Before anyone plans to adopt chatbots, he or she should first ask themselves, “Do I really need it?”
The plain truth is that not every company will need a chatbot. So, before following the trend blindly, design a full-proof strategy and see where chatbots fit your plans.
Chatbot fails to face and understand a user query:
There are times where a chatbot does not understand and gets stuck in the middle of a question from a user. Augmented chatbots fail as these chatbots rely on a limited way to manage and solve a query. Another reason is that a non-AI bot is embedded with an in-built rule-based conversational flow that could break if the chatbot is bombarded with unknown questions and could result in a chatbot failure.
Solution: – To overcome them, a chatbot must be in-built with a natural language processing software that can help the chatbot deal with the context of a query resulting in a human-like conversation. Ultimately, a company should provide services that enhance the contextual technology of the chatbot. It is mandatory first to have a clear picture of what the chatbot should be designed to do.
Lack of implementation
According to Forrester’s research, adding a live chat feature on your website can increase leads by an average of 40%.
Inflexible Solution Architecture
As chatbot use cases become more complex often a single-bot solution cannot support the experience well enough.
For example, a company may build a digital assistant to handle common customer queries and roll this out in an initial phase. Over time, they may decide that the bot should also have the capability for the user to transact, bring them through a multi-step journey, and add more capabilities, content, personalization, languages, and skills. By adding additional capability, you can potentially erode the capacity that the bot has to deal with the actual use case i.e. answering the common queries. The concept of fitting your use case into the bot also helps explain a phenomenon that some companies are seeing, where the conversational experience declines when they expand the functionality.
This has implications for how you architect your bot solution to meet the requirements of your use case. Will a single bot be sufficient? If not, how will you architect multiple bots so that they can be coordinated and work together to fulfill the need?
Not understanding customer emotion and intent
It is as important to express empathy via conversational AI to customers as it is to solve their problems. Users may be approaching the chatbot in a frustrated state, so when the chatbot fails to understand the customer queries, the situation is bound to get worse.
While it is not possible yet to train bots to understand and act on human emotions, you can fix it by laying out and clearing labeling intents using stronger decision-trees or machine learning (ML), Natural Language Understanding, and Natural Language Processing (NLP). This way, you can come as close as possible to interpreting the customer’s emotions and requests.