Conversational Analysis
as it should be
Analyze chatbot and call center interactions by identifying key topics, detecting sentiment, and uncovering insights to improve customer support.
3,000+ growth-obsessed teams from 90+ countries
create 5 reports in every 30 minutes at Kimola.
Extract Insights from Customer Conversations
Kimola processes large volumes of chatbot and call center conversations, detecting sentiment, key topics, and recurring issues automatically. Discover what drives cancellations, understand how products can be improved, solve common pain points, and help your sales team increase conversion rates—without manual effort.
Upload the transcript files in Excel, CSV, or TSV format and map key columns in a dataset, ensuring that every piece of feedback contributes to meaningful insights.
Sentiment and Themes with Aspect-Based Accuracy
Kimola assigns sentiment to each theme within a single conversation, ensuring no detail is lost. Even in long call center dialogues covering multiple topics, Kimola accurately breaks down feedback and assigns sentiment to each aspect—helping you understand what customers like, dislike, and why.
Learn about Aspect-based Sentiment Analysis
Kimola's Dynamic Classification™ technology understands context and generates relevant theme clusters without relying on pre-defined categories. It adapts to any dataset, eliminating the need for manual labeling or data annotation. This ensures precise categorization of unstructured feedback from online reviews, surveys, and social media—without human error.
Summaries, Pain Points, Motivations and more
Kimola processes large-scale chatbot and call center conversations, generating structured executive summaries with key topics, sentiment, and content classifications—providing clarity and actionable insights.
Learn more about Executive Summary
Kimola uncovers what drives customer satisfaction, identifies sources of frustration, and reveals how your service is perceived. AI-driven analysis detects patterns across conversations, highlighting key motivations, recurring issues, and opportunities for improvement.
Kimola analyzes customer feedback through a four-stage journey framework—Discovery, Purchase Experience, Usage & Support, and Loyalty & Advocacy. By applying its proprietary scoring algorithm, Kimola classifies feedback into these stages to reveal how customers perceive each step, measure satisfaction levels, and pinpoint the critical moments that define brand relationships.
Get Critical Conversation Insights in Your Inbox
Kimola automatically delivers email summaries of chatbot and call center analysis, highlighting key topics, sentiment trends, and recurring themes—so every important detail is safely stored and easily accessible.
These reports provide a structured breakdown of critical issues, customer concerns, and service trends, ensuring teams can act quickly without manually reviewing transcripts.
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Track & Analyze
Customer Feedback -
TRACK
Collect and centralize feedback.
ANALYZEMake sense of large-scale feedback.
INSIGHTSTurn data into meaningful insights.
Find out how Kimola can improve your feedback analysis process.
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![Kimola for Product Feedback Analysis]()
Product Feedback Analysis
Uncover customer needs, likes, and dislikes from product reviews and feedback.
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![Kimola for E-commerce Feedback Analysis]()
E-commerce Feedback Analysis
Analyze customer reviews and ratings to optimize online shopping experiences.
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![Kimola for Social Feedback Analysis]()
Social Feedback Analysis
Extract insights from social media conversations and online discussions.
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![Kimola for Open-ended Survey Analysis]()
Open-ended Survey Analysis
Make sense of free-text survey responses with AI-powered analysis.
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![Kimola for Conversational Analysis]()
Chatbot and Call Center Conversational Analysis
Understand customer sentiment and concerns from chat and call transcripts.
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![Kimola for Employee Feedback Analysis]()
Employee Feedback Analysis
Identify workplace trends and employee sentiment from internal feedback and reviews.
Frequently Asked Questions
About Conversational Analysis
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What is Conversational Analysis?
Conversational Analysis is the process of analyzing chatbot and call center interactions to extract insights. By identifying key topics, detecting sentiment, and categorizing feedback, businesses can improve customer support and operational efficiency.
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Why is Conversational Analysis important?
It helps businesses understand customer concerns, identify recurring issues, and optimize customer service. By analyzing conversations, companies can reduce churn, enhance sales strategies, and improve customer satisfaction based on real-time insights.
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How does Kimola analyze chatbot and call center conversations?
Kimola processes large volumes of conversation data using Aspect-Based Sentiment Analysis and Topic & Theme Detection. It breaks down conversations into multiple aspects, assigning sentiment and classifying themes automatically—without manual labeling.
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Can Kimola detect sentiment for different topics within the same conversation?
Yes. Kimola’s Aspect-Based Sentiment Analysis assigns sentiment to each theme within a single conversation. Even in long call center dialogues covering multiple topics, Kimola accurately detects what customers like, dislike, and why.
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What formats can I upload for Conversational Analysis?
Kimola supports Excel, CSV, and TSV formats, making it easy to import call transcripts and chatbot interactions for analysis.
Turn customer feedback into structured market research in 30+ languages—no AI training needed.
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