Tuesday, 10 May 2016

Researching the different words used by Women & Men

We have recently conducted a small piece of research to explore the differences in the language used by men and women when they describe themselves and other people including.

There are some quite surprising difference especially in the words men and women use on their CV.

This link below is to a survey quiz to highlight some of the most popular words we have identified with the biggest difference is gender usage.  Please feel free to circulate this link.

If you would like to find out more about this peice of research which was conducted for a Women In Research event please to get in touch and I will be happy to share the raw data.  We interviewed 500 men and 500 women in the UK and the USA.

Thursday, 31 March 2016

How to make a good prediction

This is some general advice on how to make a good prediction.

1. Have an intelligent conversation with your gut instinct! 

Gut instincts are incredibly valuable when it comes to making a prediction, the best predictors often heavily rely on their gut instincts, but remember that your gut can be flawed. Your instinct is exactly that, an instinct, so any cognitive or emotional biases you have could impede your predictive success.

The trick is to not rely 100% on what your gut instincts tell you but to always question them: subject them to critical appraisal, think about any biases that might be effecting your objectivity.

Its useful to be aware of  some of the most common cognitive biases, thinking short cuts which can corrupt our metal calculations.

2. Dis-aggregate what you want to happen with the process of predicting what will happen 

Of all these biases the most significant one you should be aware of is confirmation bias. Selectively using evidence to support your point of view.

Remember your emotions have a tendency to force you to believe much more in evidence that supports your point of view than evidence that does not support your point of view.  The more emotionally involved you are in the outcome the less reliable your predictions are likely to be as a result of this.

To illustrate how significant this issue, is here are the results of a survey conducted in January 2016 where a group of US panellist were asked to predict who would win the US election. You can see that Democrats were pretty certain a democrat would win and Republications pretty certain a republications would win.

When making predictions try to really stand back from what you feel.  But also remember you can also over compensate for this and be overtly negative about an outcome sometimes.

3. Unpack the problem

Start by having a good think about the problem you are trying to solve.  Making a prediction is essential a problem that you are trying to solve.  What do you need to know to solve the problem, where are you going to find that out, what issues do you need to consider, what could effect things? Build up a map in your mind.

4. Gather as much evidence as you can from as diverse a sources as you can

For complex geopolitical predictions assembling as much evidence as you can is vital.  Look out for conflicting points of view, understanding differences in opinions will help you pin down the level of certainty on any one topic.

5. A sage piece of advice “You don’t need to look into a crystal ball to see the future, just read the history books” 

We often over think about how unique a situation is and forget to look at evidence from the past that could help understand issues.  Always think about whether there have been any similar situations in the past or from around the world that could help guide you.

6. Break out the problem into sub-predictions

Often with complex predictions you have to break it down in to lots of sub factors that are easier to make predictions about.

7. Look at it from more than one angle – inside out and outside in, more helicopter viewpoints and in meta dimensions:  

The more ways you can look at the problem the better.  We have a tendancy to start looking at problems from the inside out. For example, elections we would consider reactions to the leadership and specific policies, an outside in approach would be to consider things like historical voting habits of a community and factors like the overall state of the economy.   A good predictor will evaluate and weigh up things from as many points of view as they can.

8. Use a “Montecarlo” style approach to aggregate the answers from the different points of view.

This sounds a bit complicated but its not really.  Its just the name given to the process of adding up the answers from different outcome scenarios.   All you do is think about the topic from each specific point of view and predict the outcome based solely on that factor e.g. if the economy is doing well this tends to favour the incumbent political party so with that way of looking at things the incumbent part would win. You then look at the next point of view and the next and then add up how many point towards one choice or the other. If from 10 points of view choice A comes up 6 times and choice B 4 times, then this suggests there is a 60% chance of A being the outcome.

9. Enter the minds eye of the people with the opposing point of view and try to dissuade yourself!

For example if you are left leaning you should read right leaning publications and sources of information to get a sense of the message and feelings from the other side of the fence and vica versa.

When making any forecasts it is very important once you have made up your mind not to give up thinking about it, but from that point in time to focus on trying to dissuade yourself.   This will help you test out how robust your prediction is.  If you can easily dissuade yourself or find your opinions being shaken it’s important to take notice of these feelings.

