The challenge
Our client was faced with a significant obstacle — a shortage of customer service agents to address concerns posed by their clientele. This then led to delayed responses and frustrated clients, negatively impacting the overall customer experience.
The company needed a prompt and efficient solution to address this customer service gap effectively. That’s why they came to us with the idea to enable an AI-fueled chatbot.
The solution
Modelit embarked on a project to develop a tailored chatbot solution — prompting an extensive research and preparation phase to fully understand the client's specific needs and customer interactions.
The chatbot's primary function would be to provide content options based on user-provided keywords — helping guide users to the information they needed. It would serve as an important backup when live agents were unavailable. The chatbot would also include an area for learning and analytics — allowing for continuous improvement based on user interactions.
The Process
- Our team began with the implementation of Service Cloud through which we enabled the chatbot. It served to facilitate the ticketing process to handle customer service requests entered into the chatbot.
- Our client helped us by putting together a comprehensive content database filled with thorough blog articles to answer a wide range of customer concerns. We then used Salesforce Knowledge for storing and sharing the articles, and Einstein Article Recommendations to create the model that would best solve customer cases by suggesting relevant articles.
- To make the chatbot public, we implemented Experience Cloud. This way, customers could be connected to an agent in the event that blog content did not satisfy their needs.
- Rigorous testing and feedback loops in the project's final stage were set to ensure that the chatbot aligned perfectly with the client's requirements.
