Blog Article
AI x CRM
Customer Analysis
VoC

Voice of the Customer (VoC): AI and Empathy

Julia Gilban-Cohen
May 5, 2025

In our fast-paced, data-driven world, understanding the customer is paramount. But as AI tools proliferate, how do we ensure we don't lose the essential human touch? As overviewed in our previous blog post on AI agents, other forms of AI technology are also reshaping how we analyze and leverage voice of customer (VoC) data. When was the last time a company really heard you, not just measured you?

What is VoC?

Voice of Consumer (VoC) is more than just collecting feedback from users and/or consumers; it's about deeply understanding customers' needs, expectations, experiences and preferences in order to make informed decisions. It encompasses everything from surveys and reviews to social media mentions and support interactions. It's the raw, unfiltered truth about what your customers think and feel, and it’s incredibly valuable for upholding a business’ overarching mission – especially if the data is analyzed thoroughly.

At Glanceable, we believe VoC isn’t just about collecting faster, it’s about understanding deeper.

Challenges Businesses Face with VoC

Collecting feedback is easy. Understanding it and acting meaningfully is the real challenge. Collecting VoC data is one thing – making sense of it is another. While the desire to understand the customer is universal, the journey from collecting Voice of Consumer (VoC) data to extracting meaningful and actionable insights is often fraught with hurdles. Simply gathering feedback through surveys, social listening, or customer service interactions is just the first step. The true challenge lies in sifting through the noise, identifying genuine signals, and translating those signals into tangible improvements. In today's complex business landscape, where customer expectations are constantly evolving and feedback channels are multiplying, making sense of VoC data presents a significant set of obstacles for organizations across all sectors.

At Glanceable, we see these hurdle every day with our clients.

Businesses often grapple with:

  • Data Overload: The sheer volume of feedback can be overwhelming.
  • Subjectivity and Bias: Interpreting qualitative data requires careful consideration to avoid bias.
  • Prioritization Challenges: Transforming thousands of verbatims into clear corrective actions requires method and tools.
  • Loss of Empathy:  In the face of massive datasets, it's easy to lose sight of the individual customer's emotional experience.

When customer voices are lost in the noise, even the best products can fail to deliver real loyalty.

How AI Strengthens Voice of Customer Analysis

So how can businesses use AI to listen at scale, without losing the human story behind the data? Here's where AI shines, offering powerful solutions to these challenges. With advanced semantic analysis, AI enables:

1. Faster and Deeper Feedback Analysis

Artificial intelligence, particularly Natural Language Processing (NLP), can analyze vast amounts of text data with remarkable speed and accuracy.

  • Sentiment Analysis: Advanced technology can the emotional tone of customer feedback, identifying positive, negative, or neutral sentiments. This allows businesses to quickly spot trends and address urgent issues.
  • Emerging Theme Identification: AI can identify recurring themes and patterns in customer feedback, revealing key areas of concern or interest. This helps businesses prioritize their efforts and focus on what matters most.
  • Data Aggregation: AI can consolidate feedback from various sources, providing a comprehensive view of customer sentiment. For example, aggregating social media posts, reviews, and support tickets to give a singular view.

This is exactly why we designed Glanceable’s insight engine: to quickly highlight emerging patterns without drowning teams in irrelevant noise.

Example: A major retail brand uses AI to detect post-purchase pain points across all it’s marketplaces in just few clicks

2. Increased Efficiency & Cost Saving

Implementing AI in VoC processes can significantly streamline operations and reduce costs.

  • Automation of Repetitive Tasks: AI can automate the tedious tasks of data collection, categorization, and initial analysis, freeing up human analysts to focus on more strategic initiatives.
  • Streamlined Operations: By automating data processing, businesses can avert unnecessary steps and redundancies, leading to faster results.
  • Faster Insights: AI's ability to process vast amounts of data quickly means businesses can gain insights faster, enabling them to make timely decisions and respond to customer needs more efficiently.

Without automation, feedback risks sitting unused, costing companies valuable time to act.

Example: A clothing retailer uses AI to automatically tag and categorize customer feedback from online reviews and social media regarding garment fit (e.g., "too small," "too large," "perfect fit"). This automated tagging eliminates the need for manual review, allowing the product development team to quickly identify sizing issues and make necessary adjustments for future production runs, saving time and reducing potential return costs.

3. Personalizing the customer experience continuously

AI can also enhance the customer experience by providing personalized and responsive interactions.

  • Personalized Feedback Requests: AI can tailor feedback requests based on individual customer interactions (history and profile), ensuring relevance and increasing response rates.
  • Real-time alerts: AI can provide real-time insights into customer sentiment, enabling businesses to respond quickly to emerging issues.
  • Chatbots and Virtual Assistants: AI-powered chatbots can collect feedback and address customer inquiries in real-time, improving responsiveness and efficiency.

At Glanceable, we don’t just use AI to collect faster, we use it to understand customers more personally and meaningfully.

Example: After a spike in customer complaints about delivery delays on a major retail marketplace, an AI assistant automatically detects a surge in negative sentiment and flags "late delivery" as an emerging theme. The system immediately alerts the operations team, generates a summary of root causes from customer verbatims, and suggests proactive actions—like updating estimated delivery times on product pages and sending reassurance emails to impacted customers.

Striking the Balance

While AI offers immense benefits, it's crucial to remember that VoC is ultimately about understanding people. AI tools are powerful, but they can't replace human empathy.

  • Context is Key: AI can identify patterns, but it can't always understand the nuances of human emotion. Human analysts are needed to provide context and interpret the underlying meaning of customer feedback.
  • Human-Centered Design: AI should be used to augment, not replace, human interaction. Designing customer experiences with empathy at the forefront ensures that technology serves human needs.
  • Continuous Learning: Empathy requires ongoing learning and adaptation. As customer expectations evolve, businesses must continuously refine their VoC strategies and ensure they remain aligned with human values.

The key to successful VoC in the age of AI is finding the right balance between technology and human empathy. AI tools can provide valuable insights, but human analysts are essential for interpreting those insights and translating them into actionable strategies. By combining the power of AI with the human touch, businesses can create customer experiences that are both efficient and empathetic.

Just as AI agents in customer relations require careful orchestration to maintain a human feel, AI in VoC needs to be tempered with human understanding. By embracing AI as a tool to enhance - rather than replace - the human connection, we can build stronger, more meaningful relationships with our customers.

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