Language is a key element in human communications that is gradually being mastered by machines. With dynamic artificial intelligence technologies, chatbots with IBM Watson are emerging as effective models for streamlined customer interactions. Watson Assistants propel chatbots to handle contextual, business-specific, and personalized interactions to fulfill diverse customer needs. This blog post explores how chatbot development services powered by IBM Watson accelerate global business operations and customer services.
IBM Watson takes AI’s natural language processing (NLP) capabilities a step ahead with a framework supporting 13 global languages. Watson’s chatbot functionality decodes the intent behind user queries to provide contextual responses. Training chatbots and populating data has become a lot easier with Watson’s Assistant Slots. Unlike complex applications, Assistant Slots transmit user responses within a single Node.
Moreover, Watson’s AI brainpower enables businesses to identify new topics of concern among their customers. The prevailing issues surfaced hereby are analyzed by AI to provide better solutions.
Below are some most effective use cases and business benefits of building chatbots using the IBM Watson framework.
The online shopping journey is still far from ideal for online customers. With an ever-expanding spectrum of choices, it is indispensable for businesses to keep their customers engaged. However, the retail and eCommerce industry is still facing customer heat with-
a) Inconsistent cross-device experience
b) Disordered virtual carts
c) Delayed or unfulfilled orders without reasonable explanations
d) Ill-advised return and exchange policy
The presence of chatbots in online stores extensively represent brand assurance and build customer loyalty. IBM powers Watson’s shopping assistants with Watson Discovery, an AI-search technology that builds cognitive data channels to identify data value effectively.
Here’s a Watson Assistant shopping bot integrated into Slack interface.
The cognitive search and analytic algorithms underlying Watson Discovery enable retail businesses to showcase multiple products efficiently. In addition, the more user queries it handles, the more accuracy does Watson chatbot attains to address customer grievances.
We, at Oodles, train Watson’s underlying NLP technology with historic customer data to track behavioral insights and customer preferences. Our experiential knowledge in building text and voice-based AI-powered chatbots enable retail and eCommerce businesses to seamlessly manage the following tasks-
a) Track and browse eCommerce orders
b) Manage product-related queries
c) Provide virtual support assistants
d) Update product details in real-time, and more.
Enhanced user experience complemented with reduced time for task completion holds the top priority for the food ordering businesses. It is, therefore, essential for food ordering applications to handle diverse user orders with optimum accuracy and minimum time-consumption.
The organization of different fields within Watson Assistant Slots enables the chatbot to fill out the required fields for a food order. It is followed by a quick request sent back to the application for any additional information.
Here’s a demo chat with Watson Chatbot for a pizza ordering business-
Watson’s in-built artificial intelligence churns the input data to make personalized suggestions and offers to customers. In addition, the ML algorithms analyze user queries to extract real-time insights to predict, asses, and mitigate supply shortages.
The AI team at Oodles train Watson Assistant Slots with function-specific data to power accurate and quick responses. We extensively train the Watson chatbot to power the following functions while receiving food orders-
a) Show suggestion bubbles with relevant user order that closely match the respective user’s preferences.
b) Auto-correct misspelled order names by users and match it with the right menu option to prevent errors.
c) Build brand value with relevant and personalized combo suggestions to regular customers.