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Enterprise Layer Intelligence
Categories/Product Management/Chattermill
Chattermill

Chattermill

Founded 2015

Analyze customer feedback from multiple channels and spot emerging issues automatically

You can use Chattermill to collect and analyze customer feedback from surveys, reviews, support tickets, and social media in a single place. The system uses machine learning models to categorize comments, track sentiment shifts, and uncover the root causes behind changing satisfaction scores. You can ask an automated analyst questions to generate reports with customer quotes, detect sudden spikes or defects, and send real-time alerts to your team. It also connects directly to external assistants and issue trackers to inform product and support decisions.

What Chattermill does

Aggregate customer feedback from multiple communication channelsTag and categorize unstructured customer commentsIdentify root causes behind NPS and CSAT fluctuationsSet up anomaly alerts for unexpected spikes in complaintsExport customer quotes and reports to Jira and LinearQuery customer sentiment data via MCP in AI chat assistantsTrack recurring reasons for product returns and cancellationsUnified feedback aggregation from surveys, reviews, social media, and support callsLyra AI model trained specifically for customer experience analysisAutomated tagging and categorization of unstructured text feedbackAnomaly detection and real-time alerts sent to Slack and emailMCP server to query customer feedback data directly inside Claude or ChatGPTRoot cause analysis linking feedback trends to NPS and CSAT score shiftsCustom dashboard building with customer quote verification

Frequently asked

ClaudeClaudeChatGPTChatGPTJiraJiraLinearLinearNotionNotionSlackSlack

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Chattermill, customer feedback analysis, voice of customer, VoC, sentiment analysis, NPS analysis, CSAT tracking, customer intelligence, Lyra AI, customer experience, survey analysis, support ticket analysis, review analysis, churn reduction, customer feedback insights, MCP server