Front cover of Skills and Training Report

Market Research Skills and Training Study 2018 Report

Posted by Ray Poynter Sue York, 8 August 2018 Below is the Executive Summary of our Market Research Skills and Training Study 2018 Report. You can download the full report by clicking here. Executive Summary Market research is a knowledge-based industry, its key asset is people – software comes and goes, techniques evolve, but if the future of market research is to be secured, it will be on the strength of its people to add value, and importantly, to add value that non-researchers cannot. To ensure that value-added future, market research needs to develop its people, and a key part of that process in training, to build the competence of the people who make up the research industry. However, this report suggests that too little training is happening. The authors believe that if market research and the insights profession is to prosper in the upcoming world of big data, automation, and artificial intelligence, this must change, and we will outline some of the key steps needed to achieve that. This report is based on a study conducted globally in April to June 2018, with 1108 market researchers and insight professionals, and builds on our Market Research Knowledge Benchmarking Study 2017. […]

Image of Escher's Relativity

What can Market Researchers learn from Escher?

Last week I was lucky enough to be able to visit the Miracle of Escher exhibition at the Ueno Royal Museum. After the visit my mind turned to the lessons market researchers can draw from the works of MS Escher. 1) The first lesson is that humans see patterns, even when patterns are not really there Escher’s Belvedere creates a building that can’t actually exist. If you look at the man and the ladder, you will note that it sits across the narrow dimension on the bottom floor, but arrives at the wide aspect of the upper floor. Most researchers will have faced a situation when there is an error in the data, but their initial response is often to make sense of the data. They tend to see a pattern when one does not exist. 2) Stories can help identify problems By following a story through you can often see the fallacy of a false pattern. Taking the example of Belvedere, If we follow through the story of the man on the ladder we can see that he starts on the narrow side of the building but arrives on the wide side – a contradiction that should tell us […]

Sign with two directions

What is semiotics and how is it used?

At the most basic level, semiotics is the study of how meaning is made. We often hear that semiotics is the study of signs, but that is only true when we take a very broad view of what a sign is, i.e. anything that communicates a meaning beyond itself. For example, the word Rose is a sign that can signify the plant, and the plant (a red rose) can be a sign that signifies love or passion (or the England rugby team). At one level, we all interpret signs every day of our lives, we negotiate the signage of human interactions, purchases, work, travel etc. In most cases we do this successfully because we have learned how to decode and use the signs in our everyday lives. However, the ability to understand how other people interpret signs, how new signs might be interpreted, and the linkage between different signs is a specialised discipline, that of the semiotician. Where semiotics becomes useful to marketers, market researchers, and insight professionals is where we hope to change behaviour, which typically means either creating new signs, or changing the way that signs are interpreted. For example, a brand wants to launch a new breakfast […]

Image of framework of knowledge

What is a Framework of Project Knowledge?

Post by Ray Poynter, 10 July 2017 On 19 July I am running a workshop on finding and communicating the story in the data for the Japanese Market Research Association (and a similar one in London for the MRS on 5 October 2018). One of the concepts I will be covering is how a Framework of Project Knowledge should be utilised. Below I have set out the basics of this way of thinking and working. As the image above shows, the Framework is divided into four segments. Known Unknowns In most well run research projects this segment is usually covered effectively. This is what market research has traditionally focused on. The researcher asks the client what the business questions are, what success would look like, and what actions they plan to take after receiving the insights from the study. From these elements the researcher can define what needs to be discovered through the research process. Known Knowns This element has two key aspects. Firstly, finding out what relevant knowledge already exists. This includes things like previous research projects and published information, but also includes the assumptions that the business is operating under and predictions about the results of the research. […]

One person in a crowd

How much training do market researchers receive, and is it enough? Sneak Peek from the NewMR Skills & Training Survey

Post by Ray Poynter and Sue York, 12 June 2018 NewMR have been running a survey looking at the training that market researchers and insight professionals are receiving. The fieldwork has now closed and we expect to produce the full report in about one month, but here is a sneak peek at some of the topline results. Boring, but important details N=1108 Fieldwork – 24 April to 1 June, 2018 Number of countries = 59 Sample source – link shared via NewMR and multiple partners (see note below) Data collection platform = Confirmit, languages used = 11 How much training are people receiving? We asked how much training people were receiving a year, and excluded the 26 people who said they were not sure. None or less than 6 hours a year 39% 6 hours to 5 days a year 43% 6 or more days a year 18% As part of writing the report we are having discussions with various people about what the right level of training is, but we think we can all agree that if market research is to be a value-added, knowledge-based profession, we can’t operate with 2-out-of-every-5 researchers receiving less than six hours training a year. […]

