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The Most Important Piece of Research a Company Will Commission

Tue, 30 Apr 2013 09:05:55 +0100

All consumer research can be set within the wider context of the different phases of marketing activity. At Bonamy Finch we conceptualise these phases as the Product~Service Management Flow. This “flow” begins with strategic market planning, through product or service development and launch, before completing with brand and product/service portfolio management (after which the cycle begins again). One of the key activities within the initial strategic market planning phase is to decide who is in (and out) of your broad category target market.

Having a clear answer to this fundamental strategic question is crucial for structuring all the subsequent research and planning activities within the Product~Service Management Flow. If you don’t know who to talk to, or who to develop offerings for, then you really are just shooting in the dark. And if you haven’t got clear definitions of which people are in and out of your market, then you can’t size the different groups and you can’t evaluate the potential of any offerings that are developed!

Who is your target market?

Given the centrality of this issue to all marketing activity, it is surprising how few companies have a clearly structured view of who is in their market. I’m not talking now about segmentation. That’s of crucial importance too, but comes after the more basic task of defining who the market is. As an example, we were recently asked to discuss our approach to segmentation with a large UK fashion retailer. During the meeting I asked if they would be able to provide quota information to us on how we should structure a quantitative sample. They said no. I then asked, in the absence of that information, what their basic criteria are for recruiting people for their research. They said they had a broad target market, and so it depended. When I pushed them further, they said “mostly younger females”.

I have had a similar conversation many times with similar answers: “Middle-aged housewives with a reasonable income” (appliance category); “Men who are interested in cars” (car battery charger category); “Women who have tried botox” (facial aesthetics category). Such vague target markets provide very insubstantial foundations for subsequent marketing and research activities.

In recent weeks ‘knowing your customers’ has been a theme running through the editorial of Marketing Week, further highlighting that there is enormous scope for companies to improve in this area.

Market Tier Definition

Given our experience, whenever we now discuss segmentation with clients, we begin by discussing their definition of their target market. If clarity is lacking, we incorporate market tier definition into our overall approach, which is a two phase research study. The first phase defines who is “out of market” altogether. In the car battery market, for instance, “out of market” was defined as those without a full driving license and no access to a car in their household. The second phase identifies four tiers of consumer, (who are all broadly “in the market”), based on their potential future category value. The consumers with the highest future potential value are the core tier. All definitions are specific to the category, and based on category ownership, behaviour and attitudes. These two research phases allow us to recruit and structure our main segmentation sample accurately, which in turn permits accurate sizing of segments and opportunities.

The benefits of market tier definition do however extend way beyond segmentation. It provides a framework which should be applied consistently to all consumer insight work a company undertakes (so that the findings can themselves be consistently interpreted). Moreover, in the absence of a full segmentation, it delivers a clear framework, easily communicated within the organisation, to help structure the marketing activities within the Product~Service Management Flow. Given these benefits, Bonamy Finch has recently launched Market Tier Definition as a stand-alone research project.

We believe that conducting such a study is the most important piece of consumer research a company can undertake, and would be happy to talk with you about applying it to your category.

How DEFRA Is Using Market Tier Definition to Save Our Bees

Tue, 26 Mar 2013 14:59:00 +0000

British bee numbers have fallen dramatically in recent years. You may have heard about this, but how many of us know why bees are so important? Having worked recently with Defra, providing research to help them get more people engaged in biodiversity issues, aware and taking action, I have learnt a lot about how the natural world works together to provide all kinds of lovely stuff for us. For instance, bees are vital to:

» Our food supply - they pollinate most of our fruit and veg

» Our economy - without bees costs for farmers would rise, meaning higher food prices

» Our quality of life - our gardens, parks and countryside

After the major DIY chains withdrew products concerning certain insecticides thought to be harmful to bees at the beginning of the year, I read last week that garden centres are the latest retailers to remove household products containing such insecticides from their shelves.

Engagement with Biodiversity

But how many of the public are joining the retailers with personal concern and action regarding declines in animal and plant species? How many actually know or think about these changes and their impact on our daily lives?

Bonamy Finch has been working with Defra to find out. The first stage to getting people more involved in any issue, (or with any product or brand), is to understand how engaged with the category people currently are. Previously in this blog we have talked about this in relation to Market Tier Definition. This approach:

» identifies, sizes and values consumer groups based on current behaviour and future potential

» provides a base to develop a clear and targeted marketing strategy, with likely size of prize

We suggested in our blog that Market Tier Definition might be “the most important piece of research a client will commission”

The Benefits of Bee-ing Informed

Our recent work with DEFRA enabled us to apply the Market Tier Definition approach to a non-commercial setting for the first time. Specifically, it enabled us to provide a clearly structured view on the thorny issue of how to define engagement with biodiversity issues, and to quantify people in terms of different levels of engagement.

Using our tried and tested process, together with DEFRA we have developed a comprehensive engagement framework that provides an important benchmark for future biodiversity focused initiatives. DEFRA have also been able to identify sectors of the public they might wish to target, either with policy measures or for further research to gain richer understanding of the drivers of engagement.

