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.<\/p>\n
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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 \u2013 \u201cdon\u2019t love your job, job your love<\/em>\u201d. 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 \u2013 most people don\u2019t realistically have the luxury of drifting around and \u2018finding themselves\u2019. Nonetheless, the point that motivation and interest frequently precede the greatest working success holds.<\/p>\n <\/p>\n As with most social media, it is the comments section that tells us whether people have understood a point, and this one didn\u2019t let me down. A combination of two mocking tones was present. Firstly, people criticising the grammar \u2013 not understanding it was intended as a pithy headline talking point, not an English exam essay. Secondly, there were many sarcastic adaptations; \u201cdon\u2019t walk your dog, dog your walk<\/em>\u201d, \u201cdon\u2019t eat your lunch, lunch your eat<\/em>\u201d, and so on. However, the one which leads us back to our topic was \u201cdon\u2019t analyse your data, data your analyse<\/em>\u201d.<\/p>\n What this person has shown me is that they don\u2019t understand good analysis. They\u2019ve accidentally stumbled upon the right answer, whilst thinking it was a joke. For the sake of grammar, a slight re-word to \u201cdon\u2019t analyse your data, data your analysis<\/em>\u201d brings us to the true order of priority. Excellence in most fields is based on technique. A great tennis player doesn\u2019t just walk up to the line and serve the ball.\u00a0 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\u2019t based on luck, it is driven by developing technique, the methodologies needed to analyse are honed.<\/p>\n <\/p>\n As such, the concept and task of analysis is one of honed methodologies. If we refer back to our statement \u2013 \u201cdon\u2019t analyse your data, data your analysis<\/em>\u201d \u2013 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?<\/p>\n 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 \u201cdata your analysis<\/em>\u201d. As with the \u201cjob your love<\/em>\u201d 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.<\/p>\n <\/p>\n 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.<\/p>\n <\/p>\n This category framework enables us to create a definable list of survey questions linked to the things we\u2019ve 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.<\/p>\n <\/p>\n In summary, success follows the path:<\/p>\n Don\u2019t just \u201canalyse your data<\/em>\u201d, instead \u201cdata your analysis<\/em>\u201d.<\/p>\n <\/p>\n <\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" \u201cDon\u2019t analyse your data, data your analysis\u201d For success in VOC surveys, which comes first?<\/p>\n","protected":false},"featured_media":29445,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false},"practices":[],"industries":[],"capabilities":[104],"class_list":["post-29444","blog","type-blog","status-publish","format-standard","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nThe Comments Section Misunderstands the Message<\/h2>\n
Methodology Comes Before Data<\/h2>\n
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Building the Optimal VOC Analysis Methodology<\/h2>\n
How Category Frameworks Enable Better Surveys and Better Insights<\/h2>\n
Why Methodology Leads to Meaningful VOC Insight<\/h2>\n
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