A single, unified view of CX and operational performance.

It provides access to both raw and normalized support data, including AI-enriched fields like sentiment scores, attention levels, and escalation predictions—structured for easy integration with your BI environment.

• UI data (RDB) is refreshed every 24 hours in random batches.
• PIPE data is refreshed every 6 hours via Fivetran.

You can access SupportLogic Data Cloud via a Snowflake account. Customers can use either a SupportLogic-provided Snowflake instance or connect through their own Snowflake environment using standard APIs.

SupportLogic Data Cloud supports integration with a wide range of BI tools, including Power BI, Tableau, SAP Business Objects, and Oracle BI. Data can be routed via SQL for analysis and dashboarding.

Yes. Once integrated into your enterprise data environment, business users can create custom reports and dashboards using their preferred BI tools.

SupportLogic PIPE data is delivered in a structured, multi-table database format. The standardized model is agnostic to your ticketing system, enabling easy migration and platform flexibility.

By surfacing early warning signals such as frustration sentiment or low engagement, teams can proactively intervene before issues lead to escalations or churn.

Analyzing support conversations alongside product usage data reveals upsell opportunities, expansion signals, and recurring feature requests that inform product and account strategies.

Yes. The data pipeline is built to meet enterprise-grade governance and security standards. The architecture is scalable to support large volumes of support data without performance issues.

• Salesforce reduced low CSAT incidents by monitoring frustration signals.
• A customer prevented escalations by tracking ignored alert patterns.
• Others use sentiment and attention insights to coach agents and optimize team performance.

Yes. Customers can compare sentiment trends and engagement metrics across teams and time zones (e.g., APAC vs. US support), enabling process improvement and coaching.

While initial setup may involve data and BI teams, once integrated, the data becomes accessible to analysts, operations teams, and business users without requiring deep technical expertise.