10. Be prepared to change your mind over and over again

Dogmatism is the enemy of good prediction, never fix your prediction in stone, always be prepared to adjust your level of confidence in what the likely outcome will be and to be ready to completely change your mind.

You need to be in a ready state to jump ship at any time, and be constantly alert to over confidence.  A good predictor, I am afraid to say, lives in a somewhat paranoid mindset.

11. Think though all what if scenarios

What if something happens like an earthquake in the next 3 weeks?  Unlikely, but a good predictor will go though and consider all the things that might happen that could influence the outcome of a predictions and take this into consideration.

12. Understand market behaviour

Markets tend to over-react to news emotionally, and as a result you often see massive spikes in stock market prices when company results are announced or economic figures are released and then within a few days they settle back to where they were before.

It’s a natural process to overestimate the impact of any one piece of news, be considered when processing news information.  My advice would be to sleep on it and think about how you will react to it the next day.

13. Understand certainty predictions

It helps if you are asked to predict how certain you are that something will happen to understand the basic odds of things happening.  For example, if there are 2 people in a race and you are 50% certain one candidate will win,  means you don’t know who will win.  If there are 3 candidates in a race and you are 50% certain one particular candidate will win, this means you think this candidate is 50% more likely to win than the other 2 candidates, though you don’t know if they will win overall.

 Having a good understanding of your certainly level can help ensure you make more considered predictions and also identify where there are holes in your knowledge.

14. Read Superforecasting by Philip Tetlock & Dan Gardner and The Signal & the Noise by Nate Silver:

Much of this advice is taken from these 2 book and if you don’t read both of these books you might find it tough to be a good forecaster.

Putting these ideas into practice...

An example:  working out the result of the UK EU referendum

Imagine you were trying to predict which way the UK will vote in the upcoming EU referendum. Below are some examples of the range of things you could think about and research to help you make a better prediction.

Unpacking the problem:  
1. What are the polls saying?
2. We cannot entirely rely on what the polls are saying can we, why?
3. Phone polls and telephone polls are telling a different story, what does that mean?
4. A lot of people are undecided which way they likely to vote?
5. Who are the people who cannot be reached by polls and what are they thinking?
6. Who will be most motivated to vote?
7. What impact could news events have as they unfold in the next few months?
8. What will be the impact of debate and discussions?
9. What impact will the personalities involved have on the debate?
10. What will be the impact of the press media and social media conversations have on the debate?

Gathering evidence:
1. What are all the polls saying right now? How much do they vary?
2. Why did the polls get it wrong last time? What can we learn from this?
3. How have the polls moved so far?
4. What evidence do we have about who the hard to poll groups are and what they are thinking?
5. What evidence do we have about voting intention amongst the undecided?
6. What was the latent sentiment of the UK public before the referendum process started?
7. What historical data is available from other referendums or independence votes?

Looking at it from different angles:
1. In other votes for independence how did public sentiment evolve in the run up to these votes?
2. Do British people feel they will be economically better off inside out outside the EU?
3. How important are immigration issues in the mind’s eye of votes compared to economic issues?

which issue will have the final say?
4. Who do the British voter trust more David Cameron or Boris Johnson?
5. What other big issues are driving people’s feelings on which way to vote?
6. With 3 major newspaper groups lobbying for an exit, how much influence do newspapers have?
7. How passionately do the different sides of the debate feel about the issue?
8. Will the levels of passion change as we get close to the vote?
9. How will the levels of passion change if the polls are leaning heavily one way or the other?
10. Loss aversion, will this impact on our decision?

Scenario planning:
1. What would happen to voter’s sentiment if a terrorist attack occurs the UK before the election?
2. What would happen if there was a collapse in the value of the pound before the election?

After you have read and thought about this the most important thing perhaps to do is think about what has not been considered in these list that might have an impact?

Friday, 20 November 2015

What should we be measuring in brand tracking studies?

…In a nutshell, what brands do you buy and why?

Byron Sharpe et al have fairly convincingly proved that the key health metric of a brand is its total universe of users.

The awareness of the brand, the loyalty of the users of the brand and how much they like the brand are all rather academic constructs as all these measures highly correlate with each other and ultimately with the brand’s universe of users. All can be modeled using a Dirichlet distribution model.

