Discover why AI alone isn't enough for customer sentiment analysis. Learn how combining AI with human insight helps uncover true root causes in customer sentiment and drive real improvements in customer experience.
Customer sentiment analysis has a problem: Companies are choosing between AI or human analysis, when they need both. While AI excels at processing feedback, it needs human insight to understand the subtle drivers behind customer feelings. The solution? A powerful combination of AI and human expertise that uncovers actionable insights.
Customer sentiment is a measurement that determines how your customers feel about your brand, products and services at any given moment - from delight to frustration and everything between. But why is customer sentiment important?
85% of customers want businesses to anticipate their needs. Utilising your customer feedback - surveys, call transcripts, social media channels etc - and assessing customer sentiment will give you those invaluable, qualitative insights into what your customers need for you. Effective sentiment analysis will give you a clear indication of the exact steps you need to take to improve your customer experience.
A question people ask us a lot is ‘how deep can AI actually go’? Traditional AI is fantastic at identifying broad themes in Voice of Customer data. It can rapidly categorize feedback into high-level buckets like:
And yes, it does an impressive job of parsing through mountains of data at lightning speed. This processing power is undeniably valuable in any CX/VoC strategy.
But AI isn't a solve all solution, this is where you and your team comes in...
AI on its own only scratches the surface when it comes to root cause analysis. For AI to truly be effective, it needs to be trained to understand the nuance needed for your unique business context and drill down into the various layers of customer feedback.
Most organizations get stuck at surface-level analysis when it comes to understanding customer feedback. Let's break down the three key levels of insight you can extract from qualitative data:
This is your basic categorisation - sorting feedback into broad buckets like "delivery issues" or "product quality." While essential for high-level reporting and KPI tracking, category-level insights alone don't tell you what specific actions to take.
The next level involves using AI to identify recurring themes in your feedback. This helps track issues at scale over time and spot emerging trends. However, traditional AI solutions often rely on pre-defined categories that require extensive training to update. While useful for tracking known issues, they can miss unexpected insights.
This is where the real value lies - breaking down broad themes into specific, actionable insights. Instead of just knowing you have "delivery issues," you discover exactly what's causing those issues (like packaging problems or specific delivery time windows causing customer friction).
Most text analytics tools, even those powered by sophisticated AI, stop at levels 1 and 2. They can tell you what general categories your feedback falls into and maybe some broad themes, but they struggle to extract the granular, actionable insights that drive real change.
Think of it this way: If a customer tells you they're unhappy with delivery, that's useful to know (Level 1). Understanding that it's specifically about flower deliveries helps narrow it down (Level 2). But learning that customers are experiencing water leaking from flower packaging that causes boxes to deteriorate - that's the kind of specific insight you can actually act on (Level 3).
At Wordnerds, we combine three crucial elements that mean you can dig down into your data as deeply as possible:
Here's a real-world example of how this works in practice.
A leading retailer came to us with a common problem: their existing tools could identify "delivery with flowers" as a pain point, but they couldn't pinpoint the specific issues driving customer dissatisfaction. They needed actionable insights, not just high-level themes.
Using our unique approach, we were able to help them drill down to the exact root cause of their delivery problems and implement solutions that improved customer satisfaction in record time.
Our platform makes this deep-dive analysis possible through two key features:
Start your exploration with predefined themes (like "Flower Delivery") from our theme bank, or create your own custom themes in real-time to match your specific needs. They only take 30 minutes to make, and you have full control over the training of your themes so you know exactly how your AI arrives at a specific conclusion.
Within each theme, discover organic topics ranked by frequency and sentiment. This allows you to see what customers are actually talking about, not just what you think they might be discussing.
This is where the magic happens. Armed with these AI-powered tools and their business acumen, you can drill down layer by layer into topics, exploring further until you reach the core issue.
In our retail example, it took five layers to get to the actual problem:
Delivery → Flowers → Bouquets → Rose & Peony → Plastic ripped, water leaking
This level of detail finally gave the retailer something concrete they could act on - a real solution to a real problem.
The ultimate goal of analysing customer feedback isn't just to understand what customers are saying - it's to know exactly what actions will improve their experience. This requires:
Don't settle for just knowing that customers are unhappy - uncover exactly why they're unhappy and what you can do about it. By combining AI with linguistic expertise, you can finally bridge the gap between customer feedback and actionable insights at scale.
