Building A Knowledge-Based Chatbot for Customer Support
Imperial, Margie (2022)
Imperial, Margie
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2022120326140
https://urn.fi/URN:NBN:fi:amk-2022120326140
Tiivistelmä
The objective of this study is to transform the current simplified version of chatbot into a smart AI chatbot that can help the case company with lead generation and will take it closer to having an automated sales funnel.
The data collection for this study includes interviews and discussions in the case company and a small-scale questionnaire with the customers. The research method for this study is action research as this requires iterative actions that can help the researcher to plan and implement in cycles based on the evaluation done at each cycle.
The study started with the current state analysis that helped to identify the issues that the case company faces within the current chatbot system. The identified issues related to a considerable manual interference, limited availability in terms of the response time, and lack of a knowledge base for more informative responses. Therefore, the focus areas of this study concentrated on improving the current version of the chatbot to implement a knowledge-based feature that can provide relevant answers 24/7 as it can understand the context and intent of the website visitors. To find relevant guidance the study explored available knowledge on the topics of chatbot building and selected especially HubSpot guidance as best practices to follow when develop the solution.
During the proposal stage, the first version of the chatbot that was developed. During the testing stage, it was deployed to the case company’s website and is currently in use since then. The validation stage included validation discussions with the key stakeholders in the case company that evaluated the testing results and suggested further improvements. The chatbot that has been launched helps the customer service agents to respond to multiple inquiries, close deals from the leads generated by the chatbot, and pass their information to the CRM system to assist the customer service agents further, when needed.
The data collection for this study includes interviews and discussions in the case company and a small-scale questionnaire with the customers. The research method for this study is action research as this requires iterative actions that can help the researcher to plan and implement in cycles based on the evaluation done at each cycle.
The study started with the current state analysis that helped to identify the issues that the case company faces within the current chatbot system. The identified issues related to a considerable manual interference, limited availability in terms of the response time, and lack of a knowledge base for more informative responses. Therefore, the focus areas of this study concentrated on improving the current version of the chatbot to implement a knowledge-based feature that can provide relevant answers 24/7 as it can understand the context and intent of the website visitors. To find relevant guidance the study explored available knowledge on the topics of chatbot building and selected especially HubSpot guidance as best practices to follow when develop the solution.
During the proposal stage, the first version of the chatbot that was developed. During the testing stage, it was deployed to the case company’s website and is currently in use since then. The validation stage included validation discussions with the key stakeholders in the case company that evaluated the testing results and suggested further improvements. The chatbot that has been launched helps the customer service agents to respond to multiple inquiries, close deals from the leads generated by the chatbot, and pass their information to the CRM system to assist the customer service agents further, when needed.