Using CSAT and Auto‑CSAT Data with Expo XT in Webex Contact Center

For any contact center manager, customer satisfaction (CSAT) is the ultimate metric. Yet, traditional methods are fundamentally flawed. With survey response rates hovering between a mere 5-30%, managers are often forced to make critical coaching, process, and strategic decisions based on a small, and potentially biased, fraction of their customer interactions.

Webex Contact Center (WxCC) tackles this head-on with a powerful two-pronged approach: traditional post-interaction surveys and the innovative, AI-driven Auto-CSAT. Together, they provide unprecedented visibility into the customer experience. But collecting data is only half the battle. The real challenge lies in transforming these separate data streams into a unified, actionable strategy.

With enhanced reporting through Expo XT UC Analytics, supervisors and engineers can bridge gaps between day-to-day management and long-term improvement plans.

Leveraging CSAT for Real-World Metrics

CSAT in WxCC is built around post-interaction surveys deployed via IVR, SMS, email, and web links. Administrators set these up using intuitive tools within Webex’s Flow Designer and Experience Management modules, automating survey distribution to capture feedback at scale.

This structured data then appears in real-time dashboards alongside metrics such as:

• Average satisfaction scores
• Abandonment and completion rates
• Queue-level and agent-level performance
• First Call Resolution (FCR) and handle time

Supervisors can use this information to spot trends, recommend agent coaching, and validate workflow changes. While these surveys provide valuable insights, the low response rates mean managers often lack the complete picture needed for confident decision-making.

Auto-CSAT: Filling the Gaps with AI

To overcome low survey participation, WxCC’s AI Assistant offers Auto-CSAT. Auto‑CSAT is a propriety AI-powered prediction system that determines satisfaction levels for voice interactions, even when no survey was completed.

By analyzing call transcripts, speech patterns (like pace and silence), and operational metadata, its proprietary machine learning models predict a satisfaction score for 100% of voice interactions with up to 95% accuracy against actual survey results.

How Auto-CSAT Works in Webex Contact Center:

Its accuracy is driven by a sophisticated Machine Learning Architecture that incorporates multiple data sources and feature types:

• Conversational Features: Speech patterns, interruptions, silence duration, and speaking rate.
• Sentiment Analysis: Real-time emotion detection from the tone and content of voice and text.
• Operational Metrics: Handle time, number of transfers, and escalation patterns.
• Resolution Indicators: Agent wrap-up codes and disposition classifications.
• Historical Context: Prior customer interaction history and agent performance data.

Trained on millions of interactions, the deep learning models generate predictions in real-time, making scores available immediately after an interaction ends. The system also provides confidence intervals, enabling managers to use the data for risk-based quality assurance sampling.

Managers and supervisors can view these AI-inferred scores in Analyzer and AI Assistant dashboards, allowing for:

• Rapid detection of dissatisfied customer interactions
• Early intervention and coaching tailored to specific needs
• Auditing of low-scoring calls for process review
• Monitoring queue, agent, and team trends over time

Important Note:

While powerful, Cisco advises that Auto-CSAT is best used for identifying trends, training opportunities, and process gaps, not for direct, individual performance management that could affect compensation. This distinction highlights an important challenge: how do you blend direct feedback with AI-inferred data for a holistic view?

Empower Deeper Data Exploration with Expo XT UC Analytics

Expo XT UC Analytics for WxCC takes reporting further with enhanced Power BI dashboards, automated Smart Narratives, and KPI visualizations that bring clarity to data across all communications channels.

Benefits include:

• Extended analytics retention: Analyze historical trends beyond native dashboard limits, enabling multiyear service evaluations.
• Predictive and anomaly detection: Identify unusual activity and anticipate demand or staffing fluctuations.
• Interactive and automated reporting: Create shareable or scheduled reports in formats suitable for executive, IT, or operations reviews.
• Advanced call and queue tracking: Drill into specifics like abandoned calls, agent wait times, and traffic peaks for more precise troubleshooting.

Instead of toggling between dashboards, Expo XT combines CSAT scores, Auto-CSAT trends, First Call Resolution (FCR), Average Handle Time (AHT), and queue performance into a single, interactive view. This allows you to instantly see how operational efficiency directly impacts customer satisfaction. For example, you can visually correlate a drop in FCR with a corresponding dip in CSAT for a specific queue, pinpointing the exact operational driver of customer dissatisfaction.

Getting Started: Your Path to Better Customer Insights

Ready to transform how you measure and improve customer satisfaction? Here's how to begin:

Whether you're a contact center manager looking for better visibility or a data analyst seeking richer insights, combining traditional CSAT with AI-powered Auto-CSAT gives you the complete picture needed to deliver exceptional customer experiences.

Ready to unlock deeper insights from your Webex Contact Center data?

Explore how Expo XT and Power BI can unify your CSAT, Auto-CSAT, and operational metrics.