Why are we blogging about this? Well - it’s a success story that we’re pleased with, but more importantly it highlights again how Market Tier Definition provides a great foundation for subsequent consumer insight and targeting. The benefits of being informed in this way are wide ranging and enduring, so please do contact us if you would like to discuss how we could apply our approach to your business.

Segmentation: A programme, not a project

Wed, 20 Feb 2013 09:28:40 +0000

Last time, I talked about the process we employ at Bonamy Finch to make sure our segmentations hit the mark. It’s notoriously difficult, with many of our clients providing war stories about undifferentiated, uninspiring consumer stereotypes. We recognise that, more than all other research studies, segmentations require a custom-built, consultative approach at all stages: Scoping, design and delivery.

So it's fitting that I'm typing this blog post on the way back from a kick-off workshop in Sweden, for a segmentation programme we're starting. And the word 'programme' is crucial here. Last night our client, plied with a couple of eye-wateringly expensive beers (look, we're market researchers, seldom endowed with limitless expense accounts), confided that, of the four proposals he received, only ours understood this. We recognised that segmentation can't be treated in the same way as a product test, or any other 'project' that is 'delivered'. It must be supported, and crafted along the way. We need to be flexible in our approach and outputs. And it takes time and effort on both sides.

This post outlines some of the different factors that can help craft the optimal solution. Having run over 500 segmentations, we have a pretty good idea of what questions we need to ask in stakeholder interviews, and what the answers tell us about the best route to take.

Asking the tough questions

Firstly, and most importantly, we need a genuine answer to the question: "why do you need a segmentation?" The answer to this is not as straightforward, or as forthcoming, as you may think! Unacceptable answers include "because our competitors have one", or "because our CEO wants one".

Often, after some prompting, we generate a list of different activities that the programme could influence: marketing and communication; target identification; brand positioning and portfolio management; innovation; sales and trade management.

The next thing to do is to start making some tough decisions. As Leigh mentioned in a previous post, one segmentation cannot perfectly inform all of these business functions while packaged into a framework simple enough for all stakeholders to take on board.

This is not a cop-out, and don't believe anyone who tells you otherwise. Having said that, it is possible to generate good differentiation across a number of dimensions, to give the maximum usefulness to a number of stakeholders. Our custom-built software allows us to do this much more than the off-the-shelf packages. But the segmentation still needs to have a single ‘base’ dimension to work from.

Let's take a few, simplified, examples. Demographic or behavioural solutions are best suited to segmentations that need customer database integration (unless you can tag the database with additional questions). Breakthrough innovation benefits from high-level attitudes, albeit ones that still maintain a category focus. Portfolio management suits a needs-based approach, where brand performance on those needs can be overlaid, to provide a roadmap for better differentiation. Repertoire markets often need a multi-modal approach, crossing consumers with consumption occasions, to best understand in-the-moment decision-making. B2B markets often have a need for commercial relationship segments, but crossing these with attitudinal archetypes to provide tailored CRM messaging. E-commerce businesses often benefit from segmenting on specific barriers and triggers; and fledgling markets often require an understanding of the market tiers, in terms of current or potential market involvement.

Crafting the optimal solution

Annoyingly, things are rarely straightforward enough for us to just pick a solution from a menu. Multiple stakeholders must be kept satisfied, and arguments can be made for a range of possible solutions. But this initial conversation must happen, and it must be open, honest and thorough. Only once all requirements are on the table can we prioritise them.

There are also softer factors affecting the course of the segmentation, such as company politics, culture & context:

What are the prevailing winds when it comes to segmentation? Have there been any previous disasters?
What are the internal capabilities? A complex solution might fit their requirements best, but is the business ready for it?
Are there global versus local considerations to be taken into account? Does the solution need to provide a lingua franca across the organisation, or can it be tailored to market-specific peculiarities?

Basically, it's a tricky balancing act, and there's no one-size-fits-all solution. In this case, the ‘Garbage In, Garbage Out’ cliché is true. Don’t expect a world-class solution if you haven’t asked the client what they want to use it for, and how their business might work with it.

But feedback from our segmentation programmes has shown that a rigorous examination of the landscape at the start of the project reaps huge rewards at the end. Our experience in designing and activating segmentations means we can help end clients and agencies maximise the benefit they get from these hugely rewarding programmes.

How long is that piece of string, exactly?

Tue, 29 Jan 2013 10:01:14 +0000

Last week I noticed a question on LinkedIn that I’d missed a couple of months ago: “Which customer segmentation strategy makes the most sense - behavioural or psychographic?”

Most responses focused on how answering the question itself should be tackled rather than answering the question itself. People weren’t advocating one approach over another – almost all responses suggested, to a degree, “well, it depends” – which is of course a very sensible reply.

So what are the questions we need to ask, to answer the question?

On the one hand, we saw quite technical arguments. For example, “The one that gives the greatest inter-cluster distance and the least intra-cluster distance”. Countering that, we saw replies that were more ‘fluffy’, such as, “What need is the customer buying our service seeking to satisfy?". Finally, people advocated a predictive approach - find the dimensions that differentiate most on consumer behaviour, and you have the most powerful segmentation.

