2014 market research book list

Coming to the end of the year, I I thought I would share a list of the best books I have read in 2014 that I think other market researchers might like to read.  Now not all of these are new books by any means so forgive me if you have yourself read half of them.

This will make you smarter

John Brockman

This book is a compendium of scientific and philosophical ideas in one of two page essays on quite a remarkable cross section of topics. There are some really exciting thought packed into this book that I think market researcher could make good use of. I think reading it really did make me a little smarter!

Expert Political Judgment: How Good Is It? How Can We Know?

Philip E. Tetlock

Philip Tetlock's thinking has had more influence on how I think about conducting market research than any one person this year. I was introduced to this book by Walker Smith from the Futures Company and I would recommend that anyone who has an interest in the science of prediction should read this book.  Learn that political experts are not quite as good as chimps tossing coins at predicting things!

The Signal and the Noise: The Art and Science of Prediction

Nate Silver

I realise this book is a few years old now, and I wish I had read it sooner. There are so many really important ideas stuffed into this book that market researcher can use in their every day research. Its both inspiring and useful.

Strategy: A History

Sir Lawrence Freedman

This small thumbnail belies a bloody thick book which I have to admit to not to have read every page of.  It looks at strategy from all sorts of angles from war through to politics and summarizes the thinking of every major strategist in history including the likes of Sun Tzu, Napoleon and Machiavelli.  There is loads of great thinking for market researchers to digest. And probably even more valuable incites for anyone running a business.   It contains a detailed look game theory and the trials and issues with trying to apply strategy in real life. There is some sage advice in this book

Decoded: The Science Behind Why We Buy

Phil Barden

This book is a really helps explain the basics of shopping decision making and is a compendium of behavioral economic theory, an important topic for nearly all market researchers to understand - I really like the way it uses visual examples to explain some of the theory making it an effortless read. This book should be on every market researchers shelf.

100 Things Every Designer Needs to Know about People

Susan Weinschenk 

This book should really be titled, 100 things market researchers designing surveys and presentations should know about people!  ...And everyone involved in either of these task encouraged to read this.   Loads and loads of really clear, sensible advice.

The Curve: Turning Followers into Superfans

Nicholas Lovell

I read this after reading a very enthusastic linkedin review by Ray Poynter, thank you!  It persuaded me to buy it. There are some nice radical ideas in here about how to market things by giving things away and at the same at the other end of the scale offering premium high price solutions for the those willing to pay for them.

The Numbers Game: Why Everything You Know About Football is Wrong

Chris Anderson (Author), David Sally (Author)

I rather immersed myself in reading sports stats books this year. The way that data is transforming sporting strategy, there are lessons to be learnt by the whole of the market research industry. As an English person with a love of football, I feel rather a bounden duty to promote the Numbers game which looks at how statistical data has changed how the game is played. I loved this book and I am afraid I bored senseless everyone I knew who had any interest in football quoting incites from it. I also read Money Ball this year too which is the classic opus on how a proper understanding of stats transformed the fortunes of a major league baseball team, it is a great story and lovely read.

Who owns the future?

Jaron Lanier

This book has an important message about the impact of the digital economy on our future I cite from the book directly as it best explains  "In the past, a revolution in production, such as the industrial revolution, generally increased the wealth and freedom of people. The digital revolution we are living through is different. Instead of leaving a greater number of us in excellent financial health, the effect of digital"  Worth a read!

The golden rules of Acting 

Andy Nyman

This is a lovely little book, you can read in one short sitting. Why though do I recommend market researchers read it?  Well not because it teaches you anything about acting more about life and humanity and dealing with failure and the right approach to challenges.  There is not much difference in my mind to going for an audition and going and doing a pitch presentation. I took some heart from reading this book.






Want to see some other book recommendations?  Try this site:

http://inspirationalshit.com/booklist#

Your 2015 recommendations?

