ESOMAR Council Report December 2018

In October I was elected to be a member of the ESOMAR Council for a period of two years. This week saw the first meeting of the new mandate, a meeting that was also the last meeting of the previous Council. The joint meeting allows the new Council to learn from its immediate predecessor and to facilitate a smooth transition. This Council meeting was a three-day session in Amsterdam, Netherlands. After an early morning flight from the UK, I attended an induction session at the ESOMAR offices, to brief the new members on our role, our responsibilities, and to sign things like codes of conduct (Council members have extra codes we need to follow in addition to the normal ESOMAR Codes). This was followed by an informal meeting with some ESOMAR staff, and dinner in the evening. Day 2 was a full meeting of the old and new Councils and we covered topics like reports on membership, events, publications, and the strategic plans that had been developed by the outgoing Council. The new Council is free to change any of the plans of the previous Council. But, if Councils behaved in that way, nothing would ever get finished, as most […]

The Insight Revolution: Question Everything – by Andrew Grenville – Book Review

Posted by Ray Poynter, 25 November 2018 If you work in a client-side insights team, then this is a ‘must read’ book for you. This relatively short book is based on interviews with leading figures from the industry and highlights the need for change in the way insights is conducted. Indeed the message is ‘change or die’. The book starts by setting the scene, briefly recapping the history of insights, highlighting that what we did in the past was correct in the past, and showing why it is wrong now. The second chapter is perhaps the most important in the book; it exposes the weaknesses in the way that insight teams relate the rest of the business. The book shows that insights teams tend to be order takers, rather than being proactive. To summarise, a stakeholder asks for the wrong thing (because they are not aware of all the options), the insight team delivers it, the stakeholder is disappointed because it does not help the business take an action, and the insights team are disappointed because nothing is done with their research. As a consequence, more decisions are made without insights, and the insights team are seen as a cost centre […]

5 key steps to creating a Modern Approach to Market Research

Guest Post by Caroline Frankum, 19 November 2018 Today, everything and anything is Digital. It’s a way of life that’s core to the DNA of what we think, feel, say and do. With 3.6 billion Global internet users surpassing half the world’s population in 2018, digital devices are getting better, faster and cheaper. There are now more mobile phones on the planet than there are TVs, which makes mobile a very powerful enabler of digital content, accounting for 69% of media access and 80% of social media access. Voice searches are also soaring: 2 in 5 say voice activated devices are ‘essential’ to their lives, and voice activated search has grown at exponential rates (up a whopping +3400% over the last 9 years), and are predicted to represent 50% of all searches by 2020. No surprise then that brands and businesses who are both increasingly embracing tech to make peoples’ lives easier and using digital media to revolutionize global communications in more engaging, personal and meaningful ways are the fastest growing brands in the world. But the best relationships are also a 2-way street. Good news then that many time-poor consumers want to provide brands their feedback (70% of bloggers […]

Do you know how to assess innovations in market research and insights?

Posted by Ray Poynter, 1 November 2018 We are surrounded by new approaches to understanding customers and markets, for example: behavioural economics, automated facial coding, neuroscience, chatbots, passive tracking, Artificial Intelligence, and of course big data. However, evaluating these new options is becoming ever harder, because there are so many of them, and because they make claims that are based on technologies that are hard for non-experts to understand. In this post, I want to share some of the techniques I use to assess innovations in market research and insight. In essence, I look at the following issues: Can it be provided by multiple suppliers? If an innovation can only be utilised via one supplier, it is much less likely to be successful, and I am much less likely to recommend it. Good innovations benefit from competition, prices come down when there is competition, and the diffusion into a market is accelerated if several solutions are available. When online surveys burst on the scene, we could use several different platforms to write the surveys, and choose between several difference panel companies for the sample – this promoted adoption, and cost reductions. Does it increase speed and/or reduce net price? In […]