Tail Wagging The Dog?

The answer, of course, is that ‘all of the above’ are important. There’s little point in providing, say, a demographic segmentation to a business that wants to use it for portfolio optimisation and NPD. Equally, a perfectly thought out segmentation process can be rendered useless if the differences between them aren’t strong enough to make the segments intuitive, targetable or predictive of category choice.

But, in our opinion, one of them is more important than the other. And there’s really no ‘one size fits all’ solution to this.

First, let’s take the technical argument. While the more statistical metrics of segment quality are worth keeping an eye on, they become more of a hygiene factor if you know what you’re doing. And, frankly, we know what we’re doing - the software we built here at Bonamy Finch allows us to get the maximum differentiation from the maximum number of dimensions. There’s a sense-check to all of our solutions in terms of robustness, differentiation and future predictive power. But it rarely becomes part of the final decision-making process.

With respect to the behavioural argument, it’s critical to get good brand and category differentiation within your segmentation, but it shouldn’t be the sole factor driving it.

Segmenting purely on what differentiates category choice might not even be that useful to the client. For example, we know that if we wanted to predict category choice in the spirits category, age & gender would do a good job. Whisky = old men, vodka = women, tequila = young, and so on. But what would I do with this information? Surely, for brand development, or for target identification, I need my segments to have deeper & richer differences than a pure demographic approach could give me?

We’re on to you...

Which leaves us with the ‘softest’ approach: Evaluating the client’s needs. It’s the hardest to define, and our experience has shown that these needs may be revised as the project progresses. But it leads to segmentations that are fit for purpose.

It requires a full appraisal of the client’s commercial and operational situation. Database considerations? Existing systems & hypotheses? Dare I say it, company politics?
It requires an open, constant dialogue with the client, involving stakeholder interviews with the key users, and iterative questionnaire development and analysis phases.
It also requires flexibility in deliverables, and an appreciation that different audiences might need completely different outputs, rather than a cobbling together a few slides from a ‘main deck’ (whatever that means).
Finally, it requires a commitment from both agency and client to drive the segments through many aspects of the business, and to educate the end users on what the segmentation should, and should not, be used for.

So, I guess I’m adding to the debate by saying “well, it depends” too. But I think ‘it’ depends far more on the client’s requirements, than on any single metric we can show them.

Having said that, we’ve still not answered the original question – which segmentation is best? Next time we’ll talk in more detail about the questions we need to ask, and how the responses drive our thinking when deciding on the best approach.

Ch-Ch-Ch-Ch-Changes to segmentation

Tue, 08 Jan 2013 15:12:17 +0000

I heard a new track by David Bowie on the radio this morning. I know - I thought he’d retired too. I liked some of his stuff during my yoof, (Ashes to Ashes still one of my favourites) but let’s face it, things really went downhill with all that China Girl guff – and they never recovered. (Although I did like his cameo appearance in Extras).

One thing about Bowie though, he was very good at ch-ch-ch-ch-changing, and was constantly reinventing himself. Long before Beyonce perfected the art of speed costume swapping, Bowie was shape shifting from Ziggy Stardust to the Thin White Duke to that weird clown bloke in the Ashes to Ashes video.

Just a shame he can’t change himself back to someone who can write decent songs.

Personal commitment

Whereas we would certainly raise a few eyebrows if we followed David Bowie’s example and dressed like ketamine addled clowns for work, most of us could benefit from his willingness not just to embrace change, but to actively ferment it. And of course this is particularly relevant at this time of year, when people are making all sorts of personal commitments not to do X, start doing Y, and definitely not to do Z after too much X.

Followers of this blog will know that we at Bonamy Finch work a lot with segmentation. (Last count we had worked on over 500 since 2005). And listening to David Bowie this morning, whilst reflecting on how serious my commitment to certain newly introduced behavioural standards might be, I considered (again) the various ways in which our approach to segmentation should be changing.

What happened next?

During these reflections, I happened across a short piece by a senior database analyst in the latest Research magazine. His article makes the point that a lot of latent insight is held within a customer database, and that proper integration of the database with other insight functions would be very beneficial to companies.

Generally a very valid point, and one particularly germane to segmentation. Many years ago, when I first began working on segmentations, I would ask potential clients if they had a customer database and if yes, how they used it. Occasionally the client had a database that was exploited in day to day business activities, (mostly financial and telecoms clients), and so we then went on to discuss how our segment approach could be overlaid on to this database.

These days, due to the rise of e-commerce, nearly every client has some form of customer database that feeds directly into business activities. And if they don’t now then they probably will do within the next few years.

The problems this presents for segmentation, and which it must evolve rapidly to deal with effectively, are as follows:

1. The CRM function, or online marketing department, may not be integrated into the part of the company commissioning the segmentation. Developing a segmentation in isolation of the database team, and then trying to graft it on to a database as an afterthought, is a recipe for disaster. I joined a company that was trying to do exactly that, and I spent a year sorting out the mess!