Love to hear your recommendations for books I might read in 2015  tweet me @jonpuleston

The science of prediction

This blog post is a short introduction to the science of prediction which is a topic that I have been totallt immersed in over the last new months and recently presented about at the 2014 ESOMAR Congress with Hubertus Hofkirchner. I thought I would share some of what I have learnt.


The accuracy of any prediction is based roughly around this formula...

P Accuracy = Quality of information x Effort put into making the prediction x (1 - difficulty of accurately aggregating all the dependent variables) x The level of Objectivity with which you can do this  x The pure randomness of the event

P = QxEx(1-D)xOxR

Here is the thinking behind this:

• If you have none of the right information your prediction will be unreliable
• If you don't put any effort into processing the information your prediction may be be unreliable
• The more complex a task it is to weigh up and analyse the information need to make a  prediction the less likely that the prediction will be correct
• Unless you stand back from the prediction and look at things objectively then your prediction could be subject to biases which to lead to you making an inaccurate prediction 
• Ultimately prediction accuracy is capped by the randomness of the event. For example predicting the outcome of tossing a coin 1 time v 10,000 times have  completely different levels of prediction reliability.

Realize that prediction accuracy is not directly linked to sample size

You might note as a market researcher, that this formula is not directly dependent on sample size i.e. one person with, access to the right information, who is prepared to put in enough effort, has the skills needed to process this data and is able to remain completely objective, can make as good a predictions as a global network of market research company interviewing millions of people on the same subject! I cite as an example of this Nate Silver's achievement of single handedly predicting all 52 US State 2012 election results.

Now obviously we are not all as smart as Nate Silver, we don't have access to as much information, few of us would be prepared to put in the same amount of effort and many of us many not be able to process up this information as objectively.

So it does help to have more than 1 person involved to ensure that the errors caused by one persons lack of info or another person lack of effort or objectivity can be accounted for.

So how many people do you need to make a prediction?

Now this is a good question, the answer obviously is that it depends.

It firstly depends on how much expertise the people making a prediction have on the subject individually and how much effort they are prepared to make. If they all know their stuff or are prepared to do some research and put some thought into it, then you need a lot less than you might think.

16 seems to be about the idea size of an active intelligent prediction group

In 2007, Jed Christiansen of the University of Buckingham took a look. He used a future event with very little general coverage and impact, rowing competitions, and asked participants to predict the winners. A daunting task, as there are no clever pundits airing their opinions in press, like in soccer. However Christiansen recruited his participant pool from the teams and their (smallish) fan base through a rowing community website, in other words, he found experts. He found that the magic number was as little as “16”. Markets with 16 traders or more were well-calibrated, below that number prices could not be driven far enough.

The Iowa Electronic Market, which is probably the most famous of prediction systems out there that has successfully been used to predicted over 600 elections, has I understand involved an average of less than 20 traders per prediction.

Taking account of ignorance

However for every one completely ignorant person you add into the mix who effectively makes a random prediction you will instantly start to corrupt the prediction.  And in many situations this is scarcity of experts means to isolate ignorant and expert predictions this often means you need to interview a lot more people than 16.

Take for example trying to predict tomorrows weather. Imagine that 10% of the people you ask have seen the weather forecast and know it will not rain - these could be described as the experts and the rest simply guess 50% guessing it will rain and 50% not its easy to understand that if by chance more than 10% of the random sample predict it will rain, which is entirely possible the group prediction will be wrong.   Run the maths and for 95% certainty you will need to have a margin of error of less than 10% to be confident which means you will have to ask 86 people.

It gets even harder if the experts themselves are somewhat split in their opinions.  Say for example you were trying to predict who will win a tennis match and 10% of the sample are you ask are keen tennis fans (experts) who predict 2:1 that player A will win, the rest randomly guess 50% player A 50% player B.  Because of division in the experts you now need to a margin of error of less that 7% to be 95% confident which means you will need to interview around 200 people.

