Turn Pylon's B2B support data into an account health system that drives net revenue retention.

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.

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.

Agent Specialisation Analysis examines how individual agents perform across different issue categories, channels, and customer segments. It identifies natural specialisations - agents who consistently resolve billing issues faster, or who achieve higher CSAT on technical queries - enabling smarter routing decisions.

Channel Performance Analysis compares key support metrics - response time, resolution time, CSAT, and volume - across the communication channels managed by Pylon, including Slack, email, in-app chat, and social media. It reveals which channels deliver the best customer experience and where investment should be directed.

Conversation Handoff Analysis measures the frequency, reasons, and impact of conversation transfers between agents, teams, or channels within Pylon. It quantifies the additional resolution time and satisfaction impact caused by each handoff, identifying opportunities to reduce unnecessary transfers.

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.

Conversation Volume Trends tracks the number of new support conversations initiated across all Pylon channels over time. It reveals patterns, seasonal variations, and anomalies that inform staffing decisions and operational planning. Sudden spikes often correlate with product releases, incidents, or marketing campaigns.

Cross-Channel Journey Analysis maps the paths customers take across support channels - starting in Slack, moving to email, and ending in a video call, for example. It measures the frequency of channel-switching, the reasons behind it, and the impact on resolution time and satisfaction.

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.

Customer Contact Frequency measures how often individual customers or accounts initiate support conversations over a given period. It identifies high-frequency contacts who may be experiencing chronic issues, as well as customers whose contact frequency is changing - a leading indicator of satisfaction shifts.

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.

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 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.

First Response Time (FRT) measures the elapsed time from when a customer initiates a conversation to when they receive the first human reply from a support agent across any Pylon channel. Speed of initial acknowledgement strongly influences customer perception of the entire support interaction.

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.

Issue Recurrence Rate measures how frequently the same issue categories reappear across different customers or accounts over time. Unlike repeat contact rate which measures per-customer repeats, recurrence rate identifies systemic problems affecting multiple customers that warrant permanent product or process fixes.

Knowledge Gap Identification surfaces topics where support agents consistently struggle - evidenced by high escalation rates, long resolution times, or low satisfaction scores for specific issue categories. It pinpoints where documentation, training, or tooling is insufficient to enable first-tier resolution.

Message Response Time by Priority measures agent response times segmented by conversation priority level - critical, high, medium, and low. It ensures that the most urgent issues receive the fastest attention and that SLA commitments by priority tier are being met.

Peak Hours Analysis identifies the times of day, days of the week, and seasonal periods when support conversation volume is highest across all Pylon channels. It provides the demand signal needed for optimal agent scheduling and helps set customer expectations about response times.

Repeat Contact Rate measures the percentage of customers who reach out about the same or a closely related issue within a defined window after their initial conversation was resolved. It reveals incomplete resolutions, temporary workarounds, and systemic product issues that generate recurring support demand.

Resolution Time measures the total elapsed time from when a customer opens a conversation across any Pylon channel to when it is marked as resolved. It encompasses first response time, investigation, back-and-forth exchanges, and any internal waiting periods. It is a primary indicator of support efficiency.

Support Cost per Contact calculates the fully loaded cost of handling a single support interaction across all Pylon channels, including agent compensation, tooling costs, management overhead, and infrastructure. It provides the economic foundation for automation ROI calculations and channel strategy decisions.

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.

Team Workload Distribution measures how support conversations are distributed across teams and individual agents within Pylon. It highlights imbalances where some agents are overloaded while others are under-utilised, enabling fairer distribution and more sustainable working conditions.

Resolution Time measures the elapsed time between when an issue is first opened in Pylon and when it is marked as resolved or closed. Because Pylon unifies shared Slack, Microsoft Teams, email, and in-app support into a single issue record, this metric captures the full lifecycle of a B2B support request regardless of which channel it arrived through. It reflects how quickly your team moves a customer from a reported problem to a confirmed solution.