AI Chatbot - Telecom Customer Support
Nation's largest 4G network with 17.1 million users, offering modern, affordable services. Our AI chatbot integrates with SMS API and knowledge base for real-time support, automating tasks to lessen human involvement.
Challenge
The client had an existing chatbot with basic NLP capabilities, but it failed to deliver the expected efficiency:
1. Manual Updates: Any new service, update, or change required manual updates to the chatbot database, consuming significant time and resources.
2. Ineffective Query Handling: Only 30% of customer queries were resolved by the chatbot, with nearly 70% being escalated to human agents. Limited knowledge among human agents resulted in varying answers to complex customer queries, leading to customer dissatisfaction. Inefficiencies increased operational costs without improving the customer experience.
3. High Dependency on Human Agents: A team of 6 human agents was needed to handle unresolved and escalated queries.
Solution
Our team was tasked with building a Proof of Concept (POC) to validate the effectiveness of an AI-powered chatbot. The solution utilized Generative AI (GenAI) and Natural Language Processing (NLP) to enhance the chatbot’s capabilities.
Phase 1: AI MVP (POC) Development (4 Weeks):
Integration: The chatbot was integrated with the client’s SMS API and a centralized knowledge base.
Data Collection: A two-week campaign encouraged customers to ask a variety of questions via SMS. These queries and their answers were aggregated to enrich the knowledge base.
Performance Validation: The POC demonstrated significant improvements, receiving positive feedback from the client’s call center and customers.
Phase 2: Full Implementation (3 months):
Automated Knowledge Updates: Enabled the chatbot to self-update with new information, eliminating the need for manual interventions.
Improved Query Handling: Enhanced chatbot performance allowed it to autonomously resolve 80% of customer queries with accurate, human-like responses.
Reduced Human Dependency: Reduced the number of human agents from 6 to just 1 for escalated cases, significantly cutting operational costs.
Results
Quickfox team helped them to reduce dependency on human agents by 80%, cutting costs while maintaining quality. Automated content updates saved hours of manual work, keeping the chatbot current and efficient. The chatbot became capable of handling 80% of customer queries independently, with accurate and human-like responses. Positive feedback from customers and internal teams highlighted the improved experience.
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