Less Ticket Triaging. More Customer Impact.

Because most support tools only measure what's easy to measure — ticket counts, status fields, and resolution times. The real signal is buried in the text: what customers are describing in their tickets, what agents are writing in notes, what respondents are saying in survey comments. That text is hard to analyze at scale without specialized tools. Querri reads the content of your tickets and surveys, identifies themes and patterns, and tells you what's actually driving volume — not just how many tickets landed in each pre-defined category.

Querri connects natively to HubSpot CRM (auto-sync every 4 hours), Salesforce (early access), BigQuery, Google Drive, PostgreSQL, MySQL, SQL Server, and Redshift. For support platforms like Zendesk, Freshdesk, Intercom, or Help Scout — and survey tools like Delighted or Medallia — you upload CSV or Excel exports. Querri automatically joins your exports and standardizes field formats, even when ticket statuses, agent names, or date ranges don't match across systems.

Built-in helpdesk reporting is great at showing operational metrics inside that one tool — ticket counts, SLA compliance, agent assignments. What it can't do is analyze what customers are actually saying in ticket text, or answer questions that span your helpdesk and your CRM. Querri fills that gap: it analyzes the text inside your tickets to find recurring themes, and lets you join helpdesk exports with other data sources to ask questions like 'Which customer segments are generating the most support volume?' — without a data engineering ticket.

Yes — and this is one of Querri's strongest capabilities for support teams. Upload a ticket export and Querri can cluster tickets by theme, identify sentiment patterns, surface the most common issue types even when tagging is inconsistent, and highlight which issues are growing. This is especially valuable for CSAT survey comments and open-ended feedback, where the most actionable insight is in the free text, not the score. <a href='/blog/how-to-analyze-unstructured-text-data/' class='text-tangelo-600 hover:text-tangelo-700 font-semibold underline underline-offset-2'>Read our guide to analyzing unstructured text data →</a>

Once your data is loaded, running a weekly support summary takes a few minutes. The first-time setup — loading your exports and defining your key questions — is a one-time investment. After that, save it as a scheduled workflow and the Monday morning support digest runs automatically, delivered to Google Sheets or your Querri Library without anyone having to pull exports and rebuild a spreadsheet.

Yes. Querri was built for people who understand the customer experience, not people who write SQL. Ask questions the same way you'd ask them in a QBR — 'What are the most common themes in tickets from enterprise customers this month?' — and get answers with charts, tables, and narrative. For ops managers who do know SQL: every step Querri takes is visible and inspectable, so you can verify the logic before you share a finding with the VP of CS.

Yes. Querri is SOC 2 Type II certified, with encryption in transit and at rest, tenant isolation, RBAC, SSO/MFA support, and audit logging. Customer data is never used to train AI models. Role-based access controls let you manage exactly who can see what — so agents see their own queues, team leads see team performance, and sensitive account data stays appropriately scoped.

Querri's agentic preprocessing automatically detects and fixes the formatting issues that make real-world support exports unreliable for analysis — extra header rows, inconsistent ticket status labels, duplicate records, missing timestamps, merged cells. It handles this at ingestion, before any analysis begins, so you're not spending half your day cleaning data before you can understand what's actually going on in your queue.

Customer support analytics is the practice of analyzing the data your support operation generates — ticket text, CSAT responses, volume trends, agent activity — to understand what customers are experiencing and where to improve. The challenge for most support teams isn't access to data: they generate enormous amounts of it. The challenge is that the most valuable signals — themes in ticket descriptions, patterns in survey comments, recurring issues in transcripts — are unstructured and hard to analyze at scale. Querri solves this by letting support managers analyze ticket text and combine exports from multiple sources to ask questions in plain English, without waiting for a BI report or building pivot tables by hand.