Redefining Conversational Commerce

Conversational tools allow businesses to interact with their customers across multiple digital channels. They are a great way to deliver a humanlike support experience, answer questions, offer buying advice, and convert leads. Conversation data can also be aggregated and processed to determine customer expectations, and personalize future engagements. But how has conversational commerce evolved over the last few years? And how do you go about implementing it?

The evolution of conversational commerce

A participant stated that the original implementations of conversational commerce were limited to chatbots with predetermined decision trees. These chatbots would often trigger conversations based on preset keywords, without truly determining user intent. For example, if a user asked, “When was the last time I went to an ATM,” the chatbot would only recognize the word “ATM” and trigger the predefined function to display nearby ATMs. Soon, organizations realized that to truly engage with their customers, they must equip conversational interfaces with adaptive intelligence. Modern AI and ML techniques were used to develop the next generation of conversational commerce tools, which can deliver hyper-personalized conversations, at scale, for millions of users.  

How do you achieve effective conversational commerce?

A speaker remarked that it’s crucial to keep the chatbot response time to a minimum. Ensure that your NLP and predictive analysis don’t keep the customer staring too long at the bubble inside the box. Strike a balance between personalization and speed. Another important thing to focus on is context. Utilize any existing data to figure out whom you are talking to and what problem they are trying to solve. Remember, whenever a person opens a chatbot, you need to reestablish your relationship with them to have meaningful conversations. Context plays a pivotal role in this regard.  

Scaling conversational commerce

An executive said that you must consider your audience size while designing conversational commerce solutions. It will help you do proper capacity planning and design an infrastructure that is resilient and scalable. They also recounted a scalability problem they faced a few years ago when the number of concurrent connections to their system grew too large. To mitigate this, they reduced the amount of data at the edge and migrated their resources to a Kubernetes cluster running on a cloud platform. The cluster has been configured to auto-scale based on need.    

The future of conversational commerce

An attendee told the audience that conversations with chatbots will be much more contextualized and valuable in the future. Instead of asking questions from customers to determine intent, chatbots will be able to recall stored data to drive personalized conversations. We can also expect conversational technologies to integrate directly with smart assistants, enabling customers to ask queries, place orders, and report feedback from anywhere, anytime.  

‍