Interacting with users in a proper way is the most important requirement for the growth of any customer-oriented organization. Chatbots are now becoming an inevitable option for organizations to sustain a healthy relationship with their customers and satisfying their demands and needs. The enormous growth in technology has put pressure on chatbots to be extraordinarily intelligent in the upcoming years. Imagine a user asking a chatbot about its insurer’s website, ‘What will be my premium for my personal auto policy next year?’ and the chatbot responds to the user as ‘What do you mean by premium? Can you be clearer with your question?’ Won’t the user feel embarrassed with this response? What kind of customer experience will such a chatbot deliver!
We have numerous chatbots around us, but the question is, ‘are they trained on the domain for which they are intended to serve?’ Read on to know how important it is for chatbots to have domain training and domain intelligence well before it is introduced to serve customers.
According to NAIC , the chatbot market is expected to reach $1.25 billion globally. The report also shows that 74% of organizations say that chatbots are key enablers of their business and customer engagement strategy.
HOW TO TRAIN CHATBOTS?
There are a few prerequisites to train a chatbot. First, the service provider should decide the purpose of the chatbot. Second, an extensive analysis should be done to know all the reasons why customers want to contact customer service representatives (CSR). This will help the businesses to create a data repository that can be used to train chatbots. For example, if customers of an insurer call its CSR for enquiring details about their policy, then those utterances should be captured and turned into data sets to feed the chatbot along with suitable static or dynamic responses and proper intents. Sources say that it takes approximately 4 weeks to train a chatbot with the available data sets. You have to try to collect data from all possible utterances, we will have a huge data set that goes unmatched with what you have trained your bot with. Saving those unmatched utterances and training your bot on a day-to-day basis will be the best choice to keep your chatbot alive! Enterprises started using chatbots to serve customers for various purposes for which chatbots can be trained effectively. Bajaj Allianz is one such example as it uses a chatbot named ‘Boing’ to help and resolve customer queries successfully.
IMPORTANCE OF DOMAIN TRAINING
It is mandatory to train chatbots that are specific to a domain for them to be meaningful and to serve its purpose. For example, an insurance domain-specific chatbot should be trained on insurance terminologies right from basic to advanced levels. Because if it doesn’t know what a premium is, then building a chatbot with AI, NLP, and so on is meaningless. In brief, a chatbot should be a Subject Matter Expert (SME) of its domain to provide the desired customer experience. It is difficult and time-consuming to train chatbots with domain knowledge as it is very vast.
Therefore, the domain should be split into groups and chatbots should be trained deeper on each of those groups. For example, the insurance domain can be split into policy, claim, and billing. Further, disseminating policy, claim, and billing into granules like quotes, submissions, renewals, and so on will help to train the chatbot in an easy way (refer below diagram). As millennials are going to be the future customers of organizations in all domains, it is important for the latter to cope with the fulfillment of the needs of the former by making things happen within a fraction of a second. As per reports, 70% of millennials report positive Chatbot experiences’
Even the basic inquiry chatbots answering customer queries are expected to perform high-end functions such as transactions, processing a new submission request, suggesting users with suitable policy, assisting in problem solving, creating a claim, settling a claim, and so on.
BENEFITS & CHALLENGES
Chatbots with domain intelligence interact proactively, thereby improving customer experience and customer engagement. For example, if a customer converses with a chatbot regarding a new policy submission, then before the end of the conversation, the chatbot should remind the insured that his/her existing policy is going to expire in few days. This helps in bringing new business to the organization and also to cut costs of hiring dedicated customer service representatives and train them technically. However, it will not completely replace the role of CSRs.
Training your chatbot initially requires a huge repository of data. Collecting that data is a tedious process. Along with domain knowledge, it also requires proper NLP and other supporting features to make it succeed.
In the near future, chatbots are going to play a vital role in the customer engagement strategy of all customer-facing businesses. Although there are multiple challenges in developing a domain-intelligent chatbot, iterative training with domain knowledge along with automation would help the chatbot market bloom. Implementing these chatbots will take customer engagement and customer experience to the next level for the enterprises. The rise of messaging apps and advanced AI has led to the growth of such chatbots that could save billions for enterprises going forward.