Taking account of cognitive bias

It gets even harder if you start to take into account cognitive biases of the random sample.  For example just by asking whether you think it will rain tomorrow more people will randomly say yes than no because of latent acquiescence bias.  We have tested this out in experiments for example if you ask people to predict how many wine drinkers prefer red wine the prediction will be 54%, if you ask people to predict how many wine drinkers prefer white wine the number of people who select red wine drops to 46%.   So its easy to see how this cognitive bias like this make predicting things difficult .

In the above example predicting the weather this effect would instantly cancel out the opinions of the experts and no matter how many people  you interviewed you would never be able to get an accurate weather forecast prediction from the crowd unless you accounted for this bias.

This is just one of a number of biases that impact on the accuracy of our predictions, one of the worse being our emotions.

Asking a Manchester United football fan to predict the result of their teams match is nye on useless as it almost impossible for them to envisage losing a match due to their emotional attachment to the team.

This makes political predictions particularly difficult.

Prediction biases can be introduced simply as a result of how you ask the question

Imagine I were doing some research to get people to predict how often when a  coin is tossed it is heads and I asked the question "If I toss this coin, predict if it will be heads or tails" for the reasons explained above the on average around 68% of people will say heads. The question has been asked in a stupid way so it delivers back a wildly inaccurate aggregated prediction.  If you change the question to "If a coin were tossed 10,000 times, predict how often it would be heads" you probably need no more than a sample of 1 to get an accurate prediction.   Now this might sound obvious, but this issue sits at the route of many inaccurate predictions in market research.

Imagine you asked 15 people to predict the "% chance" of it raining tomorrows and 5 of them happen to have seen the forecast and know there is a 20% chance of rain and the rest randomly guess numbers between 0% and 100%. If their average random guess is 50%,  this will then push up the average prediction to 40% rain.  If there is the same mix of informed in non informed predictors in your sample like this, it does not matter how many more people you interview the average prediction accuracy will never improve and will always be out by 20%.

This runs very much counter to how we tend to think about things in market research, where its nearly all about gathering large robust samples.  In the world of prediction, its all about isolating experts and making calibrations to take account of biases.

The stupid way we ask question often in second hand ways we ask questions can exacerbate this.

"Do you like this ad" for example is not the same question as whether you think its going to be a successful ad. The question is a Chinese whisper away from what you want to know.

A successful ad is not an ad I like its an ad that lots of people will like.  Change the question and motivate the participants to really think and we have found to make a perfect prediction about the success of an ad samples drop from around 150 to as low as 40.

Picking the right aggregation process

The basics

Imagine you were asking people to predict the trading price of a product and a sample of  predictions from participants looks like this.

$1, $1.2,  $0.9, $0.89, $1.1,  $0.99, $0.01, $1.13,  $0.7,  $10,000  

Your Mean = $1,000  .....Whoopse that joker putting in $10k really messed up our prediction.

Now for this reason you cannot use mean averages. For basic prediction aggregation we recommend using Median.  The median average of these is = $1 which looks a lot more sensible.

An alternative might be to simply discard the"outliers" and use all the data that look sensible.  In this example its the $0.01 and the $10,000 that look out of sync with the rest removing these the medium average = £1.03 which seems a bit more precise

Weighting individual predictions

The importance of measuring prediction confidence

In the world of prediction its all about  working out how to differentiate the good and bad predictors and one of the simplest techniques to do this is simply to ask people how confident they are in their prediction.

For example if I had watched the weather forecast I would be a lot more confident in predicting tomorrows weather that if I had not.  So it would be sensible when asking people to predict tomorrows weather to ask them if they had seen the weather forecast and how confident they were. From this information you could easily isolate out the "signal" from the "noise"

The trick is with all prediction protocols to try and find a way of isolating the people that are better informed than others and better at objectively analyzing that information but in most cases its not as easy as asking if they have seen the weather forecast.

For more complex predictions like predicting the result of a sports match, prediction confidence and prediction accuracy is not a direct linear relationship but certainly confidence weighting can help but needs to be carefully calibrated.  How you go about this it a topic for another blog post.