The proportion of people who are brand-aware can be modeled from the proportion that are spontaneously aware of that brand. With X number of total users there will be Y number of loyalists and Z number of people who love and recommend the brand. If users drop, liking, awareness and loyalty levels will drop all in parallel. If you asking about liking of brand you will find we all like the brands we use at pretty equal levels.

To illustrate the point, here is an example of data taken from a quite typical brand tracking study where the statistical correlation between brands purchased in the last 12 months and all the other core metrics measured in the brand tracking study has been calculated. The correlation for nearly every metric is above 0.85 and some metrics in the high 0.9’s.

So you could argue that the only brand equity question really worth asking in a brand tracking survey is: “Which of these brands do you use?”

Wednesday, 14 October 2015

What can researchers learn from film script writers?

If you study the art of film making, it will tell you that a good film script is based around one great question, that grabs your attention from the off and then the story naturally emerges from this and slowly reveals the answer. The question drives the whole story.

Here are some examples:
  • What if every day was the same? GROUNDHOG DAY 
  • What if a nun was made to be a nanny? THE SOUND OF MUSIC
  • What if a really smart innocent person went to prison? SHAWSHANK REDEPMPTION
  • What if dreams & reality were inter-changeable? MATRIX
  • What if there's more to life than being ridiculously good looking? ZOOLANDER
All the books also emphasise how important good narrative structure is to making a great film i.e. films that people want to watch and concentrate on watching from start to finish. Films construct heroes through which the story is told, and these stories needs to adhere to a strict story structure. There are about 7 of these basic story structures, established from a time well before the dawn of film making, in fact the basic structure of storytelling has hardly changed much for thousands of years.

Wednesday, 7 October 2015


Most business evolves in a classical evolutionary way. Through slow mutations in their approach to doing business, which leads to the business being more or less successful and in a survival of the fittest way - the strongest mutated variants win through. The most common way businesses “mutate” is through making a whole series of what is know as kaizen innovations – small baby step improvements and changes to increase the efficiency of a business.

Most kaizen improvement are logical evolutionary steps. If we do this we think we will make more money.

All of life evolves in this way, through trial and error. There are some interesting things thought that can happen if you break out of the evolutionary approach to development. Start to create things that never could emerge as a result of “market forces” of the demands of customers.

What do I mean by this, well the example I would like to use to illustrate this is the “unstable jet”.  

Imagine a bird evolving a really really long beak, the longer the beak in theory, the more efficiently the bird could cut through the air and the faster if could fly. The problem with having a really long beak though is you reaches a point of instability, because a tiny fluctuation in the movement of the tip of the beak or a gust of air in a different direction and the beak could be deflected and instantly act like a sale and the bird would flip over its nose. As a result there are no birds with really really long beaks like this as they would be unstable.

However imagine a bird with a really long beak and a the end of it there was a sensor and small computerized navigation system that could make microadjustments to the direct of the “beak” to ensure that its always in a stable position facing directly into the headwind and not deflected off course by a gust of wind and now you have designed what in theory is a bird that can fly faster because it can cut through the air more efficiently. Unfortunately no bird is likely to evolove this extra step because the solution is “non-evolutionary”. It can never get there by baby step "kaizen" mutations.  It takes a major new "non-evolutionary" improvement to get over the hurdle of an unstable beak. 

 Yet Man has been able to make this non evolutionary improvement that would have been impossible in the natural world and we have designed jets now with exactly this feature.

And this is the type of non-evolutionary form or innovation that I am particularly interested in.

Most of the establish businesses that are killed off are by major disruptive innovations like this – business solutions and leaps of improvement that break the classic evolutionary kaizen business development model. The step changes that existing businesses cannot make because it would result in the total cannibalization of their existing business (making them unstable).

To get there businesses need to take a completely different approach to innovation stop thinking about what would make money and start focusing on what is possible. What would happen if....To think more abstractly about what could happen if this other thing happened.  To cross connect ideas. To build field of dreams. To invest in the connecting points.  To look out for the non-evolutionary leaps.

Wednesday, 15 April 2015

Wednesday, 7 January 2015


Imagine if there was no commercial agenda set by your company and you could do exactly what you wanted to do. What would you do?