2. Transactional and socio-demographic (descriptive) variables of the type that are usually held on a database typically have only weak or moderate relationships with attitudinal, motivational and belief based variables. These latter variables, which are more explanatory in nature and particularly insightful for communication and opportunity development, are therefore often the drivers of segmentations commissioned by brand and comms teams. Ignoring either set of variables whilst developing a segmentation will seriously limit its impact in your business.

3. The expectations, and therefore success criteria, for a segmentation differ significantly between CRM teams and more (above-the-line oriented) brand/comms teams. (A brand director who wants to know what postcode someone lives in? A CRM director who wants to know if a customer’s personality is more self-expressive or ambitious?). There is no magic wand to wave to solve this – rather it must be built in to the segmentation approach from the start.

We are ten days into the New Year. So far this year I have been involved in a jewellery segmentation proposal, activating a magazine publisher segmentation, and developing a segmentation in the footcare category. Database integration is crucial to the success of 66.67% of these projects, and I expect that to be fairly representative of all of the segmentations I will work with this year.

We know this, and so our approach to segmentation will continue to ch-ch-ch-ch-change and adapt to the ch-ch-ch-ch-challenges of rapidly evolving business models.

Happy New Year.

Our Top Blog Posts of 2012

Thu, 20 Dec 2012 10:55:09 +0000

The end of the year is a good time for planning and also for reflection. Typically the TV schedules are full of c-list celebrities talking about how great particular moments of The Olympics / The Jubilee / I’m a Celebrity were. Not to be outdone, we’ve come up with our own countdown of the best of ‘Insight Bites’ from the last year. In 2012 some of our blog posts have created more reaction than others. Some have had more mentions and ‘retweets’ and some have had lots of people come and read them.

Here’s a run-down and brief summary of the top 5 (click on each title to read the full blog post):

5. The tyranny of choice

We do a lot of studies with the aim of optimising product ranges in order to achieve the best levels of purchase and profit. This article looked at the importance of considering the impact on survey design when there might be too much choice.

4. The dangers of having too broad a target

How do you position your brand, which customers do you target and how do you innovate for them? Here we higlighted some examples of brands creating products specifically for women and the dangers of ‘lazy’ marketing.

3. The important difference between practical and statistical significance

We often encounter clients who hold a lot of store in ensuring that differences in data are statistically significant. Here we looked at the need for researchers to understand the limitations of statistical significance and how to draw conclusions about the practical significance of differences in our data.

2. Do you need to ask questions about the quality of your data?

In June, Leigh presented at an MRS Members night about the importance of good data quality. We see too much data come through our doors from other companies where not enough attention has been paid to the collection of good data that helps create good analysis. We delivered five recommendations for questions clients should ask their agencies about data quality.

1. Lessons learned from conducting more than 500 segmentations

Our biggest area of expertise is segmentation. From creating solutions using data supplied to us by our Advanced Analytics clients, to end to end segmentation development and activation, the team has lots of experience that we wanted to share with you.

So that’s it for another year, pop-pickers! We wish all our blog readers, clients, suppliers and friends a very Happy Christmas and a prosperous and exciting 2013.

See you next year!

It’s Like Deja Vu All Over Again

Thu, 18 Oct 2012 14:18:27 +0100

I spent a large part of last Saturday in the Bonamy Finch office putting together some specific case studies that we needed quickly for a client. I emailed them off at about five o’clock, cc’ing my colleague Paul. At about eight o’clock, when most sane people are just getting ready for the X Factor (Gary’s getting a bit touchy in his 40’s, isn’t he?), Paul emailed me back to say thanks for putting the case studies together, but couldn’t I have told him I was going to do them, as he had spent his Friday evening also writing them.

I emailed him back, saying that that was a frustrating duplication of effort, but at least they were done....

I don’t live with my Mum

My mum lives in Melbourne, but is currently staying with me for a few months. (I say “staying with me” because if I say “living with me” then this can easily get Chinese whispered into “Did you know Leigh is 44 and still lives with his mum?”). I would describe my mum as traditional, in the sense that she likes to run the domestic side of the house - in fact any house she is in for no matter what duration. Since she arrived I honestly have not done a single load of washing or ironing, and know I won’t until she returns to Oz in January. I do however still like to have a browse round the local Waitrose (coincidentally the one Mourinho used to get his eggs from), and get a few essentials to tide me over the weekend.

After leaving the office last Saturday (before the “Curious Case of the Duplication of the Case Studies” had come to light), I picked up some milk, bread and mushrooms from Waitrose. When I got home and went to put them in my fridge, my Mum had beaten me to it. She had bought exactly the same things for me earlier that day (except she had also bought me some sausages, which I am trying to avoid).

Am I bothered?

So, in the space of a couple of hours, I unwittingly engaged in two clear acts of duplication of effort and unnecessary cost (of money or time).

Should this bother me? Isn’t such duplication just part of being a perfectly imperfect human – where actuation falls short of intention, where poor communication stymies achievement? Maybe. But regardless, it’s still really irritating. And yet...well....isn’t there a lot of duplication of effort, particularly when it comes to our work, that we just seem to put up with?

Things like...

Going through a presentation separately for someone who couldn’t make the original meeting.

