Can we export analytics data?

Yes. Use the REST API and webhooks to push metrics into your BI tools. Scheduled CSV exports send reports to stakeholders. All exports include the same data visible in dashboards.

Yes. CSAT surveys are included on Professional and above. Configure triggers per channel and priority. Scores tie to tickets and agents for root cause analysis.

What metrics are available?

Ticket volume by channel, queue, and time period. SLA compliance by policy. CSAT scores and trends. Agent performance (handle time, resolution rate, CSAT). Deflection rate by article and channel. Queue depth in real time.

Can we create custom dashboards?

Yes. Drag-and-drop dashboard builder lets you customize layouts, metrics, and filters. Save multiple dashboards for different review contexts: daily ops, weekly SLA, monthly quality, quarterly business.

How is agent performance measured fairly?

Compare agents within the same queue, role, and channel. Handle time norms vary by channel (chat vs email) and complexity (Level 1 vs Level 3). Coach from trends, not individual tickets.

Can analytics data be filtered by brand?

Yes. Multi-brand workspaces support filterable analytics by brand, queue, or team. Agency workspaces see aggregate and per-client metrics with appropriate data boundaries.

How does SLA reporting handle paused tickets?

Tickets waiting on customer response are excluded from compliance calculations when status-based pause reasons are configured. This prevents unfair penalization for customer-side delays.

Which plans include analytics?

Dashboard analytics are included on all plans including trial. Advanced reporting, CSAT surveys, and API access expand on Professional and Enterprise. Scheduled exports available on Professional and above.

Can non-support teams access analytics?

Yes. Share dashboard links with read-only access for stakeholders in product, finance, and leadership. Export reports for audiences who do not need live dashboard access.

How do we build an analytics culture?

Start with weekly ops reviews using standard dashboards. Define metric definitions transparently. Celebrate improvements publicly. Train team leads on dashboard reading and data-driven decision-making.

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Support analytics that drive decisions Volume, CSAT, and agent performance

Track ticket volume, response times, resolution rates, CSAT, and agent workload from dashboards built for support leaders.

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Last updated on 2026-07-08 Written by Malik S Reviewed by Gandral V

Real-time dashboards

Monitor queue depth, SLA compliance, and channel mix without exporting spreadsheets.

Agent performance

See handle time, first response, and resolution rates per agent and team.

CSAT and feedback

Collect post-resolution ratings and tie scores back to tickets and agents.

Overview

Support analytics are only valuable when they inform decisions. A dashboard that shows ticket volume without SLA context is a number; a dashboard that shows volume trending against SLA compliance, CSAT, and agent workload is a management tool. Helpefi analytics provide real-time and historical views of everything that matters: ticket volume by channel, response and resolution times, SLA compliance, CSAT scores, agent performance, and queue health—all accessible from one interface without exporting to external BI tools. Support leaders face two analytics problems: too little data (guessing about response times) and too much data (spreadsheets nobody reads). Helpefi solves both by surfacing actionable metrics in context: weekly ops review shows volume trends, SLA breaches by category, and agent workload balance. Monthly business review adds CSAT trends, deflection rate, and staffing comparisons. Ad-hoc drill-downs from any chart to the underlying ticket list so questions like "which accounts breached SLA last week?" have answers in one click. Analytics also power improvement cycles. When SLA compliance drops, dashboards show which queue, channel, or time of day is responsible. When CSAT declines, drill to agent scores and ticket comments for root cause. When deflection rate improves, attribute the gain to specific knowledge base articles. Helpefi makes metrics transparent without making them overwhelming. This guide covers dashboard configuration, SLA and CSAT reporting, agent performance measurement, export and API access, and how analytics integrate with every feature of the platform.

Real-time dashboards for daily operations

Daily operations need real-time visibility: how many tickets are open, which ones are at risk of SLA breach, how many agents are overloaded, and where the backlog is growing. Helpefi dashboards default to a daily ops view showing open tickets by status, at-risk SLA tickets highlighted in red, queue depth by channel, and agent workload distribution. Pin your most important views: SLA breach risk queue, unassigned tickets older than four hours, agent performance ranking by first response time, and channel volume breakdown. Saved views let managers open the same dashboard every morning without reconfiguring filters. Share dashboard links with team leads so weekly reviews use the same data source. Real-time dashboards auto-refresh during business hours so managers see current queue state without manual refresh. During incidents, dashboard views help incident commanders assess queue impact and reassign resources. Post-incident dashboards show breach impact and recovery timeline. Custom dashboard layouts let you drag and drop the metrics that matter most to your team. Start with the default layout and customize as you learn which metrics drive weekly decisions. Review dashboard layout quarterly—as team and priorities change, dashboard content should evolve.

