This post is written by Colin Boylan, an independent market research professional based in Wicklow, Ireland with extensive experience in Market Research in pharma and other industries in the UK and Ireland. In this post, Colin explains how the quality of the population sample used in a market research study can have significant effects on the quality of the findings. His post was inspired by recent posts here and here about “Golden Databases“. I’m glad to give Colin a chance to try his blogging chops out and I hope visitors here enjoy reading his insights in to information quality and market research.
Finding Red Herrings or Missing a Trick?
For most businesses there are major advantages to investing money in doing direct research with your customer base In theory itâ€™s a ready built list of people who are familiar with your business – so they can speak with authority on their experience as your customer.
The value of customer research to business should be by now fairly obvious, but thereâ€™s an old saying in research (and elsewhere) â€“ â€œgarbage in, garbage outâ€. The insights built off the dataÂ generated from your customer list is only as relevant as the list of people you ask to participate in the research.
However if, for example, they are lapsed customers then researching them is going to give you a picture of what your past customers wanted from you (unless these people are the focus of your research of course). Is this the same as what your present customers want? And if you are looking for why past customers stopped dealing with you and use a list full of current customers you end up with either few people able to answer the questions you set or â€¦worseâ€¦.data from people who shouldnâ€™t have answered the question â€“ which leads to another scenario.
Picture an important piece of research done with a list of past and present customers mixed in together with no way to tell who is who. Do current and ex-customers differ in their wants and needs from your business? I donâ€™t know – and neither do you. So how useful are any insights generated from this research? Not being able to separate these two groups gives rise to two potential scenarios. Either the excess numbers in there are throwing up â€˜clearâ€™ results that are not applicable to your current customers or the combination of both bodies is adding noise which stops you uncovering real insights about the customers youâ€™re interested in â€“ youâ€™re either finding red herrings or youâ€™re missing a trick!
Iâ€™ve used just one scenario here to make a point that can be applied to lots of customer data stored by companies â€“ be it incorrect regional information, incorrect gender, you can add whatever block of data is relevant to your own company here and the story is the same. If the data is not accurate then any use it is put to suffers.