In the mean time if you are interested in finding out more about prediction science read our recently published  ESOMAR paper titled Predicting the future

How to make the perfect guess in a pub quiz

Having spent the last few months researching and studying the science or prediction and also being quite fond of pub quizzes here is my guide to how to make a perfect guess in a pub quiz using some of what we have learnt.

Step 1: Ideation

Ask people to think of the first answer that comes into their heads.

If they think of an answer they should not shout out the answer,  this could corrupt the purity of other participants thinking. They should put up their hand to indicate they have thought of and answer and write it down. The should also write down how confident they are on a scale of 1 to 3.  Each player can think of more than one answer but they must score their confidence of each one.

Confidence range:
1 = a hunch
2 =  quite confident
3 = certain

Answer time:
Under 5 seconds = certain
5+ seconds = assign certainty based on personal confidence measure...

Step 2: Initial idea evaluation

After the point at which everyone gives up, you then share your answers from the team and the level of confidence.

Rules for deciding if the answer is correct:

• If more than one person has come up with the same answer in under 5 seconds then its almost certain that this answer is correct.
• If anyone is certain about their answer, there is a high chance this answer is correct.
• If more than one person comes up with the same answer and the combined confidence score is higher than 3 then there is quite a high chance that answer is correct and suggest you opt for that.

If there is a conflict or no answer scoring more than 2 point then go to step 3....

If nobody has come up with an answer the team is satisfied with go to step 4....

Step 3: Answer market trading

Each person must rate each answer by buying or selling shares in each answer choice with some "virtual money" .  They can buy or sell up to 2 shares in each answer.

Tip: If a person has 2 ideas that both are "hunches" then the first idea research has shown this is around 30% more likely to be correct.  Take this into consideration when making your buy / sell decisions.

e.g. if I think an answer is definitely correct I buy 2 shares. If I think its correct but I am unsure I buy 1 share,  If I think its definitely not correct I sell 2 shares, If I am feeling a little uncomfortable that it is wrong I sell 1 share.   Everyone has to commit to buy or sell - no body is allowed to sit on the fence.

Add up the total money traded in each idea and choose the winner.

If you want to be super nerdy about how you do this then don't simply add up the amount bet.  Answer should be weighted somewhat as there is not a linear relationship between betting confidence and prediction accuracy. Having studied a data from a large number of predictions we have found that prediction accuracy of somone who claims to be very confident is not twice as good as someone who has a hunch its only about 20% better (see chart below).  And people having a hunch are only 10% better than people making a total guess.  Interestingly there is little difference between someone who has a hunch and someone who says they are fairly sure.

Further more when you look at people betting against things and comparing to betting for things the prediction accuracy of the amount bet varies in an odd way. Smaller negative bets are slightly more predictive we found than large negative bets.  Strong positive bets on the other hand were more predictive than small positive bets but those that bet more than 2 were actually slightly less predictive than those that bet 2.  Hence our 2 point betting scale.

A more accurate betting aggregation process should score the amount bet like this:

-2 =  -20% 
-1 =  -20%
+1 = +10% 
+2 = +20% 

If on either of these aggregation processes no idea has a positive trading value then go to step 4....

Step 4: Idea stimulation

If you are not satisfied with any answer,  then all the team members should voice any "clues" they may be thinking about e.g. "I think his name begins with B" or "I think its something to do with football". Your thoughts could help another person think up the answer.

The scientific terms for this is called "Dialectical Boostrapping" - which basically means the sharing and discussion of ideas, which has been shown to help improve crowd wisdom generation processes. Find out more about this here Herzog and Hertwig (2009)

The more small clue you share they greater the chance of one of them triggering a thought in a team member. Note these can also be negative clues e.g. its definitely not...

If this process stimulates any ideas then go back to step 3 to evaluate them...

