The Common Pitfall: Data First, Thinking Second
When it comes to Voice of the Customer analysis – and, frankly, most types of analysis – too often we see a rush to gather a bunch of data. Only when it is gathered is there some attempt to make sense of it. This tends to lead to output that contains a lot of information, but very little in the way of insight and actionability. Understanding how to analyse data is a critically overlooked requirement of the data capture process. This is especially true of proactive data capture methods such as surveys. Ensuring your thought processes and knowledge requirements are prioritised correctly is critical to achieving success.
The Viral Phrase That Accidentally Explained Good Analysis
You may have seen a video that went somewhat viral on LinkedIn in the latter part of 2025. It was a woman on a podcast talking about finding a job that aligns with personal interests and ambitions. It was the catchphrase line she used to explain this that sent it viral – “don’t love your job, job your love”. Her point was simply around how working life will be more productive and successful if you love what you do. Thus, to her, the priority order was figuring out your interests first, then find that job. A fairly simple concept, if a little impractical – most people don’t realistically have the luxury of drifting around and ‘finding themselves’. Nonetheless, the point that motivation and interest frequently precede the greatest working success holds.
The Comments Section Misunderstands the Message
As with most social media, it is the comments section that tells us whether people have understood a point, and this one didn’t let me down. A combination of two mocking tones was present. Firstly, people criticising the grammar – not understanding it was intended as a pithy headline talking point, not an English exam essay. Secondly, there were many sarcastic adaptations; “don’t walk your dog, dog your walk”, “don’t eat your lunch, lunch your eat”, and so on. However, the one which leads us back to our topic was “don’t analyse your data, data your analyse”.
What this person has shown me is that they don’t understand good analysis. They’ve accidentally stumbled upon the right answer, whilst thinking it was a joke. For the sake of grammar, a slight re-word to “don’t analyse your data, data your analysis” brings us to the true order of priority. Excellence in most fields is based on technique. A great tennis player doesn’t just walk up to the line and serve the ball. They commit thousands of practise hours, perfecting exactly where to stand on the line, how to position their feet, how to hold the racket, how high to toss the ball, etc. Similarly, good analysis isn’t based on luck, it is driven by developing technique, the methodologies needed to analyse are honed.
Methodology Comes Before Data
As such, the concept and task of analysis is one of honed methodologies. If we refer back to our statement – “don’t analyse your data, data your analysis” – we have two entities, the data and the analysis, which practically, in the real world, are the data and the methodology. For success in VOC surveys, which comes first?
- Option One: send out a survey and get responses, then we determine how to analyse it.
- Option Two: establish a methodology for analysis, then develop a survey to gather responses that can be analysed accordingly.
Option One is the mistake most VOC programmes make, whilst Option Two is the true path to success. As the statement alludes to, you apply data to your refined analysis methodology, or more simply you “data your analysis”. As with the “job your love” example, you may not have the luxury of time required to work on methodology, you may need to start capturing data urgently. If this is the case, so be it, but working on developing methodology still needs to be made a priority. Surveys can always be upgraded when a methodology is developed.
Building the Optimal VOC Analysis Methodology
Considering this, what is the optimal methodology for VOC analysis, the one which will drive the most effective data capture via mechanisms such as surveys? As with all good analysis, we must focus on understanding the interaction at hand, and how customers engage with it. In simple terms, can we identify any subject a customer could possibly feedback about for this interaction? Thoroughly examining this question helps establish the realm of possibility. For VOC analysis, that means creating an all-encompassing framework of categories, and these must be unique and definable.
How Category Frameworks Enable Better Surveys and Better Insights
This category framework enables us to create a definable list of survey questions linked to the things we’ve identified that customers will want to offer opinions on. Definability is critical, as the question must be unambiguous. It must mean the same thing to both the person asking the question and the person answering it. The categories are also then the framework onto which free text responses are layered. As a result, the structured and unstructured responses to the survey are speaking the same language, with the same definitions. This removes the need for assumptions and guesswork. Instead providing a clear and replicable pathway through the data, to deliver consistent, credible and actionable insights.
Why Methodology Leads to Meaningful VOC Insight
In summary, success follows the path:
- Establish analytical methodology
- Capture data aligned to the methodology
- Analyse the data using the methodology
Don’t just “analyse your data”, instead “data your analysis”.