Saturday, 3 January 2015

2015 the survey design tipping point: change now or pay the price later

I can't change my survey as it will effect all our historical trend data.

This dilemma sits at the heart of most the discussions we have with companies wishing to update their research studies. "I would love to do things differently but I can't!"

And it is also the is the reason why so many of the surveys we look are behind the curve in way of design and questioning techniques,the reason why the average online survey length has crept up from 15 minutes to over 20 minutes over the last 5 years.

Well 2015 is the year in which things are going to have to change.

...and the reason is, our respondents are going mobile.

At the end of  2013 only 5% of people completing our surveys did so via mobile or tablet device, by the end of 2014 that figure has reached 20%. In some lead markets of Asia its already approaching 30% and as an indicator of where things are going, by the end of 2014 more than half the people signing up to our online panels did so via a mobile or tablet device.

What we are starting to see is stark differences in completion rates between those surveys that are mobile compatible and those that are not.

We are going mobile too...

 By the end of this year all our survey respondents are going to have the choice of what surveys they want to complete and every survey that is not mobile compatible will be marked as non-compatible.   As a result, the cost of fielding these non-mobile compatible surveys will start to increase significantly.

This is a change or die moment for many peoples tracking studies.

The days of getting away with a 20 minute+ grid dominated survey are pretty much over.   The dropout rates of these types of longer surveys on respondents completing surveys mobile devices are over 50% , which is simply not acceptable for anyone.

Tuesday, 30 December 2014

10 things I learn in 2014

  1. We convert statistics into emotions: and so the best way to fast track getting your statistics remembered is to emotionalise them!
  2. Our brains are Bayesian decision making engines: by and large designed to work out what choices will make us most happy
  3. A question is a problem that you ask respondents to solve:  it is easy to lose sight of this simple thought. Often we design questionnaires that skirt around the problem we are trying to solve. We ask questions so euphemistically,  we ask questions that are a Chinese whisper away from what we are trying to find out. 
  4. We like to think in different ways:  Researchers like to quantify things, particularly types of people and how they think and consumer.  We have personalities that get classified and we are this type of person or that. The simple fact is that we are all sorts of different types of people depending on the time of day our mood and our circumstance. We all like to think in different ways, its boring the make the same types of decision especially when we go shopping.  the concept of "type" in research is limiting. The same person who liked to try out new types of shampoo  
  5. Scale effects: you scale something up and sometimes different maths starts to apply
  6. If there are infinite number of universes,  then in some universes it is certain that a god will exist (as some of us know it) ..and in others it is certain that a god as we know it will not exist:  A nice thought, that's assuming that there is such a thing as infinity, some physics question this too ... it might, but there is an infinitesimal small chance!
  7. Rating something is inherently a system 2 thinking process:   compared to a binary choice process which is more system 1. The example being you come out of a film your friend asked did you like it and in a fraction of a second you can say yes or no, but likewise if the friend asked you to rate the film this takes more mental processing and anything up to 5 seconds thinking to give it a score.
  8. 16 is a crowd:  Prediction as opposed to market research is not about the numbers of people you ask its about the quality of information available to the group of predictors and their effort and objectivity.  
  9. Unwise crowds:  crown wisdom is a nice idea but it only work in certain rather rare circumstances. Crowd predictions are mostly corrupted by system errors & network cognitive biases. 
  10. Computers using artificial intelligence can now read some of our emotions better than we can:  Artificial intelligence tools are getting so good at reading our emotions by combining various input data sources ranging from how we are typing, the language we are using and the music we are listening to that they can identify traits of depression often months before it is apparent to ourselves

Thursday, 18 December 2014

Meeting Jargon in heard in 2014

These are some of the wonderful pieces of  jargon I heard and noted in various meeting I attended this year...enjoy (and forgive me, I have probably use most of these myself!)