SLA compliance reporting stakeholders trust

SLA reports are most trusted when they are transparent, traceable, and consistent. Helpefi SLA reporting shows compliance percentage by policy, breach count by queue, and average response and resolution time with drill-down to individual tickets. Export SLA data for quarterly business reviews and customer QBRs using the same numbers your agents see daily. Define SLA reporting vocabulary before sharing reports externally: "met" (responded within target), "breached" (missed target), "paused" (waiting on customer, excluded from compliance calculation), and "excluded" (spam, internal tickets, tests). Consistent definitions prevent debates about what compliance means. Share definitions with customers in SLA documentation so QBR conversations use shared language. SLA compliance trending over weeks and months reveals whether staffing, automation, or process changes are working. A compliance improvement trend after AI Copilot rollout quantifies ROI. A compliance decline after holiday season signals staffing gaps for next year planning. Customer-facing SLA reports for enterprise accounts use the same data source as internal reports. Export filtered by account for QBR presentations. Share live SLA dashboard links with enterprise customers who request transparency—trust increases when customers see the same numbers your operations team uses.

CSAT measurement and quality analysis

CSAT surveys capture customer sentiment after ticket resolution. Helpefi CSAT surveys are configurable by trigger (resolution, channel, priority), frequency (every ticket, once per customer per week), and channel (email, portal, SMS). Scores tie back to tickets and agents for root cause analysis. Interpret CSAT with context: a billing queue with 4.5 CSAT and a technical support queue with 4.2 CSAT are both good—but the technical queue may handle more complex issues. Compare CSAT within queue rather than across queues. Track CSAT trends over time rather than absolute scores. A CSAT decline in a normally high-scoring queue signals a problem worth investigating. Respond to low CSAT tickets promptly. Automation can flag tickets scored 3 or below for manager review and follow-up outreach. Customer comments provide qualitative context that dashboard numbers miss. Share positive CSAT comments with agents as recognition; share constructive feedback as coaching opportunities. CSAT survey design affects response rates. Short surveys (one question plus optional comment) get higher completion than multi-question forms. Send surveys promptly after resolution—delayed surveys capture fading memory rather than fresh experience. Offer surveys in customer language when multi-language portals are configured.

Agent performance: fair metrics, better coaching

Agent performance analytics are most effective when they inform coaching, not punishment. Helpefi shows per-agent metrics: tickets resolved, average handle time, first response time, resolution time, CSAT score, SLA compliance rate, and current workload (open tickets, at-risk SLA tickets). Compare agents within the same queue and role—a Level 1 agent handling high-volume simple tickets has different metrics than a Level 3 agent handling complex escalations. Handle time varies by channel, ticket type, and time of day. A chat agent with two-minute average handle time and an email agent with fifteen-minute handle time are both performing well within their channel norms. Compare like to like: chat agent to chat agent, email agent to email agent, Level 1 to Level 1. Coach from trends, not individual tickets. An agent whose CSAT has declined over two weeks needs different support than an agent with one bad rating and otherwise excellent scores. Use dashboards in one-on-ones: share agent their own metrics alongside team averages for context. Be transparent about which metrics are tracked and how they are used. Publish agent performance criteria so everyone knows what is measured and why. Review and adjust criteria quarterly as team priorities evolve. Agents who trust the metrics system perform better than agents who fear it.

Volume analysis and capacity planning

Volume analytics show how many tickets your team handles by channel, queue, time of day, day of week, and month. Use volume trends to staff appropriately: if chat volume peaks 2-4 PM daily, schedule more chat agents during that window. If email volume spikes Monday mornings, prepare weekend backlog clearance as first task for Monday shift. Capacity planning connects volume to staffing. If each agent handles thirty tickets per day and daily volume is three hundred, you need ten full-time agents plus buffer for absences and peak periods. Helpefi volume analytics paired with agent performance metrics give staffing model inputs that HR and finance trust. Seasonal patterns help annual planning. Holiday volume, product launch spikes, and end-of-quarter renewal surges repeat yearly. Review year-over-year volume trends to predict upcoming peaks and staff accordingly. Build seasonal staffing models based on analytics, not gut feel. Volume forecasting uses historical trends to predict future volume. Three-month moving average gives short-term forecast; year-over-year comparison gives seasonal pattern. Share forecasts with leadership so hiring and budget decisions are data-informed rather than reactive.

Deflection analytics and knowledge base impact

Deflection analytics measure how many tickets never reached an agent because customers found answers in the knowledge base. Helpefi shows deflection rate per article, per collection, and per channel (portal vs chat). Track deflected count, article views, and post-deflection reopen rate to assess content effectiveness. A high-view, low-deflection article needs rewriting—customers read it but still submit tickets. A low-view, high-deflection article is excellent but underutilized—promote it in portal search or chat deflection. A high-view, high-deflection article is an asset—maintain it and link related content. Combine deflection data with ticket tags to identify content gaps. If "password reset" generates many tickets but no matching article has high deflection, write or improve that article. Track gap closure over months to show content team ROI. Report deflection trends monthly alongside volume trends. If deflection rate improves while ticket volume stays flat, you are helping existing customers more effectively but not reducing absolute queue pressure. If deflection improves and volume declines, you are reducing new ticket creation—the highest-impact outcome.

