Most market researchers (IMO) who use Twitter do so with the #MRX tag, with the #NewMR, #ESOMAR and #AMSRS tags a little way behind. Indeed Vaughan Mordecai has recently posted an interesting analysis of #MRX contributor and content – and Jeffrey Henning Tweets a weekly list of top #MRX links and posts a biweekly blog on GreenBook about the top ten. But, is all of this just creating a cozy world where a few thousand market researchers tweet to each other, and nobody else really contributes, reads, are even cares? The quickest way to get recognition amongst market researchers is to use the #MRX tag, so it becomes the default, and in doing so, perhaps, it becomes a fence or boundary of our own making? Time add new links to the wider world? Other leading #MRX figures, such as Tom Ewing and Reg Baker have written about what happens if you ignore the #MRX audience, your figures quickly decline. But perhaps the key is to be adding more dimensions to what we do, and for those dimensions to have an external focus? By external focus, I mean using cues and clues that other people are likely to be looking for. […]
Below is a list of the five posts, on NewMR.org, that in 2013 have been read by the largest number of unique readers, as measured by Google Analytics. Why do companies use market research? This was posted December 30, 2012, and has had 633 unique viewers in 2013. The ITU is 100% wrong on mobile phone penetration, IMHO. Posted 29 June, 2013, viewed by 380 unique people. Is it a bad thing that 80% of new products fail? Posted 7 March, 2013, 353 unique viewers. Notes for a non-researcher conducting qualitative research. This was only posted on 26 August, 2013, so it is probably still on its way up. It has 350 unique viewers. A Short History of Mobile Marketing Research. Posted 1 March, 2013, with 278 unique views. I ran the analysis to see if I could spot any patterns in what made a successful NewMR post. However, so far, no clear pattern is emerging. Any thoughts or suggestions?
This post has been written in response to a query I receive fairly often about sampling. The phenomenon it looks at relates to the very weird effects that can occur when a researcher uses non-interlocking quotas, effects that I am calling unintentional quotas, for example when using an online access panel. In many studies, quota controls are used to try to achieve a sample to match a) the population and/or b) the target groups needed for analysis. Quota controls fall into two categories, interlocking and non-interlocking. The difference between the two types can be shown with a simple example, using gender (Male and Female) and colour preference (Red or Blue). If we know that 80% of Females prefer Red, if we know that 80% of Men prefer Blue, and if there are an equal number of Males and Females in our target population, then we can create interlocking quotas. In our example we will assume that the total sample size wanted is 200. Males who prefer Red = 50% * 20% * 200 = 20 Males who prefer Blue = 50% * 80% * 200 = 80 Females who prefer Red = 50% * 80% * 200 = 80 Females […]
From neuroscience to behavioural economics, from advanced and adaptive choice models to participative ethnography, from facial coding to big data there are masses of analysis approaches that are threatening to be the next big thing (yes, I know they are not all new, but they are contending to be the next big thing), and I’d love to hear your thoughts. However, in my opinion, text analytics (using the term in its widest sense, but focusing on computer assisted and automated approaches) is my pick for the biggest hit of the next few years. There are several reasons for this, including: The software is beginning to work, from tools to help manual analysts at one end of the spectrum, to better coding, through to concept construction software, the tools are beginning to mature and deliver. Text analytics, as a category, is not linked to a niche. Text occurs in qual and quant, in free text, in the answers to survey questions, and in discussions. Text analytics will help us ask shorter surveys, one of the key needs over the next few years. Instead of trying to pre-guess everything that might be important, researchers can reduce the number of closed questions massively, […]
As I mentioned in earlier posts. NewMR is involved in the creation of a new book, provisionally called the Handbook of Mobile Market Research. We will be publishing a lot of our work online, as the book progresses, to share our learning, to invite comments, and hopefully elicit extra material. Much of the material we are gathering is available via our Mobile Market Research Resources page. The post published on this page is a piece of ‘work in progress’ from one of the chapters in the new book. The chapter will look at key debates in mobile market research, and this post addresses the question “How do clients move 20 to 30 minute tracking studies onto smartphones?“. We have access to some raw data and studies to back up the points in this post, but we’d love to have more, and I have flagged up in the post where we are particularly looking for more material. So, if you’d like to contribute: comment here, comment in the NewMR LinkedIn group or email us via firstname.lastname@example.org. Note, this work remains our copyright, or at least until it is transferred to the publishers. If you use it, or quote from it, please […]