Building a Chatbot Platform: A Cautious Approach
Chatbots are becoming increasingly popular owing to the convenience and connect they offer for customer engagement. Many enterprises are already investing in building and deploying their own chatbots, with intent to reach the customer faster and first. A chatbot platform, which caters to the specific target audience of the brand, requires a high degree of customization in its construction.
The business of brands is becoming more real-time, and customers are connecting and engaging with them round-the-clock. Moreover, today’s customers want to be served in a jiffy and in the best possible manner. Whether it is buying a suitable outfit or searching for a gift for a loved one, most customers love to engage in a dialogue with their favorite brands. It is all about the “customer experience,” which is not limited to just walking in and buying, but really looking for the right fit. Chatbots fit this bill by interacting with customers by recommending options, or pulling together a quick call-to-action.
Chatbots work in two ways: The first type of chatbot is designed to action a specific task at hand, for example, ordering a pizza. This type of chatbot platform serves as a user guide, prompting suggestions through buttons or pop-ups. The second type of chatbot is more of a virtual assistant with an ingrained ability to complete a wide range of tasks customized to the user. Many of these operate by voice inputs. The first step in building a chatbot platform is for an enterprise to understand its customer base and decide on the kind of chatbot platform it really needs. For example, a travel company will benefit by the first kind, whereas a mobile operating system calls for the second universal chatbot.
Once this business-led decision is taken, the technicalities of building the chatbot platform must be studied. Chatbots operate on the fundamentals of artificial intelligence (AI) of machine learning, with natural language processing (NLP) capabilities being an essential component. NLP is a hard nut to crack, considering it is not limited to mere words and their arrangement, but encompasses elements of tone and context too. A case in point is two negative words, which connote a positive meaning in totality. However, this may be perceived by the NLP processor as two separate negative words, translating to a double negative — far from desirable! This challenge in building a chatbot arises from language being imprecise. Chatbot developers constantly face this dilemma of how to best represent varying language situations in the correct manner. With chatbot functionalities increasing in number and complexity, this challenge has grown manifold. Moving from a web interface to a conversational interface is therefore a different ballgame altogether. How the chatbot will actually “learn” from prior interactions or from the user cannot be accurately predicted. Take the case of Microsoft’s first chatbot Tay, which used self-learning capabilities to throw racist and sexist sentiments to users. Microsoft had no option but to shut down the chatbot and issue a public statement.
This NLP conundrum brings us to the current realities of chatbot development. Developers must exercise caution and follow a conservative approach with extensive checks and balances while coding chatbots. Conceptually, the endless possibilities of chatbot interaction may seem appealing, but it can easily go wrong without the necessary precautions. These revolve around how the chatbot self-learns and what information it picks up to assimilate.