Want to know what the most customer-obsessed brands are focusing on for their feedback analysis in 2025? Latest trends and insights, right here...
As we enter 2025, forward-thinking businesses are reimagining their VoC Strategy. Drawing insights from our work with the UK's most customer-obsessed brands in housing, retail, travel and hospitality and financial services, we've identified the key priorities shaping customer experience this year.
Leading organisations are pursuing a singular focus: building a comprehensive view of their customer experience. Why? Because fragmented customer feedback leads to missed opportunities and costly mistakes. According to CMS Wire 38% of organisations identify fragmented data as a major obstacle to creating great customer experiences.
Organisations are focusing on getting a holistic view of their customer experience by consistently analysing what their customers are saying, everywhere they’re saying across various feedback sources:
This enables them to build a complete picture of customer sentiment.
Businesses are no longer satisfied with simply collecting and analysing customer feedback – they're focused on turning those insights into service improvements, process changes, and CX enhancements that drive real business value.
Recommended Reading; 📚 10 Essential CX Insights Statistics
So let’s dive in, here are the 7 key trends shaping how organisations will approach VoC in 2025:
Organisations are increasingly pushing for a more comprehensive view of their customer experience by analysing feedback across surveys, social media feedback, call centre transcripts, online reviews and customer service logs. While surveys remain valuable for direct customer feedback, companies are now looking to paint the full picture of CX with insights from various other sources, including:
The key challenge isn't just collecting this data – it's connecting it and analysing it meaningfully. By effectively analysing feedback from a plethora of data sources, you create a complete picture of the customer experience and identify patterns that wouldn't be visible from any single source alone. Understanding root causes is critical, and that’s where predictive analytics becomes essential.
Recommended Reading; 📚 Getting to the Root of Customer Sentiment = AI + Human Expertise
2025 is seeing a shift from reactive to proactive approaches when it comes to customer experience. Our customers know that there are millions of things they can be doing to improve CX, but being able to identify the things that will make the biggest difference is the key. This is predictive analytics and organisations are using this to:
Consider this real-world impact: One of our housing providers identified a critical pattern relating to their boiler maintenance. This was negatively impacting tenants' living situations as well as costing a lot of money and time, not to mention a huge backlog for repairs which was causing frustration.
Our client pulled together a BI (Business Intelligence) dashboard, brought in all their complaints data about boilers and overlaid it with the age of boilers across all their housing stock and worked out that when boilers were 13 years, there were multiple issues in quick succession and the cost of servicing the issues was greater than the cost of replacement.
This insight led to implementing a preemptive replacement program when boilers were reaching the 12 year age mark, demonstrating how predictive analytics can drive both cost savings and improved customer experience. It wasn’t until they combined their quant and qual data that they were able to confidently make that link between the age of boilers and when things start to statistically cost them more money.
When you can start combining your numeric data and your text data, VoC becomes even more powerful and we believe that this is the direction that companies are heading in 2025.
We have another case study with The Guiness Partnership on how they identified damp and mould risks and prioritised red-flag complaints using Wordnerds.
In 2025, organisations are integrating customer feedback directly into their BI dashboards to validate and explain quantitative metrics. While companies have become adept at tracking numerical KPIs, they're now focusing on:
However, organisations face challenges in this area, particularly with limited BI analyst availability, data integration challenges and system compatibility, but this is something that we at Wordnerds help solve with our managed services - so you can get exactly the insights you need across your quant and qualitative data.
One of the most exciting developments for 2025, especially for us at Wordnerds, is the intersection of behavioural science and data science. Organisations are moving beyond simply understanding what customers are saying to comprehending why they make certain choices and how to influence behavior positively.
This approach is particularly valuable for organisations trying to drive positive change, such as encouraging more sustainable choices or improving service utilisation. The key is understanding both:
Long gone are the days of data rich, insight poor. Companies now have so much insight, they don’t know where to start with prioritising insight. The key question isn't just 'What insights do we have?' but rather 'Which insights will drive the most significant business impact?What are the things that you can work on that can deliver the most impact the quickest with the least amount of effort?
The focus has shifted from simply gathering insights to ensuring they drive meaningful change. Leading organisations are taking concrete steps:
When we talk about meaningful change, we have specific customers who are really working on closing the feedback loop with their customers and publishing changes back to customers to show that they’re being heard, and what they say matters.
When most people mention AI, one of the first things that people think of is ChatGPT. While generative AI tools like ChatGPT have captured attention, it is definitely NOT the route to go down for robustly analysing your data.
The best practices for AI implementation in VoC feedback analysis are:
1. Robust classification and analysis tools for accurate data categorisation: This can be done using a specialist AI feedback analysis tool.
2. Transparent, verifiable analysis methods: The most important thing is that you have control over the outcomes of your data analysis.
3, AI-assisted report writing and summary generation: This is where ChatGPT really shines. Once you have accurately analysed your data and know that the conclusions your tool has come to is correct, you can then use a tool like ChatGPT to summarise your findings in a flash.
4. Human oversight and validation: This is one of the most important elements in VoC analysis. You know your customers better than anyone, and therefore you need a tool that champions this and allows you to have control over feedback analysis, but allows you to do it at scale.
The limitations of generative AI for VoC analysis are significant. As Pete Daykin, CEO of Wordnerds, explains:
“A question we get all the time and is going to be more prevalent in early 2025 is ‘why don't I just get Copilot or ChatGPT to analyse my voice of customer data?’ Generative AI is brilliant at summarising data, but it's not very good at categorising data. And there's a massive difference. A summary will tell you the kind of things that people are saying, the big themes in the data, a categorisation of data will tell you exactly how many people have exactly what problems and how that's changing over time. And when you overlay sentiment on top of that, you can look at how they're feeling about it as well. And that's the building blocks of any kind of voice of customer strategy.”
Placing a bigger importance on customer effort and listening for it in your data is going to be important in 2025. Effort usually relates to expectations having not been met and can be broken down into the different types of effort that people expend:
How complex, complicated or confusing is the experience? Are complex processes confusing your customers, are your instructions unclear or is there inconsistent information across channels? When customers need to repeatedly explain their situation, satisfaction and efficiency can really suffer.
What frustrations or stress do customers face, are you needlessly causing unnecessary emotions? Stress from unresolved issues, frustration with repeat contact and anxiety about outcomes can lead to decreased trust and loyalty.
What actions must customers take, are they constantly having to call up and chase up issues? If a customer has to contact you multiple times, repeat submissions or perform unnecessary steps - it can have a huge impact on satisfaction.
How long do processes take - are things taking too long? Resolution delays, extended wait times and being passed from pillar to post can impact retention.
By breaking down customer effort into these components, organisations can:
With Wordnerds, you have the ability to identify patterns in how customers express different types of effort and cross reference that with other pieces of feedback data. You can capture all the weird and wonderful ways that customers talk about effort - whether it’s frustration, anger, disappointment - the list goes on. And then the useful thing is then being able to overlay that with the other themes that you're categorising so you can understand what's causing that feeling, what's causing that effort.
Recommended Reading; 📚 Understand Customer Slang, Sarcasm, Bias
As we progress through 2025, the focus is clearly on making VoC programs more actionable, predictive, and impactful. Success will come to organisations that can effectively combine multiple data sources, leverage advanced analytics, and turn insights into concrete improvements in the customer experience.
Want to learn more about the various AI options for analysing your customer feedback? We think you’ll really enjoy this free guide which shows you the best AI options for your company's current situation.
