, rapid initial response times and in-app purchasing options within messaging platforms can significantly increase the likelihood of customers completing their transactions. By making the checkout process more convenient and engaging, businesses can minimize cart abandonment and maximize sales conversions.

An organization can implement conversational commerce across multiple channels, depending on what the organization is using to communicate with its customers. The primary applications businesses use vary, but some include messaging apps, voice assistant and chatbots.

These bots are software programs designed to simulate human-like conversation, often deployed on websites, messaging platforms or social media. AI-powered bots can handle a wide array of tasks, from answering FAQs and guiding product selections to completing transactions and offering postpurchase support. Examples include chatbots found on brand websites or within Facebook Messenger.

Integrating conversational commerce with voice-enabled devices, such as Amazon's Alexa or Google Home, enables users to engage in hands-free, voice-activated shopping experiences. Users can browse product catalogs, place orders and receive order updates through simple voice commands.

Short message service (SMS) has evolved into a powerful conversational commerce channel, allowing businesses to send personalized product recommendations, promotional offers and order updates directly to customers' mobile devices. SMS interactions can also facilitate two-factor authentication and help secure payment confirmations.

By using popular social media platforms such as Facebook, Instagram and Twitter, businesses can embed shopping capabilities within existing conversations. Through features such as shoppable posts, brands can tag products within posts or stories, allowing users to browse and purchase items without leaving the platform.

Conversational commerce also extends to visual and text-based platforms, where users can engage in silent, nonverbal interactions. For instance, visual search tools allow users to upload images of wanted products, while AI-powered recommendation engines suggest complementary or similar items based on visual cues.

IVR systems, commonly employed in call centers, represent another form of conversational commerce. By using voice prompts and spoken responses, IVR systems enable customers to browse menus, access account information and complete transactions without the need for human agents.

The conversational commerce approach can be implemented throughout many areas of an organization. It can improve customer interactions across various channels and is transforming the digital landscape. Popular use cases include:

Conversational commerce empowers businesses to offer highly customized product suggestions based on individual customer preferences, purchase history and browsing behavior. For example, Apple's Siri, an AI-powered voice assistant, can analyze a user's past interactions and provide tailored recommendations when prompted. This approach enhances customer satisfaction by addressing specific needs but also drives sales by highlighting relevant, high-margin items.

Other examples:

• Shopify Inbox is a platform that allows customers to chat with the brand across many channels and send product recommendations.

• Bloomingdales offers 1:1 personalized style recommendations through video calls and live chat.

Conversational commerce facilitates a cohesive, unified shopping journey across multiple touchpoints and channels. For example, a customer browsing products on a retailer's website might receive personalized product recommendations through text message. The customer can later convert those recommendations into a purchase through an integrated social media shopping feature. Such omnichannel experiences help ensure a consistent, seamless brand interaction, ultimately driving customer loyalty and retention.

Other examples:

• Sephora created a seamless way to communicate in their community and build relationships with customers across all of social media.

• Dolce Vita, the shoe brand, uses omnichannel reengagement to contact customers who abandoned their cart.

By using conversational commerce platforms, businesses can provide instant, personalized customer support across various customer interactions. For example, a customer experiencing difficulty with a product might initiate a chat with an AI-powered bot on a retailer's website. The bot offers the customer step-by-step troubleshooting guidance and, if necessary, help escalate the issue to a human agent. This immediate support can resolve issues swiftly while also fostering positive customer experiences, contributing to improved satisfaction and brand perception.

Other examples:

• Four Seasons Hotels and Resorts uses conversational concierge in the company’s app for its guests.

• Birk Sport is a Norwegian bike shop chain that uses an interactive conversational AI chatbot to help customers narrow down exactly what it is they’re searching for.

The conversational commerce approach enables businesses to dynamically adjust pricing and offer targeted promotions based on individual customer preferences, purchase history and market trends. For example, a fashion retailer might use AI-powered chatbots to send personalized discount codes to customers who have shown interest in a particular category of clothing or have recently abandoned their carts. These dynamic pricing and promotion strategies optimize revenue while also enhancing customer satisfaction by providing relevant, timely offers.

Other examples:

• Apple’s Siri can suggest where to find a good or service in the exact price range requested and in a specific location.

• Best Buy sends push notifications to show accessibility to their live chat assistance and uses copy that drives certain sales or promotions.

1. Prioritize user experience and accessibility:

   Design conversational commerce interfaces with a focus on usability, helping to ensure that interactions are intuitive, simple to browse and accessible across various devices and platforms. For example, optimizing chatbots for both text- and voice-based interactions caters to diverse user preferences and accessibility needs. Moreover, seamlessly integrating conversational commerce tools within chat apps and social messaging platforms enhances discoverability and user engagement.

    

2. Use advanced conversational AI capabilities:

   Employ sophisticated conversational AI techniques to facilitate natural, context-aware interactions. Use machine learning algorithms to refine chatbot responses based on historical data, enhancing accuracy and relevance over time. Also, incorporating sentiment analysis to gauge customer emotions and adapting interactions as needed, can ensure empathetic and effective communication.

    

3. Integrate conversational commerce across touchpoints:

   Help ensure a cohesive, omnichannel commerce experience by integrating conversational elements throughout the customer journey. For example, enable shoppers to add items to a virtual cart through a chatbot, then proceed to checkout within the same conversation or transition seamlessly to an online store. This cohesive approach minimizes friction and fosters a consistent, engaging brand interaction.

    

4. Continuously monitor and optimize performance:

   Regularly assess conversational commerce tools and strategies to identify areas for improvement. Analyze metrics such as customer satisfaction scores, conversion rates and average resolution times to gauge effectiveness and inform optimizations. Moreover, solicit customer feedback through surveys or in-chat polls to gain insights into evolving preferences and concerns, enabling data-driven refinements to the conversational commerce experience.

The future of conversational commerce is more or less going to be shaped by advancements in generative AI (gen AI) and other emerging technologies, heralding a new era of immersive, personalized and autonomous shopping experiences. These innovations promise to revolutionize every facet of the conversational commerce landscape, from product discovery and decision-making to purchase completion and post-sale support.

Gen AI models, capable of producing human-like text and creative content, will play a pivotal role in crafting increasingly sophisticated and engaging shopping assistants. These AI-powered shopping companions will not only provide personalized product recommendations based on individual preferences and purchase history but also generate compelling narratives and contextual insights to inform purchasing decisions.

For instance, a generative AI-driven shopping assistant might craft a captivating story around a product, highlighting its distinctive features, benefits and applications that resonate with individual customer values and aspirations.

By embracing these innovations, businesses can redefine the boundaries of customer engagement, transforming conversational commerce into a powerful catalyst for growth.