Step 5:  Picking the best of a bad bunch of guesses

If you are left with more than one answer that nobody is particularly satisfied with,  then pick the first answer the first person thought of.  This one has the highest chance of being correct.  It wont necessarily be right but it will have a slightly higher chance.

Advanced techniques:

Performance weighting your teams predictions

If you keep track of each individual's answer trading record over the period of several quizzes (.i.e if they bought 2 shares in an answer that eventually proved to be correct their personal balance would be +2). You can then start to weight your teams market predictions. You can do this by giving each person in the team a different total pot of money to bet based on their past performance record in correctly predicting the right answer based on how much money they would have won.

Note it would take several weeks studying at least 100 predictions to get a good idea of the prediction ability of each player so it would be a mistake to calibrate this after only one or two quizzes - luck has far more important role to play thank skill in the short term.

You might also want to assess how individuals confidence levels change when they have drunk 1 unit, 2 units 3 units of alcohol and start removing budget (or indeed giving extra budget!) as the night progresses!

Encouraging the team to think like foxes not hedgehogs

What buggers up the predictions of many pubs quiz teams can be the bullish viewpoint one or two individuals.   Having a strong opinion about things generally I am afraid does not correlate very well with actually being good at making predictions.  If you want to read up some evidence on this I recommend  you order this book all will be explained.

The team should foster an atmosphere where its OK to change your mind,  its not a battled between right and wrong , and should not be scared of failure.

Avoiding decision making biases

If the question is multi-choice make sure that your answer is no biased by the order effect or anchoring in the way the question is asked.  For example yes/no questions more people pick yes than no for irrational reasons.  When presented with multi choice options slightly more people pick the first choice for irrational reasons.   By being aware of this you can be conscious that your decisions are being made objectively.

Important Note/disclaimer:

The advice is a fantasy methodology for making a perfect prediction.  I don't advocate you using it in a real pub quiz. Firstly for practical reasons,  In reality the speed at which most pub quizzes progress you probably would not have the time be able to implement this approach.  Secondly it may also not be in the spirit of a fair pub quiz to use this technique in real life - it might be considered cheating!

Tips on writing a good conference presentation

Are you fretting about putting together a presentation for an up coming event? Here are some tips based on the experience of sitting through a few and delivering a few and designing a few.  They are geared for market research but I suppose the thinking applies to any presentation.

Design

1. Images really really do help make a good presentation - but read presentation zen to understand how to use them effectively
2. Aim to present one thought per slide (you can break this rule if the slide is exceptionally well designed)
3. Avoid at all possible costs bullet points - this is as much a philosophy as anything - "set your baysian priors to zero", OK you might be persuaded to let one or two creep in but don't start with a presentation that is 100% bullet points!
4. Is your  main message tweetable? Think about what people viewing your presentations will being doing - some may be tweeting the content so help them by turning your headlines into tweetable messages
5. Avoid video - don't fall into the trap of thinking adding a video will make your presentation more "dynamic" it usually dehumanizes your presentation.
6. Don't fret on look and feel too much - yes do your best but, there will be sure to be better designed presentations at the event you attend - the story and how you deliver the content is far more important  - some of the best, most inspiring presentation I have ever listened to have looked awful from a design point of view (um...would it be undiplomatic to name Brainjuicer presentations as an example) - focus on the story and you will be fine! 

Structure

Now this might sound a bit pompous advice, but to write a good persuasive presentation I really do suggest you first read up on the basic tenants of Greek rhetoric, in particular Aristotles ideas of Ethos, Logos, Pathos, the 3 ways to persuade. 

You must start your presentation by establishing Ethos which is about building a bond of trust with your audience, then use Logos to which is about making a logic arguments and then end with pathos which is all about drawing out the emotions of the audience.

Often I see pathos being used wrongly at the start of a presentation e.g. kicking it off with some sort of cocky joke or dramatic video from which point everything else seems flat.  Drama and emotion must be saved until you have won the trust of your audience and won the argument then you use it to drive home your message.