Charting data separately for one market when you’ve done it three times already for other markets

Writing questionnaire content where you’ve covered something very similar for another client before, but can’t be seen to just copy and paste?

And if occasionally your work feels like some kind of insight frenzied Groundhog Day, imagine how members of internet survey panels feel. There are some things that every company needs to know from respondents, regardless of whether they sell sambuca or nappies. Media usage of key segments is one such information area, and respondents are subsequently asked about it repeatedly. This duplication must really get on their nerves.

The Segment Media Reports

Taking the example of media profiling, instead of this duplication and frustration, imagine a situation where a research agency develops a single comprehensive media profiling questionnaire. It’s all ready to go, so no need to spend a lot of time writing a new one for every new client. And because the content is stable (just tweaked yearly), the reporting is automated. Again, no need to spend a lot of time and effort on this. Which makes it quick. And also cost efficient.

You would just send your segmentation algorithm to the research agency, and a few weeks later get back detailed media profiling (through clear automated reporting) for your segments and lifestage groups (if you are interested in these).

Well we did imagine this, and so developed The Segment Media Reports, which we are proud to launch this week. Contact us to find out more about our innovative approaches to streamlining research and generally making your (working) life more simple.

Duplication of effort?

Case closed.

This Blog Post is Not For Men: The Dangers of Targeting a Broad Demographic

Thu, 13 Sep 2012 16:20:04 +0100

Since László Bíró first introduced his creation to the world in 1931, ballpoint pens have evolved quite a lot, or not very much, depending on your point view.

For the vast majority of people, men and women alike, there seems to have been little call for change. The furious scribbling in the margins to make them work is part of their charm, isn’t it? And the liberating feeling of throwing a lifeless biro across the office, for maximum office-diva effect, takes some beating.

But Bic don’t really like people throwing their pens across the room – or at least, not without some sense of emotional attachment to it. Having sold over 100 billion Bic Cristal pens, their ongoing task is to create and sustain both functional and emotional hooks to their brand, for the widest of target markets. Which means, over time, the need to segment this broad market into smaller chunks.

“What next, the vote?”

Which all leads us up to the furore over the launch of Bic Cristal For Her – a pen aimed squarely at women. Now I’m not going to quote some of the more entertaining spoof reviews on Amazon – you can read through them yourself, or catch up with the Tumblr devoted to it, or even peruse the spoof Twitter account (@BicForHer) – but it got me wondering why Bic was singled out for such lampooning. After all, we have beers for women, PC books for women, muesli for men, and obviously Yorkie, which has been aiming square-jawedly at the same gender for over 40 years, but whose message has morphed from overt-but-implicit machismo, through genuine Loaded-style laddishness (“we needed to take a stand for the British bloke and reclaim some things in his life, starting with his chocolate”), and gradually into its new self-parodying style. And, here in the office, Giselle has a pink Vaio laptop.

So, why pour scorn on Bic? Is it the product category? How can a pen be just ‘for her’? The only genuine product benefit on the Bic website is that the barrel is “thinner for a better handling for women”, but surely other products have had equally tenuous benefits attributed to them, all in the name of advancing gender-equality. One thing I did notice, though, was that many of the more sarcastic comments, of which there were a lot, focused on the implication that women could be made more intelligent with such advances in writing technology (apologies for the snarkiness – I’ve obviously been reading too many comments) – so perhaps this did have a bearing on the extent and nature of the reaction.

Light the pink touchpaper

Is it lack of self-awareness? Could a nudge-wink slant to the communication have avoided it? Unlikely. On the other hand, because it was never intended to be a dramatic shift in brand focus, but purely an extension to a particular target audience, did no-one really give the implications on the brand much thought? More likely. Or was it just bad luck – a ‘Black Swan’ that couldn’t have been forecast using research or other methods, but will provide another anecdote in How Not To Do Marketing lectures?

In any event, the reaction will no doubt spook marketers, and will make them think twice before communicating to broad demographic segments too clumsily, while considering the implications of ANY shift in marcoms on the overall brand positioning.

One other thing. I remember, years ago, a particularly long brand tracking study I worked on had two statements: “suitable for women”, and “suitable for men”. I remember that almost all brands either scored well on both, or on neither. Which showed that some brands are, effectively, “suitable for everyone”.

Something which, with 100 billion sales, Bic could probably have done well to remember.

A Bad Apple

Tue, 28 Aug 2012 16:16:11 +0100

I watched with initially only a minor interest the global legal battle between Apple and Samsung about patent violation with respect to their smartphones.

Skirmish

The first round was fought in the contender’s home ground – South Korea. A disappointing affair: a minor skirmish with little excitement. A score draw, with both sides guilty of technical infringements, and paltry fines for each in the region of £20,000. Not much when you make about £20,000,000,000 profit a year.

The second round was a lot more one-sided. A jury in California awarded Apple about a billion dollars for having their patents infringed (ouch), and Apple are now trying to stop Samsung selling many of its phones in the US. Apple says it’s possible to create a smartphone that doesn’t rip them off – look at Blackberry and Nokia. Samsung say it’s bad news for the consumer – and means that you can effectively patent a rectangle. (On the basis of this, if you are thinking of filing patents for squares and triangles – too late - I’ve already got mine in. Unfortunately the circle was taken a few thousand years ago).

