Whether you’ve conducted the research yourself or got help from an expert, the culmination of fieldwork is that you need to make sense of the data you have collected.
If you have outsourced the data processing, you don’t need to worry about the quality of your data. The researcher will have checked all the data for errors and inconsistencies, and anything that looks peculiar will have been investigated. Typical issues might be: someone who says their date of birth is 02 June 2022, or people who have said they have a dog, but never buy dog food. A good researcher will spend time looking at each and every one of these anomalies, so you can be confident that your data is clean.
The standard output from any market research software will be data tables. Something like this image below – rows and columns of data.
For me, this kind of output is heaven, it’s what I spend my days creating and then analysing. We can see that 62% of these respondents visited Oxford for a day trip while, at the other end of the scale, 3% stayed for more than a week. Reading along the rows we can see how the responses differ amongst sub-groups of the sample: 68% of those <35 years stayed for the day only, compared to 54% of those aged 45+ years.
However, I can totally appreciate that most people would rather see something like this graphic below. An image makes it much easier to see what is going on.
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Whichever way your data is presented back to you, the key things to consider are –
- Is this a representative sample? I’ll write about this in more detail in a forthcoming blog but, in essence, you must ensure that your respondents are representative of your whole audience. If you’ve only spoken to a sub-set of your audience then the results will be skewed.
- Do you understand what the data is telling you? If not, seek help. You will be using this information to make business decisions, so it’s essential that you understand the results.
- Are the findings important to you, or are they irrelevant? The data might tell you that all your customers are also customers of company B. But you will need to consider whether this is important or even relevant.
- Don’t be tempted to embellish the truth to make the data appear more favourable. If 68% of your customers said they would “probably buy from you again”, then you can say 7 in 10 customers are likely to buy again. But you can’t say “all of our customers will definitely shop with us again”.
- Don’t ignore the results if they reveal something that isn’t so positive. You’ve spent time and effort gathering this information, so use it. Take this opportunity to look at what’s wrong, and identify ways of improving.
If you are thinking of conducting research but unsure about how to interpret the findings, please give me a call, I’d be happy to talk through your options with you. Or, if you want the research taken off your hands completely, then I can do that for you too.
