AI technology has laid a solid foundation for the birth of context chatbot with the ability to chat like humans, helping to reframe the customer experience. In addition to the ability to enhance service performance, the contextual chatbot also helps businesses increase employee efficiency. The article below provides an overview of the context chatbot and the practical values it brings.
Why does customer service need the support of contextual chatbot?
Undeniably, a chatbot is one of the most popular automated platforms being used for customer service nowadays. Booming increasing in Vietnam since 2015, these chatbots can respond to the speed of response and customer care in a pre-built cycle. However, with the advent of new technologies besides the increasing demands of customers, it is difficult for old-generation chatbots to meet the inevitable development trend. Specifically, the stereotyped interactions through button options do not appeal to customers. Not to mention the interactive behavior is complicated and without rules. Any mishandling can lead to an incomplete experience, negatively impacting your reputation and brand awareness. The explosive development of AI has brought the generation of contextual chatbots with the ability to chat like humans. Not only the ability to handle complex conversations, grasp problems, but also the personalized experience met in full measure, bringing the level of satisfaction beyond expectations.
What is a contextual chatbot?
Contextual chatbot that based on machine learning and natural language processing (NLP) technology, providing human-like conversation with the ability to provide case-specific feedback and real-time context. Besides, the context chatbot is also known for its ability to remember preferences and personalize interactions with customers. A contextual chatbot is an outstanding solution, overcoming the weaknesses of old-generation chatbots while promoting promptness and accuracy in serving customers.
What can contextual chatbot help for business?
All the people interaction is associated with a reliable context or topic. Depending on different contexts, the answer will be different. It helps bot better control natural instances of customer interactions. For example, a customer who needs a loan advisor might begin with a question like I need to borrow cash, followed by informational questions such as What is the interest rate? How can I register for a loan application? The keyword chatbot cannot handle such general questions. Without determining the context, these chatbots cannot give a response.
The context chatbot supported by NLP technology will identify the conversation topic is to ask for a loan through the utterance I need to borrow cash. The next questions are understood related to the “Loan advice” helps chatbot give appropriate responses until the end of the context.
Seamless customer experience
In some cases, customers want to change information previously provided to a chatbot. They want to have the advice suitable for that information. If not supported by an NLP technology, the regular keyword chatbot would barely handle this case. The conversation may stop because the chatbot cannot be handle. The customer must provide the information again. It makes them feel inconvenient and leaves because it is not handled in a timely. An inconsistent experience causes customers to disappoint and may leave the business. With the ability to remember information in context, context chatbot almost solves this problem. Consider the following example of the Vaybot context chatbot built by EM&AI Virtual agent platform.
At an advanced level, this information is stored as personal preference knowledge that is used to automatically suggest subsequent interactions.
According to statistics, businesses have applied a personalization strategy that had an average turnover of 19% higher than businesses that did not. Although businesses are striving to give a personalized experience, the truth is they haven’t done yet because they are limited in collecting, organizing, and using customer observations to create experiences really relevant. Based on the ability to remember the chat history understanding through behavioral observations to give appropriate suggestions is what a context chatbot can solve for the problem of personalizing the customer experience.
For example, Bank of America chatbot Erica can offer personalized recommendations, incentives, and advice based on data analyzing customer transaction history and average monthly spending. Accordingly, Erica acts as a virtual assistant to help customers make smarter decisions. Launched in 2018, Erica quickly attracted more than 10 million users in 2019 and continues to bring more value to the problem of improving the digital banking experience.
Delivering personalized experience is the future of customer service that every business aims for. However, this problem requires a strong technology foundation and an appropriate deployment strategy. With its outstanding values, the contextual chatbot will help businesses initially achieve their personalization goals and create a breakthrough experience step by step.
Right now, for advice on the appropriate contextual chatbot deployment strategy, please leave the information here, the consultant team will contact directly to support.
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