Shenanigans

As I said, my interest in all this legal shenanigans was minor. But then I found out this: to help prepare its case, Apple had commissioned a couple of conjoint studies (one to explore preferences for smartphones, one for tablets), overseen by John Hauser, a marketing professor at MIT. If you believe the position of the conjoint software developer, and I have no reason not to, then this research was fundamental in building Apple’s legal case. Well, we all talk a lot about return on investment, and market research often struggles to establish its worth in this respect. These conjoint studies however must rank as the best return on market research investment ever – with a €1,000,000,000 return on what was most likely only a five figure investment. Perhaps every agency should have a slide on this as a case study about how research can drive profit.

Gentle wandering

Allowing my thoughts to wander gently around the case some more, I started to think a bit about what patents have been taken out in the area of market research. I can’t think of that many things we do which would be patentable (if that is a real word, which it probably isn’t). It would certainly be difficult to argue you had intellectual property over a question, and so a series of questions (i.e., a questionnaire) must also be difficult to “own”. I think that is why there are so many trademarked brand equity models. Because you can’t own the individual questions, companies combine them together using an esoteric set of coefficients which then does become something akin to intellectual property.

As I’ve mentioned before on this blog, we run a lot of segmentations. Part of our standard output on these is a profiling sheet that makes it very easy for clients to compare and contrast different solutions: this in turn makes it easier for them to choose the best solution for their business. It’s a brilliant little piece of kit, and took quite some time to develop. Our clients love it.

Unfortunately so do our competitors. Not so long ago I was asked to review a segmentation that had been conducted by a competitor, and didn’t make much sense. What I was sent was essentially our segment profiling sheet, but with the competitor logo on the front page, some rounded edges and a slightly different shade of blue for the font.

Kaizen

So what to do? I don’t know if what we have in that sheet is truly intellectual property (such as a rectangle with rounded edges?). A solicitor might have an opinion, but my recent experience is that solicitors often disagree with each other. What I do know is that as a small company we would find it difficult to pursue a case to establish whether it was indeed our IP.

Well, we decided to take a different approach. We accept that others might want to copy what we do, but they can’t copy the experience and thinking that enables us to come with these things in the first place. So we are staying ahead of our competitors by continually developing what we do and deliver. The Japanese call this philosophy of continuous improvement kaizen, apparently. In this spirit of kaizen, this week we are releasing an updated version of our segment profiling sheet, which represents a major step forward on version 1.0, making it even easier to get to grips with different segment solutions. Please contact us if you’d like to be taken through it (though not if you are a would-be competitor wearing a fake moustache and overcoat).

The Dalai Lama Does Key Drivers Analysis

Thu, 09 Aug 2012 09:17:04 +0100

I follow the Dalai Lama on Twitter (@DalaiLama), and he tweets regularly about the value of compassion in people’s lives. In fact I’d say that most of his postings address this theme in one way or another. For the Dalai Lama, compassion is the fundamental key to a happier life: a life spent acting out of concern for the welfare and happiness of others is itself a happy one. It’s a simple message, and, judging by the number of Buddhists in the world, a compelling one. Yesterday he didn’t mention compassion, but instead tweeted the following:

“Wealth may contribute to our happiness, but it’s not the most important factor; by itself wealth fails to bring us deep inner satisfaction”.

Thinking about Key Drivers Analysis

And this got me thinking about Key Drivers Analysis (KDA). Although he probably wouldn’t think of it in such terms, the Dalai Lama was offering his own model of the drivers of life satisfaction (happiness) based on a subjective KDA. Within his model, compassion might be regarded as the most important driver of happiness, and a necessary condition to fulfil in order to be truly happy. Wealth might be considered a secondary driver of happiness, but not a sufficient condition by itself to make one truly happy. You might also interpret his latest tweet as suggesting some kind of interaction effect, whereby wealth might interact with compassion to raise levels of happiness. I’d have to chat with him to see if that is what he is suggesting.

So, a few simple statements help to reveal the Dalai Lama’s implicitly held model of the key drivers of happiness. He has spent his whole life determining this model, and putting it into practice through his actions and thoughts. He clearly takes it seriously.

Problems with KDA

On a less spiritual level, at Bonamy Finch we also take our key drivers analyses seriously. Whether it’s working out what’s important, necessary or sufficient to make a customer come back for more, or say good things about you, or whether it’s exploring what brand personality attributes really push people’s buttons, we are always looking for better ways to arrive at the answers. For instance, the trusty correlation technique doesn’t help get past drivers that are highly related to each other. Regression often relies on complete datasets (where every respondent answers about every potential driver), and these are not too common. It can also throw out some very squiffy results when the drivers are related to each other. Structural equation modelling is perhaps the gold standard, but the data requirements needed to produce reliable results are often not met by research that is not designed with this analysis specifically in mind.

