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Connect Pylon to Count

Transform your Pylon customer support data into actionable insights with Count’s AI-powered pylon analytics dashboard. While Pylon excels at managing customer conversations, tickets, and agent workflows, Count unlocks the deeper intelligence hidden in your support operations.

Beyond Basic Reporting

Pylon’s built-in reports give you standard metrics, but they can’t answer the complex questions that drive strategic decisions: Which conversation patterns predict escalations? How do response times vary by customer segment and issue complexity? What agent behaviors correlate with higher satisfaction scores? Count’s pylon data analysis capabilities dive deep into these multi-dimensional questions.

Bespoke Analysis for Complex Support Data

Count’s AI agent writes custom SQL and Python logic tailored to your specific questions — no rigid templates. Whether you’re analyzing conversation sentiment trends across channels, identifying knowledge gaps by agent specialization, or correlating customer effort scores with resolution paths, Count crafts every query for exactly what you’re asking.

Uncover Hidden Patterns

Count runs hundreds of queries in seconds, finding insights buried in your support data that manual spreadsheet analysis would miss. It automatically handles messy data quality issues, connects with your other business systems for comprehensive analysis, and delivers presentation-ready insights that your team can act on immediately.

Stop wrestling with spreadsheets or settling for surface-level dashboards. Count transforms your Pylon data into strategic intelligence, helping you optimize support operations, improve customer satisfaction, and drive team performance with transparent, collaborative analysis your entire organization can trust.

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Metrics & Analyses You Can Run

First Response TimeTrack how quickly your support agents respond to new customer conversations in Pylon across all channels.
Resolution TimeMeasure the average time it takes to fully resolve customer support tickets from creation to closure in Pylon.
Customer Satisfaction ScoreMonitor customer satisfaction ratings and feedback scores collected through Pylon's post-conversation surveys.
Conversation Volume TrendsAnalyze patterns in incoming support conversation volume over time to identify peak periods and seasonal trends.
Agent Productivity ScoreEvaluate individual agent performance by tracking conversations handled, response times, and resolution rates in Pylon.
Issue Category DistributionUnderstand the breakdown of support requests by category or issue type to identify common customer pain points.
Channel Performance AnalysisCompare support metrics across different communication channels (email, chat, phone) to optimize channel strategy.
Escalation RateTrack the percentage of conversations that require escalation to senior agents or managers for resolution.
Customer Contact FrequencyMonitor how often individual customers reach out for support to identify high-touch accounts and potential issues.
Team Workload DistributionAnalyze how support tickets and conversations are distributed across your team to ensure balanced workloads.
Message Response Time By PriorityTrack response times segmented by ticket priority levels to ensure urgent issues receive appropriate attention.
Conversation Sentiment AnalysisAnalyze the emotional tone and sentiment of customer conversations to identify satisfaction trends and escalation risks.
Tag Usage PatternsExamine how support tags are being applied to conversations to improve categorization and workflow efficiency.
Repeat Contact RateMeasure how often customers contact support multiple times for the same issue, indicating resolution quality.
Account Health ScoreAssess overall customer account health based on support interaction patterns, satisfaction scores, and issue frequency.
Peak Hours AnalysisIdentify when your customers most frequently contact support to optimize staffing and resource allocation.
Custom Field UtilizationTrack how effectively your team uses custom fields in Pylon to capture important conversation context and data.
Cross Channel Journey AnalysisFollow customer conversations as they move between different support channels to understand the complete support journey.
Issue Recurrence RateIdentify how often the same types of issues reoccur to pinpoint systemic problems requiring permanent fixes.
Agent Specialization AnalysisAnalyze which agents excel at handling specific types of issues to optimize ticket routing and training programs.
Customer Effort ScoreMeasure how much effort customers need to expend to get their issues resolved through your Pylon support process.
Conversation Handoff AnalysisTrack when and why conversations are transferred between agents to identify handoff inefficiencies and training needs.
Support Cost Per ContactCalculate the average cost of handling each support interaction to optimize resource allocation and pricing strategies.
Knowledge Gap IdentificationIdentify areas where your support team lacks information or resources based on conversation patterns and resolution times.