Our client, one of the largest US retailers, faced difficulties with their chatbot for customer service. Ineffective prompts resulted in low customer satisfaction and a greater-than-expected number of tickets. With a thorough, quick optimization effort, the company has improved the chatbot’s performance, which resulted in increased customer satisfaction, decreased support costs, and improved operational efficiency.Â
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The Challenge
Low Customer Satisfaction: Most customers were unsatisfied with the chatbot’s experience, which led to low feedback and decreased engagement.
High Unresolved Support Tickets : The chatbot could not answer many queries, resulting in many tickets needing support staff manual intervention.
Inefficiencies in the Chatbot’s Response: The chatbot was frequently able to provide irrelevant responses to the customer’s question, which reduced the efficiency of the software.
Increased Operational Costs: The necessity to recruit and retain more customer service personnel to deal with unresolved issues significantly increased the company’s cost of operations.
Our Approach
A3Logics implemented a complete quick optimization plan to overcome the issues identified. The process consisted of the following essential steps:
- Analyzing and Diagnosis:Â The chatbot’s current performance was analyzed in depth to identify areas for improvement and discover common customer pain points that hindered its performance.
- Data collection:Â A vast amount of data was gathered through customer chatbot interaction, focusing on failed and successful queries to understand the root causes better.
- Prompt Engineering: The optimized prompts were designed to enhance the chatbot’s ability to comprehend the customer’s queries and provide more precise, relevant responses. This included:
- Refining existing prompts to provide more clarity and simplicity.
- Implementing context-aware prompts to better manage unclear or ambiguous queries.
- Implement fallback prompts to support non-supported queries and guide customers in answering their questions more efficiently.
- Test and Training:Â The chatbot was taught using data collected and new, optimized prompts and then rigorously tested to ensure better performance in real-life interactions.
- Continuous improvement:Â A feedback loop was set up to monitor the chatbot’s performance over time, allowing for continuous iteration and improvements based on the latest information and feedback from customers.
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The Solution​
A3Logics used the solutions below in its rapid optimization strategy:
Refining Existing Prompts: The prompts already in use were improved to increase their clarity and precision and assisted the chatbot in understanding customer questions.
Context-Aware Prompts: Context-aware prompts were created to efficiently handle unclear and clear queries and ensure the chatbot can provide relevant answers in response to the current conversation’s context.
Implementing Fallback-Related prompts: The Fallback prompts were designed to help with queries that are not supported, direct customers to alternative solutions, or escalate problems to human agents if necessary.
Training on Optimized Prompts: Chatbots underwent instruction using optimized prompts, which used historical data to boost their ability to learn and respond.
Rigorous Testing:Â Extensive testing was conducted to evaluate the chatbot’s performance in different situations and ensure that the customized commands resulted in precise and appropriate responses.
The Results
- 30% increase in customer satisfaction
- 40% decrease in volume of unsolved support tickets
- 50% improvement in accuracy and relevance of the chatbot’s response
- 25% reduction in the need for additional customer support staff