A better type of KDA

We have spent considerable effort exploring techniques that overcome these problems, and through this work have found very positive results using a statistical technique called Kruskal’s Relative Importance Analysis. It’s not that common – we’ve had to write our own software to run it – but it produces results that are reliable and differentiate clearly between the importance of drivers. Not only that, but it can deal with missing data and variables that are strongly related to each other. For those reasons we have made it our default approach to KDA. (Although of course, we always look at the research thoroughly before deciding what the most appropriate approach is). We have written a concise overview of Kruskal’s Relative Importance for KDA, and how this can help deliver clearer, more reliable research results. Please contact us if you would like a pdf copy.

Now I have to go and start being compassionate…

NB. This blog post was first published in March 2012 and is re-published now to coincide with the release of our new Kruskal's RIA software (contact us for an overview dcoument). Please take time to look at some other prevoius blog articles by navigating through the 'Browse by Date' menu on the right.

The Most Important Piece of Research a Company Will Commission

Thu, 19 Jul 2012 08:58:21 +0100

Who is your target market?

In recent weeks ‘knowing your customers’ has been a theme running through the editorial of Marketing Week, further highlighting that there is enormous scope for companies to improve in this area.

Market Tier Definition

We believe that conducting such a study is the most important piece of consumer research a company can undertake, and would be happy to talk with you about applying it to your category.

5 things clients should be asking their agencies about data quality

Fri, 06 Jul 2012 13:46:46 +0100

“What the eye doesn’t see, the heart doesn’t grieve”. Well, that’s what my granny used to say, and I used this snappy idiom as the title of a talk I gave at the MRS members’ evening last Monday. So whereas most men of my age see themselves slowly turning into reconstructed facsimiles of their fathers, I clearly need to be more on my guard against turning into my mother’s mother. Such is life.

The main point I was making concerned the need within the research industry for greater transparency in the client-supplier relationship. There is an old adage which suggests that the two things people should not see being made are sausages and laws. To these it would appear some practitioners believe we should add research findings. All too often the supplier waves ‘au revoir’ to the client when a questionnaire is signed off, only to reappear at a debrief or workshop some time later to present a fait accompli, (having had perhaps a few emails or brief telephone calls in the interim).

Oftentimes clients may inadvertently collude in such working practices by encouraging agencies to “work independently”, appearing harassed, and generally limiting the time they make themselves available to the supplier.

Greater transparency required

I believe this lack of transparency in terms of how what goes in becomes what comes out of the research process undermines the robustness of our findings and ultimately the professionalism of our industry. Greater transparency is needed now in three areas:

Data quality

Just because a client wants to know something, it doesn’t mean that it can be easily found out. We need to be much more aware of what respondents can and can’t answer well. Poor data quality is the elephant in the research industry’s sitting room!

Sample achievement

How closely did the samples achieved match the quotas agreed and ultimately the population they claim to represent?

Analytic license

What key steps have been taken in the analysis phase that had a clear impact on the results and recommendations?

Five recommendations

In my talk I highlighted these issues in relation to several questions clients should be asking their agencies, and concluded with the following recommendations:

1) Studies should be commissioned properly, with clear specifications in terms of process, sample and (exact) deliverables.

2) Measures of acceptable data quality should be explicit and agreed within contractual frameworks relating to each project.

3) Agencies should help to eradicate the main source of poor data quality by speaking more loudly on questionnaire design

4) There needs to be clearer guidance from agencies on the analysis performed, and clients need to be more prepared to engage with this information

5) A data quality summary should become a standard deliverable from suppliers, and should come before any debrief so its implications can be assessed.

On this final issue Bonamy Finch have recently produced a draft version of the data quality summary, and this is being taken forward by the MRS for further industry wide discussion. As I often seem to say, watch this space.

Footnote: A recording of the MRS members’ evening presentation I gave on these issues will be posted on the MRS website soon

500 Up: 7 Years of Segmentation at Bonamy Finch

Fri, 22 Jun 2012 13:18:31 +0100

We have a bell on a shelf in our office which we ring to mark special occasions. It was rung again this week, not as is often the case to announce the arrival in our freezer of a family size pack of Cornettos, but instead to mark the 500^th segmentation Bonamy Finch have worked on. That’s a lot of segmentations by anyone’s standards, and so in an area where the old research adage of “if you’ve done one you’re experienced, if you’ve done two you’re a guru” seems particularly apposite, we thought we would mark this company milestone with a few thoughts from our team. Specifically I asked them each to give one key thing they have learnt from all this segmentation experience. Here are some of their responses:

What the Bonamy Finch team say

“It’s impossible to look at more than two ranges of solutions in detail in one day – it kills your brain to do more!” Victoria, Senior Research Executive, Research Solutions

“Having a robust assignment algorithm that can be answered easily and quickly, so you can actually find the segments in the future, is almost as important as the segmentation itself.” Frances, Director, Advanced Analytics

“Segmentations are not research projects! They are the foundation of consumer understanding, making a daily difference at the heart of the business.” Lindsay, Associate Director, Research Solutions

“Flat data really isn’t good value for money so it’s best to include rules within the questionnaire to screen out respondents with poor data quality. And to optimise questionnaire design to make it less gruelling for people! This is a bit of an obvious one - but it is still the biggest data issue in segmentations.” Giselle, Associate Director, Advanced Analytics