Export, API, and BI integration

Helpefi analytics are accessible through the built-in dashboard, REST API, and scheduled exports. API access lets you push metrics into your BI tools (Tableau, Looker, Metabase) and data warehouses for cross-functional reporting. Scheduled CSV exports send weekly or monthly reports to stakeholders who prefer spreadsheets. API endpoints cover ticket metrics, agent performance, SLA compliance, CSAT scores, and deflection analytics. Webhooks can send metric updates to Slack channels, Microsoft Teams, or custom webhook endpoints for real-time operational dashboards in your organization preferred tools. BI integration enables cross-functional analysis that Helpefi alone cannot provide. Compare support metrics against sales data (do high-CSAT accounts renew faster?), product data (do tickets spike after releases?), and finance data (cost per ticket by channel). The API gives your data team flexibility while the dashboard gives your ops team daily utility. Export frequency depends on stakeholder needs: weekly for ops reviews, monthly for OKR tracking, quarterly for business reviews. Automate exports to avoid manual report generation that consumes team time better spent on analysis and action.

Building an analytics-driven support culture

Analytics only improve support when the team trusts them and acts on them. Building an analytics-driven culture starts with transparency: share dashboards openly, explain metric definitions, and invite agent feedback on what metrics miss. An agent who understands why SLA compliance matters is more likely to prioritize breach-risk tickets. Create regular analytics rituals: weekly ops review (volume, SLA, queue depth), monthly quality review (CSAT, deflection, content gaps), and quarterly business review (trends, capacity, budget). Use standard dashboard views so reviews compare consistent data. Document decisions triggered by analytics so the team sees data driving action. Celebrate metric improvements publicly, but avoid metric gaming. If first-response time improves by ten percent, acknowledge the team effort—but check that CSAT did not decline from rushed replies. Balanced scorecard prevents optimizing one metric at another expense. Invest in training: teach team leads to read dashboards, ask questions of the data, and identify improvement opportunities from trends. Analytics literacy across the support team reduces dependence on a single reporting expert and embeds data-driven decision-making into daily operations.

Use case: Daily ops standup

Support manager opens the default dashboard every morning: open tickets by status, at-risk SLA tickets highlighted, unassigned tickets older than four hours flagged. Ten-minute standup assigns priorities based on dashboard data.

Use case: Weekly SLA review

Team leads review SLA compliance by queue: which policies breached, which agents had highest compliance rates, which time of day saw most breaches. Staffing adjustments made before next week.

Use case: Monthly CSAT analysis

Quality manager reviews CSAT trends: low-scoring tickets identified for follow-up, positive comments shared with agents, common complaint themes documented for product and process improvement.

Use case: Quarterly business review

Support director presents volume trends, SLA compliance, CSAT scores, and deflection rate to leadership. Year-over-year comparison and staffing model recommendations inform budget and hiring decisions.

Use case: Post-incident analytics

After a major incident, analytics show ticket volume spike, SLA breach impact, and recovery timeline. Post-incident report uses data to recommend process changes and capacity adjustments.

Data-driven support starts here

Helpefi analytics give support leaders the dashboards, reports, and API access needed to measure what matters and act on the data. Track volume, SLA, CSAT, and agent performance from one interface, build an analytics-driven culture with transparent metrics and regular review rituals, and connect support data to the tools your organization already uses. Analytics should inform decisions, not generate spreadsheets—Helpefi makes actionable metrics the default.

Frequently asked questions

Can we export analytics data?: Yes. Use the REST API and webhooks to push metrics into your BI tools. Scheduled CSV exports send reports to stakeholders. All exports include the same data visible in dashboards.
Is CSAT included?: Yes. CSAT surveys are included on Professional and above. Configure triggers per channel and priority. Scores tie to tickets and agents for root cause analysis.
What metrics are available?: Ticket volume by channel, queue, and time period. SLA compliance by policy. CSAT scores and trends. Agent performance (handle time, resolution rate, CSAT). Deflection rate by article and channel. Queue depth in real time.
Can we create custom dashboards?: Yes. Drag-and-drop dashboard builder lets you customize layouts, metrics, and filters. Save multiple dashboards for different review contexts: daily ops, weekly SLA, monthly quality, quarterly business.
How is agent performance measured fairly?: Compare agents within the same queue, role, and channel. Handle time norms vary by channel (chat vs email) and complexity (Level 1 vs Level 3). Coach from trends, not individual tickets.
Can analytics data be filtered by brand?: Yes. Multi-brand workspaces support filterable analytics by brand, queue, or team. Agency workspaces see aggregate and per-client metrics with appropriate data boundaries.
How does SLA reporting handle paused tickets?: Tickets waiting on customer response are excluded from compliance calculations when status-based pause reasons are configured. This prevents unfair penalization for customer-side delays.
Which plans include analytics?: Dashboard analytics are included on all plans including trial. Advanced reporting, CSAT surveys, and API access expand on Professional and Enterprise. Scheduled exports available on Professional and above.
Can non-support teams access analytics?: Yes. Share dashboard links with read-only access for stakeholders in product, finance, and leadership. Export reports for audiences who do not need live dashboard access.
How do we build an analytics culture?: Start with weekly ops reviews using standard dashboards. Define metric definitions transparently. Celebrate improvements publicly. Train team leads on dashboard reading and data-driven decision-making.

References & sources

Helpefi on G2 →
User reviews and ratings for Helpefi on G2.

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