Pylon Metric
Customer Support
Custom Field Utilisation = Conversations with Field Populated / Total Conversations × 100
Custom Field Utilisation measures the percentage of conversations where custom fields - such as product area, issue severity, or account tier - are populated by agents or automation. High utilisation ensures reliable data for routing, reporting, and analytics. Low utilisation undermines the value of custom fields entirely.
PylonCustomer SupportCustom Field Utilisation
Custom Field Utilisation measures the percentage of conversations where custom fields - such as product area, issue severity, or account tier - are populated by agents or automation. High utilisation ensures reliable data for routing, reporting, and analytics. Low utilisation undermines the value of custom fields entirely.
How to calculate custom field utilisation
Why custom field utilisation matters for Pylon users
Custom fields are only valuable when they are consistently populated. Empty fields create blind spots in reporting, break routing rules, and make it impossible to segment performance by the dimensions that matter most to your business.
For Pylon teams, custom field utilisation is a leading indicator of data quality. If agents are not filling in fields, it may indicate that the fields are unclear, the workflow is cumbersome, or the taxonomy needs simplification.
Understand and act on custom field utilisation with KPI Tree
Extract custom field data from Pylon conversations in your warehouse and compute utilisation rates per field in KPI Tree. Link utilisation to data quality and reporting accuracy in your operations tree.
Assign RACI ownership to the support operations lead and set minimum utilisation targets, with alerts when rates drop below threshold.
Get started with your Pylon data
MCPPull metrics from Pylon directly through the Model Context Protocol.
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Related Pylon metrics
Tag Usage Patterns
Customer SupportMetric Definition
Tag Usage Patterns examines how conversation tags are applied across Pylon, measuring tag frequency, consistency, coverage, and co-occurrence. Consistent tagging is essential for reliable topic-level reporting, routing automation, and trend analysis. Inconsistent tagging undermines every downstream metric that depends on categorisation.
Issue Category Distribution
Customer SupportMetric Definition
Issue Category Distribution breaks down support conversations by topic or category - billing, technical, onboarding, feature requests - to reveal which areas generate the most volume. It informs product improvement priorities, training focus areas, and automation investment decisions.
Agent Productivity Score
Customer SupportMetric Definition
Agent Productivity Score is a balanced composite metric that evaluates support agent effectiveness across multiple dimensions including conversations handled, resolution time, first-response speed, customer satisfaction, and escalation rate. It avoids over-indexing on volume by equally weighting quality indicators.
All Pylon metrics
Account Health ScoreAgent Productivity ScoreAgent Specialisation AnalysisChannel Performance AnalysisConversation Handoff AnalysisConversation Sentiment AnalysisConversation Volume TrendsCross-Channel Journey AnalysisCustomer 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
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