Bot Audits
Introduction
LimeChat is at the forefront of AI-driven customer interaction solutions. We’ve developed an advanced bot benchmarking and auditing system that helps brands optimize their chatbot performance. This document outlines how our auditing feature works, its benefits, and how it can be used to improve chatbot experiences.
1. The Problem Brands Face
Many brands using chatbots face common challenges that hinder the efficiency and effectiveness of their customer service:
High Manual Effort: Brands often rely on manual audits to understand bot performance, which is both time-consuming and limits the number of conversations they can review.
Limited Insights: There’s often difficulty in identifying key customer queries, which leads to poor insights and inadequate responses.
Inconsistent Accuracy: When a bot doesn’t respond accurately, it frustrates customers and increases human intervention.
Underutilization of Data: Brands struggle to use the vast amounts of conversation data to improve the chatbot's performance continuously.
Our bot auditing feature addresses these issues by automating the auditing process, providing detailed insights, and helping brands improve their chatbot interactions.
2. What is a Bot Audit?
A bot audit is a comprehensive evaluation of a chatbot’s performance, focusing on key aspects like:
Intent Classification: How well the bot understands what the user is asking.
Entity Extraction: The ability to identify specific details (like product name or size) from the user's query.
Query Resolution: How efficiently the bot resolves user queries without human intervention.
Agent Handoff Efficiency: How smoothly the bot escalates conversations to human agents when needed.
Through this audit, we identify areas where the bot excels and where it needs improvement, ultimately helping brands optimize their chatbot interactions.
3. Benefits of Bot Audits
Our bot audit system brings numerous benefits to the table:
Improved Accuracy
By analyzing conversations and query types, we help brands refine their chatbot to provide more accurate responses, reducing frustration for users.
Faster Service
Bots can handle a larger volume of queries automatically, reducing the time customers wait for answers and allowing for quicker service.
Personalized Interactions
Better classification of customer intents means bots can engage in more personalized, relevant conversations with users.
Operational Efficiency
With automated audits, brands save time and resources previously spent on manual reviews. This allows them to focus on more strategic initiatives.
Results for Kapiva (Case Study)
After implementing our bot benchmarking system, Kapiva saw significant improvements:
98% improvement in product search functionality.
10% improvement in query resolution accuracy.
8% reduction in drop-off rates during bot conversations.
These improvements helped Kapiva offer a smoother, more efficient customer experience. (Improvements in relative numbers)
4. How to Run a Bot Audit
Running an audit is straightforward with LimeChat’s system. Here’s how you can get started:
Step-by-Step Guide
Enter Inputs:
Company ID (Helpdesk account ID).
Start and end dates.
Data quantity (up to 1000 data points).
Audit title (e.g., "Company Name").
Select audit type (RASA or GPT).
Run the Audit:
Once the necessary inputs are provided, execute the audit.
Receive Results:
After the audit completes, results are available in a Notion page, linked to a Google Sheet.
5. Understanding Audit Reports
The audit report provides a detailed analysis of the chatbot’s performance, split into three key sections:
1. Pre-Purchase Audit
Intent Classification: Shows how well the bot classifies pre-purchase queries (e.g., "I want to buy a laptop").
Entity Extraction: Evaluates how accurately the bot extracts entities like product names, sizes, etc.
Example:
User Query: "Show me the red shirts."
Intent: Find_Product
Entities Extracted: Size (Medium), Color (Red)
2. Post-Purchase Audit
Intent Classification: Assesses how well the bot handles post-purchase queries (e.g., order tracking).
Misclassification Table: Shows where the bot misclassifies queries.
Example:
User Query: "Where is my order?"
Intent: Track_Order
3. Bot Resolution Audit
Resolution Rate: Measures how well the bot resolves queries independently.
6. How to Read the Audit Report
Each audit section provides insights into the following:
Intent Classification Table:
Correctly Classified Queries: Shows how many queries were classified correctly.
Misclassified Queries: Shows where queries were misclassified, such as a pre-purchase query being classified as post-purchase.
Entity Extraction Table:
Good Product Search: Indicates when the correct product was shown and relevant entities were extracted.
Wrong Product Shown: Highlights cases where the wrong product was displayed.
Wrong Entities Extracted: Shows when the bot extracted incorrect or irrelevant information.
Quality Metrics:
Good product search: Correct product shown with accurate entity extraction.
Wrong product shown: When the wrong product or no relevant product is shown.
No entities extracted: When no relevant entities were extracted from the user's query.
7. Improvements Based on Audit Insights
The audit provides actionable insights that can be used to optimize the bot's performance. Here's how improvements can be made:
1. Pre-Purchase Issues:
No Entities Extracted: Add missing entities or enhance data groups to improve extraction.
Wrong Products Shown: Address fuzzy matching issues or train the bot on more diverse examples.
2. Post-Purchase Issues:
Misclassified Queries: Retrain the bot on misclassified queries with new, more explicit examples.
3. Bot Resolution:
Queries Not Resolved: Identify the areas where the bot failed to resolve queries and create action plans for improving the resolution rate.
8. Process Summary
Here’s a quick overview of the audit process:
Run the Audit: Execute the audit script and generate a report.
Analyze Results: Review the findings and identify areas needing improvement.
Create Action Items: Based on audit insights, create tickets for fixes and improvements, and communicate with teams for further optimization.
9. Conclusion
Our bot auditing feature is a powerful tool that helps brands improve their chatbot performance by providing in-depth insights into intent classification, entity extraction, and query resolution. By using automated audits, brands can reduce manual effort, enhance bot performance, and deliver a better customer experience. With continuous improvements based on audit insights, brands can ensure their chatbots are always performing at their best.
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