Why is research so risk averse?

This post started life as a response to Ellen Woods’ well thought out Greenbook post on the paradox of risk, which you can read here. However, here is an extended version of my thoughts on the difference between the herd and good for the individual, in terms of risk, risk avoidance, and its implications for marketers and market researchers. In this post I want to concentrate of two issues: 1) the difference between what is good for the economy and the individual, and, 2) the difference between the short-term and the long-term. The difference between what is good for an economy and what is good for an individual. Most innovations fail, most entrepreneurs fail, most new products fail – the failure rate is typically quoted as being in the 80% to 90% range. Given these high failure rates, the logic for each individual entrepreneur is that they should not risk everything on some new change, idea, or innovation. However, the benefits to the economy of the few innovators and entrepreneurs that succeed is massive. Indeed, I think the number of entrepreneurs in the US is one of the keys to its success over the last 100 years – a success […]

Notes for a non-researcher conducting qualitative research

In November I am presenting a paper to the ESOMAR Conference on Qualitative Research, in Valencia in Spain. My paper suggests that one threat to qualitative research is the potential for damage caused by people with no training in qualitative research using one of the many DIY tools that are appearing – especially those for online discussions and instant chats. My suggestion is to create a simple set of notes that will help put newcomers to our world on the right path. Below is my initial draft if of my notes, and I would really appreciate your feedback. The Playbook The playbook needs to be short, relevant, and easy to use if it is going to be of value to people looking to conduct their own research. Therefore, this initial draft covers the following topics: Evidence, not answers Creating a narrative Analysis begins at the start not the end of the project Creating a discussion guide Not everything that matters can be counted Data does not mean numbers Consider actors and agendas We are poor witnesses to our own motivations Memoing Enabling the participants whenever possible Grounding the story in the data Examples that inform, not ones that entertain The […]

In market research, is agnosticism the new belief?

Traditionally the term agnostic has been applied to people who have not had the courage of their convictions to settle for belief or refutation. However, over the last few years the term agnostic has become increasingly used in the area of market research, and indeed agnosticism appears to be the new creed for many of the suppliers to our industry and the prediction for many of the pundits forecasting the future. Among the key areas where agnosticism is becoming a driving principle are: Mode agnosticism – especially between online, mobile phone, and tablet. Pull-Push agnosticism – between apps and browser based mobile research. User agnosticism – between DIY, assisted serve, partners, and full-service. Code agnosticism – between classic market research conducted under research codes, and other forms of research, such as Big Data and Social Media Research, which are as likely to be offered by non-research companies as research companies. Sampling agnosticism – market researchers used to be believers in ‘the way’ (aka random probability sampling), but now the largest single method is the convenience sample (aka online panel) and alternatives are picked according to their merit (especially availability, speed, and price), rather than on a priori beliefs. Why the […]

Unintentional Interlocking Quotas

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 […]