  • Boil the ocean 
  • Spin our wheels 
  • Capability area 
  • Landing it 
  • Resonate with...
  • Next steps
  • Gun up
  • Creating confusion 
  • Lense (wide angle /focused/zoom)
  • Data science unicorns 
  • The unsexy stuff 
  • Slower pace 
  • Focus in on... 
  • Support of the board 
  • Additional resources 
  • Executions logistics 
  • Treat as a priority 
  • Alignment 
  • Asking for permission not asking for permission 
  • How we are going to scale it 
  • Catching lightening in a bottle 
  • CDO 
  • UI
  • Mathmagicians 
  • Tentpoling 
  • Lumascape 
  • Ecosystem 
  • Insights into actions 
  • Data at the centre... 
  • Making a brand more culturally relevant 
  • Triangular across the data 
  • Effecasy 
  • The mandate 
  • Manadated 
  • Implementation 
  • A hard stop 
  • The pillars 
  • The lead sled dog 
  • Skin in the game 
  • In the new world 
  • Analysis paralysis 
  • 90% of data created in the last 2 years
  • Experience based economy 
  • Product service experience 
  • Ubiquitous computing
  • Ambient intelligence
  • Embedded
  • Network 
  • Internet of things 
  • Context aware 
  • Sensor fusion 
  • Personalised behavioural profiling 
  • Adaptive ai learning 
  • Anticipatory predictive analysis 
  • The Google car crash dilemma 
  • Cut off data cut off blood 
  • Hyper personalisation

If you have any nominations do tweet them #mrjargon

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

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

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

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

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

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

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:


Your 2015 recommendations?

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

Questions on trial

Surveys are competing with a billion+ largely more fun things to do online these days and we are reaching tipping point were many people, the young age groups in particular are simply refusing to complete survey because they are too boring.

To have any chance of competing we have to change our approach and I think this starts with taking a long hard look at some of the boring questions we are asking in our surveys.

One of them being this one...

"What brands are you aware of?"

It a question asked in nearly every consumer survey I come across, usually asked both unprompted with an open ended question and then prompted with a closed question set of brand options (twice the work). Its one of those sacred cow set of questions that everyone insists on asking.

Do we actually need to keep asking this question?  Are there not better questions that could be asked in better ways, that make better use of a respondents brain that deliver more useful data?

Here is the case for the prosecution:
  • Its a really dull question: From a respondents point of view these are probably the dullest most cliched question they continually have to answer in surveys. Respondents don't like answering these questions,  they trigger drop out. 
  • Respondents put little thought into their answers: Less than half the respondents you ask this question name more than one or two brand when asked unprompted and well over 20% say don't know. The prompted question often gathers together a random set of clicks from respondents.  
  • Little statistical value:  If the average respondent list 1 or 2 brand and assuming the number of individual brands listed adhere to a Zipf's law style distribution, the most popular brands named much more often than the least popular brands - on a typical sample of 400 respondents there will only likely to be 1 or 2 brands you ever have enough data to work with statistically. 
  • Large error boundaries:  The data error boundaries on this question on most survey samples are of often so large that in wave to wave in brand tracking studies the fluctuations down to pure statistical error are often of an order of magnitude larger than the actual underlying change in brand awareness.  Resulting in a lot of "overfit"
  • Nstand alone value: The data it delivers back is almost always duplicated or can effectively be modeled from answers given to other questions in the same survey.
  • Meaningless metric: Brand awareness information is totally useless in isolation from anything else. What it measures is intangible it's certainly not an accurate measure of purchase behavior for example - Unprompted awareness correlate at around about 0.54 with purchase behavior*, prompted awareness it drops to under 0.2*.  If you specifically want to find out what brand consumers are likely to buy there are other question that are far more effective.  It is also not a measure of how much I may like a brand....
  • I have never see how this information is really used profitably:  It's always the chart that everyone skips past in a presentation. 
The case for the defense:  

Now I have challenged several prominent and respected researchers many of whom are still very wedded to asking this question in survey and asked them why they like to use it.
  • Its a fundamentally important measure: The brand that is mentioned first is the brand that has the most brain neuron connections and associations between the product and the category. So many see it as fundamentally the most important question you can ever ask in a survey.
Now I get this but I am still left with a feeling of so what....if you ask me what brand of chocolate I am aware of  - I will say Cadbury's.  I have been exposed to this brand all my life seen thousands of Cadbury's ads, seen the brand in every confectionery counter I have ever visited.   But I never buy Cadbury's and so what x number of people are aware of a brand.

Now I am open to some other arguments as to why it should be kept if anyone wants to make them, but my judgement verdict is that its a question that if not completely banned, should at least be taxed, in the same way that cigarettes and alcohol are taxed to discourage their usage.