CCTV camera

Researchers should be aware of the problems with observational data

Posted by Ray Poynter, 18 May, 2018 The world is shifting from asking questions to utilising observational data (mostly for very good reasons) and this is creating a new set of problems that researchers need to recognise and address. What is observational data? Observational data refers to information gathered without the subject of the research (for example an individual customer, patient, employee, etc.) having to be explicitly involved in recording what they are doing. For example, collecting data without people having to respond to a questionnaire, without having to take part in a depth interview, and without having to maintain a research diary. Most big data is observational data, for example, the transaction records from a bank, people’s viewing habits on a video streaming service, or posts in social media. But, observational data can also be small data (based on just a few people). For example, participant ethnographic methods, used to to study people in their everyday lives, collects observational data, that is clearly not ‘big data’. Observational data can be based on census or it can be based on sample. For example, a few years ago a leading mobile phone company was able to sell very detailed data about […]

Baker Street road sign

The dog that didn’t bark – a great way to find insight in information

Posted by Ray Poynter, 10 May 2018 In the Sherlock Holmes story ‘The Adventure of Silver Blaze’ the key to finding the story (i.e. the person who committed the crime) is the curious incident of the dog in the night. When the horse (the Silver Blaze) was being stolen, the dog in the stable yard did not bark, and that was what was curious. This clue led Holmes to deduce that the theft was an inside job; conducted by somebody the dog already knew. Over the years, I have found the ‘dog that did not bark’ idea to be a useful tool when trying to find key messages and stories from a set of information. Thinking about what has not been said by research participants can be as revealing as what has been said. When analyzing the data, ask yourself what is not there? Consider the following examples: You read some references for a new employee, and they all stress team player, effort, innovative and cheerfulness. But none of the references address quality of work or accuracy – so perhaps the person’s work is not great – something you need to check. You look at a set of NPS data […]

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Do you understand what sort of Data Visualisation you need?

Over the past ten years, there has been a rapid and widespread growth in the use of data visualisation. However, this growth has resulted in wide diversity in the quality and usefulness of the visualisations being used. Visualisation helps if it meets the needs of the creator and audience, and for that to happen the needs must be clearly understood. This post looks at six different needs and maps them to different visualisation approaches. The key uses for visualisation are: To help find the story in the data, as part of the analysis process. To present a recommendation or interpretation. To explain/illustrate a concept or idea. To help other people explore information. Data as art. As an instruction or teaching aid. In the sections below, I will take each of these needs in turn and outline the implications for the sorts of visualisation you might want to use. Finding the Story When you are analysing data, looking for: patterns, meanings, and eventually the story, the key needs are: Speed You want to be able to move through lots of iterations to find the views that help you interpret the data. Flexibility In order to explore data you will play around […]

Training Survey 2018

A 2018 benchmark for training in the market research and insights industry

The NewMR survey closed on 31 May and the data is currently being processed. The future of the market research and insights industry is dependent on our ability to add value to data. The digital revolution (including the rise of passive data, automation, and more recently AI) means that data is plentiful and every month it becomes cheaper and ever more plentiful. If market research and insights are to prosper, it will be because we can add something that the machines can’t, for example, design skills, qualitative insights, presenting flair, storytelling, the ability to synthesise information, and the ability to act as consultants. However, if our future is based on adding value to data, in a knowledge-based economy, our key asset has to be our people, and developing that asset requires training. The 2017 NewMR Knowledge benchmarking study suggested that too many researchers, globally, were not getting the sort of support they needed to develop their skills. So, Sue York and Ray Poynter have launched a new study in 2018 looking to benchmark training. Once the data is collected we will write and distribute a report on our findings, along with our recommendations. When you have finished the survey you will be asked […]

Evette Cordy Book

Cultivating Curiosity by Evette Cordy, a book worth reading

To read the Japanese version of this post (from Mr Ryota Sano) click here. Posted by Ray Poynter, 19 April 2018 On the flight back from Australia to the UK, I read Evette Cordy’s new book Cultivating Curiosity: How to unearth your most valuable problem to inspire growth. I definitely recommend this book to anybody who wants to solve problems, help clients, grow their role at work, and/or get more stuff done. I do have some quibbles with a few of the observations and recommendations, but between the quibbles, there are large slabs of really useful pages, which provide a mix of broad philosophy and detailed suggestions for improving the way you work. The main thrust of the book (in my eyes) relates to the need to properly identify problems. For example, ensuring that you are actually tackling the main/underlying problem, that you have properly understood what is needed, and you have correctly assessed the context. The book identifies several problems, with the two key ones being: Starting the process by looking for solutions – which prevents time being spent on problem finding. Assuming things – for example assuming that we know what customers need, or that the solution has to […]