Is AI the New Customer Whisperer?


The Gist

  • AI revolution. AI in decision-making revitalizes Voice of the Customer (VoC) by offering real-time, actionable insights.
  • Efficiency boost. Automation through AI frees up VoC teams to focus on strategic tasks, enhancing customer experience.
  • Ethical caveats. While AI can empower VoC processes, it raises ethical concerns that demand transparency and accountability.

Even though VoC seems to have taken a backseat in recent years, AI is bringing the concept back to life. Let’s take a look at AI in decision-making. 

With insight-gathering capabilities now well beyond the scope of what we could ever do before, the Voice of the Customer (VoC) is once again playing a crucial role in driving customer experience, loyalty and revenue growth. 

Put simply, CX and marketing teams around the world are seeing the potential AI has to revolutionize VoC by providing actionable insights that proactively meet customer demands.

What AI in Decision-Making Can Improve

Traditional VoC strategies relied on manual data collection and analysis, which can be time-consuming and often riddled with human error. AI, on the other hand, can now streamline data collection and analysis processes through automation. This, of course, frees up VoC personnel to be able to gather, understand and act on customer feedback in real-time. If your customer-facing teams haven’t incorporated AI tools and tech yet, here are three reasons you may want to consider doing so:

The Benefits 

When developed and “trained” properly, AI can provide valuable insights across various dimensions in lightning speed using the following methods.

Related Article: NLP and Text Analytics Enhance VoC Programs, Boost CX Engagement

Natural Language Processing

Natural language processing (NLP) enables AI to understand human language and interpret customer feedback from various support interactions (social media, chat, email, etc.). 

Unlike traditional methods that rely on human analysis, AI can process vast amounts of data quickly and accurately, identifying patterns, sentiment and key themes almost immediately. This enables customer teams to extract valuable insights from unstructured data and use AI in decision-making.

For instance, companies like Starbucks have used NLP to analyze social media conversations and extract insights about customer sentiment, which has helped them make informed decisions on marketing campaigns and product offerings. 


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