Problems in Identifying Causality in Observational Data – BigSurv18

Posted by Ray Poynter, 27 October, 2018 Earlier today I gave a presentation at the BigSurvey18 Conference in Barcelona, Spain. The conference was themed “Big Data Meets Survey Science” and comprised one day of short courses and two days of presentations. The majority of attendees and presenters were from academia, which perhaps explains why it included a Saturday. I enjoy attending some conferences outside of my comfort zone, I feel I can learn different things and help break down the silos between disciplines. You can download a copy of my presentation “Problems in Identifying Causality in Observational Data” by clicking here. Although I am a fan of observational data (all the way from Big Data through to ethnographic investigations), we need to be aware that observations based on the real world can produce findings that are difficult to unpick. This is also the topic in a blog I wrote last week “Does Running Damage Your Heart? Another example of the problems of using observational data to infer cause and effect.” Interesting topics that arose at the conference inlcuded: Rene Bekkers from the Vrije Universiteit Amsterdam shared results from many studies looking at trust in society. What he showed is that using a 10-point scale […]

Does Running Damage Your Heart? Another example of the problems of using observational data to infer cause and effect.

Back in 2012, there were several research papers that suggested that running for more than about 20 miles a week was either not giving any benefit, or, worse still, it could be damaging hearts and making death more likely. The key sources were a piece of research by Dr Duck-chul Lee with 50,000 patients (presented at the American College of Sports Medicine (ACSM) 59th Annual Meeting 2012, see The Not-So-Long Run: Mortality Benefit of Running Less Than 20 Miles per Week) and heart findings from Dr James O’Keefe that looked at issues such as fibrosis, calcified arteries, and arrhythmias. This was picked up by a joyful media, with stories about how running was bad for you, and that anything other than a small amount of exercise was either useless or damaging. However, as is often the case with observational data (as opposed to control and test experiments), there were several problems with the conclusions. The key problem with Lee’s study was identified in 2013 by Dr Thomas Weber. In the sample of 50,000 people there were some long distance runners, for example, marathon runners. Lee wanted to assess these people against the non-runners and the occasional runners. However, he needed […]

How to Use Comparisons to Understand Data

Posted by Ray Poynter, 15 October 2018. Most individual numbers do not mean very much. In many cases, in order to see the real meaning in most data, you need comparisons. For example, if I tell you that the Belgian cyclist Eddy Merckx won 11 Grand Tours, you will no doubt guess that he was a good rider. But, when I tell you that 11 is the most any rider has ever won, and that only one other rider has won 10, and only one other rider has even won 8, then you start to get a sense of how special Eddy Merckx’s was. So, this post focuses on how to use comparisons to understand the story in the data, and how to use comparisons to communicate the story in the data. How Popular/Unpopular is Donald Trump Nate Silver’s FiveFiftyEight.com provides a wealth of data on US sports and politics and provides a really good example of how to use comparisons in their regularly updated series ‘How popular/unpopular is Donald Trump?’ The chart below shows the picture on 12 October 2018, 631 days after Donald Trump took office in January 2017. Note, in the US the election for a new […]

Bias Runner 2049

Story-with-a-meaning post by Ray Poynter, 11 October 2018 Tom Torquemada was looking forward to his interview today, he was off to the Global Broadcasting Corporation to talk about his work, and he loved his work. Tom was a Bias Runner, one of a team that hunted down and retired errant AI systems. Tom had been thinking overnight about the best, non-technical, way to describe what an errant AI system was and how he and his colleagues identified them. The scale of the problem was clear to everybody – nearly everything today, in 2049, was determined by AI. Machines and bots determined who got a job, who got the next home loan, who might commit the next crime, and whether in this brave new world your schooling/conditioning would result in you being a labourer or artist. But with this transfer of power to the AI machines came a fear, a fear that the machines might not play fair, they might be biased or simply error-prone. The job of the Bias Runners was to find the biased or error-prone machines and ‘retire’ them. There were two key types of problems that the Bias Runners were looking for ‘biased machines’ and ‘unstable machines’. […]