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 […]
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 […]
Post by Sue York, 27 January, 2018 At NewMR we love starting a research conversation – that’s one of our main reasons for being – to encourage researchers to think and talk about research and how to move our methods, approaches and practices forward to better embrace the future. So I was delighted to see this follow up to our November New, But Not Tech! event (click here if you would like to listen to the presentation that sparked this follow up conversation or the rest of the event). What started the conversation? In our New! But Not Tech event Sue Bell was interviewed by Suzanne Burdon on “Sense-making – a challenge to behavioural insights” (click here to listen to the recording) and in the Q & A session following the presentation a question was asked – Is sense making an ethnographic technique? Sue and Charlie Cochrane continued their conversation on this after the event and Sue has kindly summarised the exchange on her blog http://www.sbresearch.com.au/index.php/bellbird/139-revisiting-ethnography-a-conversation-between-sue-bell-and-charlie-cochrane Thanks for sharing Sue and Charlie!