Pylon Metric
Customer Support
Conversation Sentiment Analysis applies natural language processing to Pylon conversation messages to classify customer sentiment as positive, neutral, or negative throughout the interaction. It tracks both the starting sentiment and the trajectory - whether the conversation improved or deteriorated - providing insight beyond end-of-conversation surveys.
PylonCustomer SupportConversation Sentiment Analysis
Conversation Sentiment Analysis applies natural language processing to Pylon conversation messages to classify customer sentiment as positive, neutral, or negative throughout the interaction. It tracks both the starting sentiment and the trajectory - whether the conversation improved or deteriorated - providing insight beyond end-of-conversation surveys.
Why conversation sentiment analysis matters for Pylon users
Survey-based metrics only capture sentiment from customers who choose to respond, creating a biased sample. Sentiment analysis captures signal from every conversation, including the silent majority who never fill out surveys.
For Pylon teams, sentiment trajectory is particularly valuable - it reveals whether agents are de-escalating frustrated customers or inadvertently making things worse. Real-time sentiment alerts enable supervisors to intervene before a negative interaction becomes a lost customer.
Understand and act on conversation sentiment analysis with KPI Tree
Apply sentiment scoring to Pylon conversation data in your pipeline and track trends in KPI Tree. Link sentiment to CSAT, escalation rate, and account health score in your support quality tree.
Assign RACI ownership to the quality assurance lead and configure alerts for conversations with rapidly declining sentiment scores.
Get started with your Pylon data
MCPPull metrics from Pylon directly through the Model Context Protocol.
Connect your existing warehouse where Pylon data already lands.
Our professional services team can build you turn-key AI foundations in a matter of weeks. Data warehouse on Snowflake/BigQuery, ELT with Fivetran, all modelled in dbt with a semantic layer.
Related Pylon metrics
Customer Satisfaction Score
Customer SupportMetric Definition
CSAT = Positive Ratings / Total Ratings × 100
Customer Satisfaction Score (CSAT) measures the percentage of customers who rate their support experience positively after a Pylon conversation. It is the most widely used indicator of support quality and directly reflects whether agents are meeting customer expectations across all channels.
Escalation Rate
Customer SupportMetric Definition
Escalation Rate = Escalated Conversations / Total Conversations × 100
Escalation Rate measures the percentage of support conversations in Pylon that are escalated from first-line agents to senior specialists, managers, or engineering teams. High escalation rates indicate gaps in first-tier training, documentation, or tooling that prevent frontline resolution.
Account Health Score
Customer SupportMetric Definition
Account Health Score is a composite metric that evaluates the support health of each customer account by weighting conversation volume trends, sentiment patterns, resolution times, and escalation frequency. It provides an early warning system for accounts whose support experience is deteriorating and may be at risk of churn.
Customer Effort Score
Customer SupportMetric Definition
CES = Sum of Effort Ratings / Total Responses
Customer Effort Score (CES) measures how much effort a customer must expend to get their issue resolved through Pylon's support channels. It is captured via post-conversation surveys and strongly predicts customer loyalty - lower effort correlates with higher retention.
Explore conversation sentiment analysis across integrations
All Pylon metrics
Account Health ScoreAgent Productivity ScoreAgent Specialisation AnalysisChannel Performance AnalysisConversation Handoff AnalysisConversation Volume TrendsCross-Channel Journey AnalysisCustom Field UtilisationCustomer Contact FrequencyCustomer Effort ScoreCustomer Satisfaction ScoreEscalation RateFirst Response TimeIssue Category DistributionIssue Recurrence RateKnowledge Gap IdentificationMessage Response Time by PriorityPeak Hours AnalysisRepeat Contact RateAverage Resolution TimeSupport Cost per ContactTag Usage PatternsTeam Workload DistributionEmpower your team to understand and act on Pylon data
Map what drives your metrics, measure progress at any grain, prove what works statistically, and deliver personalised action plans to every team member.
