The CFM Copilot, an AI-driven conversational assistant integrated within Sprinklr CFM, enables business users to effortlessly query survey data using natural language. This eliminates the need for advanced analytical skills or manual exploration of dashboards. Seamlessly embedded in both Analytics and Custom Dashboards, the Copilot provides highly confident and well-founded answers, which are consistently supported by actual survey responses.

The Copilot helps to reduce the complexity involved in extracting meaningful insights from survey data, particularly when multiple custom fields, filters, hierarchies, or surveys are in play. Instead of relying on specialized analysts or extensive dashboard exploration, users benefit from a self‑serve conversational interface that can interpret natural language questions and deliver accurate, verifiable insights. The goal is to enable intuitive data querying, ensure every response is grounded in actual survey inputs or visualized data, and support advanced capabilities such as trend identification, comparisons, root‑cause exploration, thematic analysis, emerging issue detection, and multi‑turn follow‑up interactions.

Business Use Cases

1. Eliminates manual analysis for common questions (trends, comparisons, distributions).

2. Bridges skills gap with a natural language, self-serve interface.

3. Grounded answers reduce risk of misinterpretation and build trust.

4. Faster decisions via concise, data-backed summaries + charts.

5. Context-aware multi-turn exploration (filters, time, segments, hierarchies).

This solution enables natural language–to–insight interaction with no specialized skills required, delivers grounded and auditable answers with sources displayed in a dedicated third pane, supports multi‑turn depth by preserving context across follow‑up questions for progressive analysis, and accelerates speed‑to‑insight by instantly rendering trend lines, comparisons, and distributions.

Examples

• Trend & Comparison for CX Scores
  Example query: “How has NPS changed across quarters for the Shell sites in the region of Arizona?”
  Delivers a trend summary with an NPS chart, along with suggested refinements such as “Compare with Texas” or “Show Tier 1 only.”

• Root Cause & Themes for Low Scores
  Example queries: “What are the top complaints from low CSAT responses?” or “Why did NPS drop in July for Tier 1 cities?”
  Applies relevant filters, surfaces themes, sentiment, and drivers, and offers follow‑ups like “Show top 3 themes by city.”

• Emerging Issues & Sentiment Monitoring
  Example queries: “Which new issues have appeared recently in open comments?” or “Has positive sentiment improved in the last 60 days?”
  Highlights emerging topics versus baseline, shows sentiment trends, and identifies impacted regions, sites, or channels.

Prerequisites

You would need CFM Copilot permissions at the App Level.

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