What could be asked instead?

There are a range of alternative ways of directly or indirectly measuring brand awareness that are more interesting and potentially useful.

I find one of the frustrations to answering the what brands  do you recall question is thinking why does it matter and not knowing how many to list, Simply applying a rule to the question that contains the task it in a more meaningful framework for respondents can make it far less dull to answer.

you could ask:
...what are their favorite brands
...which brands they would have in their perfect supermarket
...which brand, if they could only buy one, would they choose to buy a life long supply of... ...which brands they would take to the desert island
...which brand they would invite to a party
...which brands they would recommend to their best friends
...which brand they would invest in
...which brands they think they will still be buying in 5 years time

All these will gather top of mind awareness to some degree or other but are more interesting and purposeful for the respondents to answer and adding in these "rule" can make them more salience and relevance e.g. asking about what brands they would have in their perfect supermarket  it not just just a measures awareness but also intent to purchase.

In head to head experiments we have found we get more responses to these more conceptually fun questions too.

You could turn it into a full blown game by adding to your list of brands some fake ones and challenge respondents to pick out the real brands.  We have used this approach on several occasions, its more fun for respondents and the data you get back is almost identical to prompted brand awareness.  You could also show them a facet of the packaging of a brand and see if they can guess which brand its for, or show them a de-branded ad.

I would also advise jumping straight past the specific awareness question and asking what range of brands they have purchased on there last 10 shopping occasions. The has very high correlation with actual sales c0.8 and again cuts to the heart of the problem, if they are aware of it but not purchased it in any of their last 10 shopping occasions its not on their purchase radar!

If you want to defend the use of this question in your surveys and have got a strong argument for doing so i would love to hear your thoughts.

Sunday, 7 December 2014

How I feel is how I choose: A Gedankenexperiment

As market researchers we like to classify people and in particular we like to classify how people make decisions. However, we have a dreadful habit of thinking that there are different types of people who think and make decisions in these different ways.  We define segments like loyalists and switchers, impulse v considered shoppers.

The reality is that how we feel so often dictates how we choose; and our ever changing mood states mean we are all manner of different types of shopper all rolled into one.

We might have...our stress choice, our curious choice, our distracted choice, our ‘I've just been paid’ choice, our practical choices, our happy choices, our don't care choices etc....

The process of observing things changes things

All these different thinking process are all bundled together and co-exist in a quantum style mix - we are all or nothing of these or a random mix at any one point in time. It’s difficult to determine, and - just as Schrodinger pointed out - just the process of observing changes things.  You ask someone why they have purchased something and it immediately puts people into an analytical thinking framework, far removed from the mind state they might have been in when they made the purchase.

The biggest problem for market researchers trying to understand decision making processes is how to simulate these different moods to be able to effectively measure these different choice situations and how to take account of observational biasing effects.

Some mental states are easier to evoke than others.  The price conscious mood state and the impulse mood state are actually quite easy to simulate in say an online piece of research. You just ask people to either shop with a price conscious budget or ask them to perform the choices quickly.

Some of the others, though, are a lot harder to evoke. For example our distracted shopping mind-set is an important one, as when we go shopping it’s likely that we will often get distracted and have other things on our minds while we do it e.g. I am having a relationship crisis and as I go round the supermarket I am thinking mostly about that and making some choices on auto pilot.  What are these choices like? Are they the same as when we are, say, in a hurry? I think not. The choices would probably be the more habitual ones and could perhaps be more reward driven and may also be quite impulsive.

How you evoke these mind-set in a survey is a difficult one.  If you ask people why they bought something their rational thinking processes kick in and what you get out is in effective cognitive dissonance - we can shape our reasons for purchasing around what makes us feel good so often.  

Don't get me wrong; this is useful information in itself. But it can hide other less conscious factors that for many marketers are the factors we are most interested in.

e.g. to say “I bought it because it was cheap” could hide some resentment to the purchase. In a sense this statement is an excuse for buying it or hide some guilty feelings about buying a brand that is viewed as extravagant.

We are Bayesian decision making engines working out what would make us happy

It has been observed that when we buy things we make a Bayesian trade off prediction about what will make us most happy. On one side might be the price, how long a product will last and the risks involved in making that choice, on the other side are the benefits that the product delivers.

