How customers think, feel and behave
All posts from: April 2010
Here’s an emotional perspective on how the 3 candidates make you feel after the first debate: a different perspective from opinion polls (and not to be taken entirely seriously).
Gordon Brown’s Emotional Profile
Considerable displeasure and unhappiness although a reasonable sense that he is focused and a safe pair of hands. Urgently needs to prove more exciting. Looking at our database of emotions (the line shows typical scores) the closest industry is Oil and Gas. Solid dependable, aggressive and uninspiring.
David Cameron’s Emotional Profile
Bit of a split. Tends to evoke more liking and more displeasure than Brown. Mildly more arousing but still much less than you’d expect. Urgently needs to get a positive spin going. Looking at our database of emotions the closest industry is the mobile phone industry. A bit too flash for its own good, but potential for a revamp.
Nick Clegg’s Emotional Profile
A real cut against the grain profile: evokes deep liking and deep dislike at the same time. Strong arousing emotions which could easily turn negative: at the moment a stong positive driver. Safety is lower than the other candidates - an achilles heel. Urgently needs to reduce peoples fear ratio on some key policies. Looking at our database of emotions the closest industry is retail grocery. Can develop great affection but that emotional equity can quickly turn sour.
Interesting article on the BBC: http://news.bbc.co.uk/1/hi/technology/8612292.stm
This shows how tweets are just starting to be used for predicting sales. OK, so perhaps it worked once but that does not prove that will always be the case.
Nonetheless some interesting fodder for looking at predictive analytics in a different way: perhaps the power of gossip is stronger than the power of surveys.
Just a quick update to recommend the book ‘The Cult of Statistical Significance’ by Ziliak and McCloskey. This is an important book. One key issue is about how we think of impact. Just because something is 95% significance with a weak effect on, say, spend it does not mean that we should ignore something with say 70% significance with a strong effect on the same spend, CSAT or whatever.
An interesting and better way to approach this is not to cut our models down to 95% significance but take a multiple of ‘significance x effect’ (thanks to Mark from Cranfield for this advice). I would recommend this book to blow away some of the continuing problems of quantitative understanding in the market research profession.