Ethos is really about establishing some humanity and connection with the people you are talking to on some level and have written about it in this separate blog poste   

The logos part of the process is the most skilled and it is about identifying the key problems the audience might have on an issue and then outlining your solutions.  Never present a problem to an audience if you are not going to follow with a solution.

Tips for writing the story

1. Your presentation must tell a simple story that you can recount in a basic elevator pitch
2. To devise your story really roughly sketch it out first either in your mind, on a piece of paper 
3. Try writing  your presentation as a story in excel - you will be amazed how effective this is at allowing you to coalescence you thoughts into a simple story - one line of the excel is one slide of your presentation. You can then once you got the basics down, really easily hack it around.
4. Go on a walk and tell the story to yourself or tell it to yourself as you are going to sleep or driving to work and see if it flows cleanly
5. If you get stuck telling the story to yourself you have what is known as a story knot - step back from it and try and tell it in a different way 
6. Test market the story by trying to summaries what you are going to talk about to a colleague 

Content market researchers should avoid

1. Don't pad your presentation with background stats - I don't need to know about all the details of the sampling techniques, we are all grown up market researchers - that's a given, just jump to the headlines.
2. Avoid the genero advice trap:  we need to be faster, more insightful and cheaper!
3. Check if your content fails the mobile phone growth statistics bleeding obvious test:  yes we know more people are now using mobile phones than own a toothbrush! Spouting any statistic we all could have a good stab at guessing or many in the audience has  heard before is wasting delegates time.
4. If you are going to play to the crowd by highlighting one of the many short comings of our industries working practice, don't you dare propose a proprietary solution that you and only you can use. The audience will want to hit you! 
5. Don't mention a problem without having a bloodly good solution to unveil that we can all grasp hold of.
6. Don't tell us you have a solution and then not show us the details or an example
7. If you are delivering a pure sales message about your great new piece of technology - come clean about it up front
8. I don't need to be told about how great Daniel Kahneman's work is anymore or need to be explained what system 1 and system 2 is.
9. Along the same likes, avoid the cliche buzzwords of the moment - in the naughties it was the word web2.0 which drove me mad hearing and this decades most important word to avoid using in any form of presentation is big data and I will let you make you mind up about all the rest
10.  This is the year of the xxxx!!! mmmm... This is probably not the year of anything (and certainly not the year of the mobile!) Avoid protestation unless you are the chairman of the conference when that task becomes obligatory. 
11. 100% recycling someone else's ideas already recounted in a New York Times best selling business publication is cheating!  

Technique

1. Try and make me laugh at least once (or at very least a smile)
2. Use simple examples: tell us your theory and then show us a real example could be seen as the essence of the structure of a good market research presentation
3. Be prepared to go off screen - Never under estimate the value of a good prop! Most of the show stealing presentations I have seen have used something other than just a presentation to get their message across. 
4. Admit your short comings and failures in a sandwich between your successes.  You could call this an integrity sandwich - we are much more open to hear about and believe in your achievements if you own up to your failures too. 
5. Make it interactive - a quiz embedded into your presentation is the simplest way to do this but it can be tedious being challenge to guess the answer if there is no reward for doing so. Come armed with prizes if you are going to do this!
6. If you are going to get people to do things, make sure its inspiring. It embarrassing to all stand up so if you are going to ask your audience to do this it better be fun or genuinely interesting
7. Dress rehears your presentation the office first to your staff - they will benefit too by knowing what you are going to talk about 
8. OK I suppose you better get your presentation spell checked too!  As a class one dyslexic spelling is a challenge to me and often the first feedback I get when I give a presentation is a polite aside about the spelling. To avoid this type of humiliation I do recommend getting your presentation spell checked by a third party.* 

*Admittedly this is not advice I always take myself to the fury of my marketing department and sales team as I would describe myself as a bit of a militant dyslexic and feel I have the human right to make spelling mistakes on my own content sometimes.