So to try and untangle some of this, I have been thinking about a new research technique. A form of self-observed Choice ethnography, where we get people to try to mentally map out all the different thinking protocols involved in making a decision for different products.  A sort of helicopter viewpoint on their own behaviour, the sort of thinking that takes place when you sit in front of a therapist and really try to self-observe your own thoughts and feelings.

To test this idea out, I conducted a Gedankenexperiment, a thought experiment on myself to see how easy it was to self-observe my decision making protocols.

What I found was that the more I thought about it, the easier and more interesting a process it became….

My Gedankenexperiment

I started to observe my own behaviour and traced out my decision making on a variety of different types of purchases. 

I started out by imagining my choice of beer in a pub, thinking about it. Here are some of the potential factors that I decided were influential in my choice:

What I fancy,
What choices they have available
What others are drinking
What I had last time
How much I had already drunk.

Interestingly, when I examined my decision making behaviour in a pub, the first thing I learned, was that it was never about what it costs.

Even more interestingly I realized that when I go into my two local pubs,  in each one I am a completely different type of shopper: In one, perhaps because it is more conventional with a limited choice of beer, I always buy exactly the same brand of German lager. I never vary my choice of drink ever...I thought about it and realized I am buying instant relaxation. That brand has been ingrained into the experience.  

In the other pub, which has a fuller range of craft beers, I never, ever buy the same beer twice and certainly never ever buy lager.  I take pleasure out of trying different things and so I move from one pub being beer monogamous to another where I am completely polygamous.

I then started to think about my choice of a beer in a supermarket. These are the criteria that I process in my mind:

What they cost
What they have
What l like
What I have not tried before
What looks nice

In the supermarket the look of the beer became paramount compared to the pub where it was the taste. Additionally, in the supermarket cost suddenly becomes near the top of the list.

Then I thought about my choice of wine in supermarkets...

A weird thing about my wine shopping is that I realise I decide based on trying to game the system as here there is actually too much choice... what I do is I look at all the brands on discount and try to find the ugliest bottle. My reasoning is that is probably the best tasting wine but it’s on discount because nobody wanted to buy it because of the ugly packaging.

Next, I thought about my choice of shampoo. This boils down to:

What they have
What it costs 
What I can bear to buy

I end up picking the cheapest brand that has the least offensive packaging. But it often takes several minutes to decide.

Compared to choice of deodorant & toothpaste….do they have Dove/Colgate, is it on offer, yes but my decision making time is seconds. In both these categories I have made up my mind as to what brand to buy and have stopped deciding.

This went on and I examined my thinking process across a wide range of product purchases, trying as best as possible to observe some of the less conscious factors. 

I realise, for example, that my choice of confectionary was triggered often by childhood associations.  would literally buy a chocolate bar to try and feel like I felt in a situation when I was younger.

What have I learnt about thinking about my own behaviour?

1.    How dramatically varied it is, I could be described as both a loyalist and a switcher. From a market research point of view it would be almost impossible to classify me as one thing – and I am sure the same could be said for anyone.
2.    I realise I was able - by simply observing my own thinking process - to gather a range of personal “incites”
  • I buy old things in new places where I don't feel comfortable
  • In new places where I feel comfortable I buy new things
  • In familiar places I buy new things to break out of the routine sometimes
  • I buy a limited portfolio of things if I am unsatisfied with the portfolio on offer generally but hold resentments to the products I buy and so am not loyal
  • I am an extremely loyal purchaser once I have made up my mind about what is the best product
  • When I have not made up my mind I vacillate
  • When there are more than one product I like in a category I sleep around, so to speak, and can be very promiscuous (wine is a good example)
  • When I am buying something from a category for the first time aesthetics control so much of my decision making protocols - what it looks like is key and I rely on design cue that basically say that product understands me
  • When I don't like any of the packaging this is when I become an instantly disgruntled shopper
3.    The process, once I got into it, was fun, easy and quite cathartic, and I am sure with a bit of explanation anyone could use this same process easily on themselves, but it does take time and thought.

I am left with the thought that this would be a very interesting process to do on a larger scale, so that is what I am looking at doing next….watch this space.

Friday, 19 September 2014

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