Increase in CSAT
30%
Decrease in agent ramp time
120%
Increase in monthly coaching sessions
300%
Company Overview
SpotOn helps thousands of restaurants and retailers manage operations and customer relationships through its platform. To deliver consistent, high-quality service at scale, the team needed a smarter way to understand what customers were saying—and to connect those insights to the business data they already trusted.
By combining structured merchant and ticket metadata from Snowflake with conversation analysis in MaestroQA, SpotOn built a scalable workflow for detecting issues, surfacing trends, and enabling faster, data-informed decisions across the organization.
The Challenge
SpotOn’s team reviewed just 3–5% of tickets manually. These limited insights didn’t scale across the business or connect to broader business impact. Reporting processes were slow and required manual effort, often without the context teams needed to prioritize fixes or improvements.
Key challenges included:
- Manual reviews covered only a small fraction of tickets
- Reporting was inconsistent and spread across multiple tools
- CRM labels were unreliable, making trend analysis difficult
- Product and Growth teams lacked clarity on true drivers of churn and missed opportunities
- Manual workflows created blind spots and delayed response times
SpotOn needed a scalable way to analyze 100% of customer conversations, unify data across systems, and deliver insights that every department could trust.
The Solution
Scalable LLM analysis enriched with business context
Working with MaestroQA, SpotOn created tailored LLM-based metrics to detect themes such as overpromising, billing confusion, hardware frustration, and churn signals. These models run across 100% of support tickets—surfacing insights that previously went unnoticed.
By ingesting merchant segmentation and case metadata from Snowflake into MaestroQA, SpotOn adds critical business context to its analysis. This makes it easy to filter, explore, and prioritize issues based on which merchants they affect and the potential impact.
SpotOn routes insights directly to:
- Product, Engineering, and Enablement through reports
- Salesforce, by creating Growth cases from flagged tickets
- Leadership teams, via Snowflake exports powering dashboards
For example, when Sales or Support miss an upsell opportunity, Snowflake scripts automatically generate a Salesforce case for the Growth team—all within an hour.
Impact
Faster decisions, wider adoption
- 100% coverage of CX interactions using MaestroQA’s LLMs, replacing the 5% sample of manual QA
- Time-to-insight reduced from 2 full days to 1.5 hours per week
- Insights routed to 13+ departments including Product, Engineering, Implementation, Success, Finance, and Growth
- Ability to filter and segment trends by merchant characteristics
- Product fixes prioritized based on merchant value, not volume
- C-level alignment improved, with Power BI dashboards built on Snowflake showing dollar impact of issues
The Outcome
Customer conversations as a strategic data source
By combining structured data from Snowflake with conversation-level insights from MaestroQA, SpotOn operationalized support interactions as a trusted source of intelligence. The workflow now scales across the business, empowering teams to act faster—with more context and confidence.
What’s Next
Expanding LLM intelligence across the customer journey
Looking ahead, SpotOn is applying the same Snowflake + MaestroQA foundation to Sales conversations—ingesting Zoom transcripts and layering in business context from Snowflake. By analyzing both what’s promised during Sales and what plays out in Support, SpotOn is building a full-loop intelligence system to surface misalignment, reduce churn risk, and improve handoffs across the customer journey.
