Turn Intercom conversations into a support performance system that drives retention.

Agent Performance Analysis evaluates individual support agents across multiple dimensions including response time, resolution rate, customer satisfaction scores, and conversation volume handled. It provides a balanced scorecard that avoids over-indexing on speed at the expense of quality.

Agent Utilisation Rate measures the percentage of an agent's available time spent actively handling conversations versus idle or performing administrative tasks. It helps balance workload to prevent both burnout from over-utilisation and waste from under-utilisation.

Article Effectiveness Score measures how successfully help centre articles resolve customer queries without requiring further human support. It considers article views, positive and negative reactions, and whether customers who view an article subsequently open a conversation. Effective articles deflect tickets and empower self-service.

Company Support Trends analyses conversation volume, topic distribution, and satisfaction scores at the company or account level over time. It identifies accounts with escalating support needs that may signal churn risk, as well as accounts whose declining support needs may indicate successful adoption.

Conversation Abandonment Rate measures the percentage of support conversations where the customer stops responding before the issue is resolved. It indicates friction, frustration, or perceived futility in the support experience. High abandonment often correlates with long wait times or unhelpful initial responses.

Conversation Channel Analysis breaks down support metrics by communication channel - in-app chat, email, social media, and phone - to compare volume, performance, and customer satisfaction across channels. It informs channel investment decisions and identifies where customers prefer to seek help.

Conversation Funnel Analysis maps the stages a support conversation passes through - from initiation through triage, assignment, response, and resolution - and measures conversion rates between each stage. It identifies where conversations stall, get lost, or are abandoned.

Conversation Resolution Rate measures the percentage of support conversations that are closed and remain closed - without being re-opened by the customer within a defined window. It distinguishes genuine resolutions from premature closures that leave the customer's issue unaddressed.

Conversation Volume measures the total number of new support conversations initiated within a given period. It is the foundational capacity metric for support operations, driving staffing decisions, budget planning, and automation investment. Sudden volume changes often correlate with product releases, incidents, or seasonal patterns.

Customer Effort Score (CES) measures how much effort a customer must expend to get their issue resolved through Intercom. It is typically captured via a post-conversation survey asking customers to rate the ease of their experience. Lower effort strongly correlates with higher retention and loyalty.

Customer Journey Support Analysis maps support conversation volume and topics to specific stages of the customer lifecycle - onboarding, activation, adoption, renewal, and expansion. It reveals which journey stages generate the most support friction and where product improvements could reduce support demand.

Customer Satisfaction Score (CSAT) measures the percentage of customers who rate their support experience positively after an Intercom conversation. It is the most widely used indicator of support quality and directly reflects whether agents are meeting customer expectations.

Customer Segment Support Analysis evaluates support metrics - volume, resolution time, CSAT, and topic distribution - across customer segments defined by plan tier, company size, industry, or cohort. It reveals whether high-value segments receive appropriate service levels and where segment-specific issues exist.

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. It is one of the most impactful support metrics because speed of initial acknowledgement strongly influences customer perception of the entire interaction.

Help Centre Article Views measures the number of times each article in your Intercom help centre is viewed by customers. It reveals which topics customers most frequently seek help with and which articles are discoverable. Pairing view data with effectiveness scores identifies high-traffic articles that need improvement.

Message Sentiment Analysis applies natural language processing to Intercom conversation messages to classify customer sentiment as positive, neutral, or negative. It tracks sentiment trends over time and across segments, providing an objective measure of customer emotion that complements survey-based metrics like CSAT.

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

Proactive Support Effectiveness measures the impact of outbound messages, product tours, tooltips, and banners on reducing inbound support volume. It compares support conversation rates between customers who received proactive communications and those who did not, quantifying the deflection value of proactive support.

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

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

Self-Service Success Rate measures the percentage of customers who find a resolution through help centre articles, chatbot flows, or product tours without needing to speak to a human agent. It is the ultimate measure of whether self-service investment is paying off and is a key driver of support scalability.

Support Cost per Conversation calculates the fully loaded cost of handling a single support conversation, including agent compensation, tooling costs, management overhead, and infrastructure. It provides the economic foundation for automation ROI calculations and staffing decisions.

Support Ticket Escalation Rate measures the percentage of conversations that require escalation from first-line agents to senior specialists, managers, or other departments. High escalation rates indicate gaps in first-tier training, documentation, or tooling that prevent frontline resolution.

Tag Usage Analysis examines how conversation tags are applied across Intercom, measuring tag frequency, consistency, and coverage. It ensures that the tagging taxonomy remains relevant and that agents apply tags consistently, which is essential for reliable topic-level reporting and routing.

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