Rethinking Voice of the Customer: Using AI to Measure What Really Matters in CX

Voice of the Customer (VoC) has long been a cornerstone of customer experience (CX) strategies. Traditionally, organizations have relied on surveys to capture insights about customer perceptions, satisfaction, and expectations. These surveys are versatile, cost-effective, and provide structured feedback that can be used to gauge specific metrics. Here are the primary types of surveys used in VoC programs:

• CSAT (Customer Satisfaction): Measures customer satisfaction with a specific interaction, product, or service. Typically captured through a single question such as: "How satisfied were you with your experience?"
  • Calculation: Positive responses are typically those rated as 4 or 5 on a 5-point scale.
• CES (Customer Effort Score): Evaluates the ease of interaction. For example: "How easy was it to resolve your issue today?"
  • Calculation: Effort scores are usually captured on a scale (e.g., 1 = very easy to 5 = very difficult).
• NPS (Net Promoter Score): Assesses customer loyalty and likelihood to recommend your brand, with a question like: "How likely are you to recommend our product/service to others?"
  • Calculation: Promoters are respondents who rate 9-10, while detractors rate 0-6 on a 10-point scale. Respondents rating 7-8 are considered neutral and not included in the calculation.

While effective, these methods have inherent limitations:

• Survey bias: Responses can be influenced by timing, wording, or the customer's most recent interaction.
• Limited scope: Surveys typically focus on isolated touchpoints, missing the holistic view of the customer journey.
• Delayed insights: By the time surveys are analyzed, opportunities to address pressing issues may have passed.

How Conversation Intelligence Enhances Voice of the Customer Strategies

With the introduction of AI, Conversation Intelligence is transforming how organizations capture and act on VoC data. Unlike surveys, which rely on direct customer responses, Conversation Intelligence leverages advanced AI and machine learning to analyze interactions across all channels in real-time. Here’s how it works:

• Automatic Sentiment Analysis: Modern Conversation Intelligence platforms use machine learning—not keyword-based methods—to gauge sentiment (read about the difference between keyword- vs machine learning-based sentiment analysis). By analyzing tone, context, and intent, these systems accurately identify emotions like frustration, satisfaction, or enthusiasm. However, sentiment analysis alone often falls short of providing actionable insights.

Why Sentiment Analysis Alone is Not Enough

Sentiment analysis is invaluable for understanding customer emotions but lacks the depth to explain what drives those emotions. For example, detecting frustration does not automatically reveal whether it stems from long hold times, unresolved issues, or a lack of agent empathy. This is where combining sentiment analysis with key performance indicators (KPIs) creates a more powerful and comprehensive understanding of customer interactions:

• Contextual Understanding: Sentiment analysis identifies the "what" — the emotion being expressed — but integrating KPIs uncovers the "why." For instance, negative sentiment may be tied to friction points revealed through CES scores or inefficiencies measured by agent performance metrics.
• Actionable Insights: Sentiment trends can highlight emotional patterns, but KPIs pinpoint specific areas for improvement. For example, reducing hold times or increasing first call resolution (FCR) directly addresses customer pain points.
• Enhanced Accuracy: Sentiment analysis can sometimes misinterpret sarcasm or ambiguous language. Supplementing it with KPIs like CSAT ensures a more balanced and reliable understanding.
• Holistic View: Sentiment analysis offers a snapshot of emotions, while KPIs provide a structured framework to analyze the broader customer journey.

Image: MiaRec Sentiment Analysis results

Introducing AI-powered KPIs

Building on our Voice Analytics and Conversation Intelligence expertise, MiaRec introduces a groundbreaking approach to measuring customer experience with AI-powered KPIs. These KPIs go beyond traditional sentiment analysis by incorporating advanced AI models that analyze interactions holistically, identifying customer emotions and the factors driving those emotions.

This shift allows organizations to:

• Gain a precise understanding of customer satisfaction, loyalty, and effort.
• Pinpoint actionable areas for improvement.
• Deliver data-driven insights that enhance decision-making and strategy.

Image: MiaRec IMetrics results

By leveraging this AI-driven methodology that we call MiaRec IMetrics, businesses can elevate their VoC strategies to new heights, ensuring comprehensive insights that enable transformative customer experiences.

How MiaRec IMetrics Calculates Key KPIs

1. CSAT (Customer Satisfaction):
   • AI evaluates multiple factors that contribute to satisfaction, such as:
     • Issue Resolution Score: Whether the customer’s problem was resolved effectively.
     • Agent Performance Score: The quality of communication and support the agent provides.
     • Customer Engagement Score: The level of rapport and empathy demonstrated during the interaction.
     • Efficiency Score: How quickly and efficiently the issue was addressed.
     • Extra Mile Score: Whether the agent exceeded expectations to assist the customer.
   • These factors are weighted based on their impact on satisfaction, and AI generates a detailed CSAT score with transparency and actionable insights.
2. CES (Customer Effort Score):
   • AI identifies friction points in the customer journey, such as:
     • Long hold times or delays.
     • Repetition of information across multiple agents or touchpoints.
     • Complexity of resolving the issue, such as navigating unclear procedures.
   • By analyzing language, tone, and context, AI determines how easy or difficult the interaction was for the customer and calculates a CES score accordingly.
3. NPS (Net Promoter Score):
   • AI detects loyalty signals or dissatisfaction cues within conversations. For example:
     • Positive language indicating advocacy (e.g., "I’ll definitely recommend this to my friends.").
     • Negative remarks signaling dissatisfaction or churn risk (e.g., "I don’t think I’ll use this service again.").
   • AI assesses these cues across interactions to infer a likelihood-to-recommend score, ensuring relevance and accuracy.

Image: MiaRec IMetrics descriptive diagram

Advantages of Automated KPI Measurement

Conversation Intelligence offers unparalleled benefits for measuring and acting on VoC metrics:

• Accuracy: Automated systems analyze entire conversations, eliminating human error and bias.
• Timely Insights: Quick identification of issues allows organizations to intervene before customer dissatisfaction escalates.
• Comprehensive Coverage: Every interaction is analyzed, ensuring no data is left behind.
• Scalability: Thousands of interactions can be processed simultaneously, making it feasible for large organizations.

The Future of VoC: Unified and Actionable Insights

By leveraging Conversation Intelligence, organizations can transcend the limitations of traditional VoC methods. These platforms provide a 360-degree view of customer sentiment, satisfaction, and loyalty, enabling businesses to:

• Anticipate and prevent customer churn.
• Identify areas needing improvement, such as agent training, procedure updates, or product enhancements.
• Discover opportunities for cross-selling and upselling.
• Deliver hyper-personalized experiences that exceed customer expectations.

In conclusion, while surveys remain a valuable tool, they’re no longer sufficient on their own. The integration of AI-powered conversation intelligence elevates VoC strategies, providing deeper insights, greater accuracy, and actionable intelligence to drive transformative CX outcomes.