“I have learnt how important it is for clients to ‘brand’ the segmentation internally – it really helps with embedding it in the company.” Paul C., Deputy Managing Director

“A lot of agencies confuse developing materials to bring the segmentation to life (for instance videos, segment rooms etc.), with actual implementation. The latter, which involves activities like concept development, brand positioning, and communications brief development, is where the real work, and value of the segmentation to the client, lies.” Lorenza, Director, Research Solutions

"Segmentation is definitely a blend of art and science. You must have the statistical rigour to execute it but you need creativity in how you design, analyse, interpret and convey the findings. One without the other will never deliver a powerful segmentation." Nick, Head of Research Solutions

“Bi-polar scales help to overcome response bias, and so we are less likely to find problematic flat clusters if they are used in questionnaires. Developing methods to eliminate response bias and country differences is all important.” Anders, Director, Advanced Analytics

“It’s more important that segments are meaningful to people, than necessarily being the most statistically ‘perfect’. So business understanding, thorough survey design and thoughtful analysis are more important than the actual act of segmenting the data.” Will, Director, Research Solutions

“Database integration should be understood from the outset. It’s no good thinking about overlaying segments onto a database after segmentation has been carried out.” Paul J., Head of Advanced Analytics

“Segmentations can be hard work, but the end benefits to the clients are huge” Manick, Associate Director, Research Solutions

And my own key segmentation learning:

Don’t underestimate the value of simplicity or the burden of complexity.

New segment profiling platform

To coincide with our 500^th Segmentation we are launching a new segment profiling platform as part of our standard deliverables. This platform simplifies the process of comparing different segmentation solutions, helping to ensure the best framework possible for the client to move forward with. More on that will follow in a couple of weeks time, so watch this space. In the meantime, please contact me if you would like to discuss Segmentation further.

What Does It All Mean?

Thu, 17 May 2012 15:32:01 +0100

As I crawled around the M25 on my way home last week, several news items on the radio caused me to question the way statistics are interpreted and reported in the public domain.

Confusing interpretation of numbers

Visiting a factory in Essex to underline the government’s commitment to austerity measures, an employee asked David Cameron why Conservatives and Liberal Democrats could not agree on new policies. The Prime Minister replied that forming new policies was inevitably more difficult because “you, the voters, decided that no one won the last election”. It is a puzzling (perhaps convenient) interpretation and not the first time politicians have suggested that the British public voted for a coalition government. It reflects the outcome more than the wish of individual voters. To use a football analogy, it’s like saying that a Wembley FA Cup Final crowd can’t decide which team should lift the trophy and so they settle for a draw. The truth, of course, is that the crowd’s support is clearly divided between the two teams. The same could be said of TV shows like The X Factor. The vote for two performers may well be very close, but that shouldn’t be mistaken for an indecisive audience. Every viewer has a preferred winner and this is reflected in how they cast their vote.

The second story related to the disturbing case of abuse in Oldham. “Nine men jailed for a total of 71 years” said the headline. What, exactly, does this mean? And why is the number 71 relevant in this story? In and of itself, it seems meaningless. Or does the magnitude of the total sentence convey the severity of the crime? Does it help the listener judge whether the sentence for any of the nine men is appropriate? Would it make the listener draw comparisons with similar cases in which multiple prison sentences had been handed out? I don’t think so. Surely the more relevant message is that each man was jailed for between 6 and 11 years?

On Twitter’s 5^th birthday, the company issued a press release boasting that “Twitter users send 1 billion tweets per week”. An impressively large number that captures the attention you might argue, but does it convey meaning? Only with further interrogation does the reader discover how this translates into the number of tweets per user per day, a much more relevant currency.

Implications for Market Research

Important parallels exist between these stories and our work. The MR equivalent of the coalition story would lead us to examine the distribution of ratings, not just the average. A new concept might be loved by some people and hated by others. It would be misleading to convey the average as the outcome. Likewise in the Oldham case, we would be unlikely to say that 9 concepts rated on a 1-10 scale attracted an overall score of 71. To promote wider adoption and appropriate use of MR requires researchers to interpret data correctly and present it in a clear and meaningful way.

And, on that note, I’m off to watch the football. And I won’t be satisfied with a draw...

The tyranny of choice

Thu, 10 May 2012 10:33:13 +0100

Recently a client came to us with a range of 58 different concepts for evaluation. This presented many tricky issues for the design of the research, and lead to a lively discussion with my colleagues regarding how many concepts a respondent could evaluate meaningfully in one survey, and how this compared to the decision making process in real life purchase situations. This in turn got me thinking about a couple of articles I had read recently about how increasing choice for consumers is creating an interesting effect. First was an article from Harvard Business Review. Its authors argued that the notion of the purchase funnel has in some instances been sidelined by other effects, most notably the purchase ‘tunnel’. This “tunnel” describes how consumers avoid the cognitive effort of careful consideration, and instead make simple choices based on the “easiest option”. Second was an old article I had read in The Economist, which talked about the ‘tyranny of choice’.

The tyranny of choice

As we have become collectively “better off”, and as distribution channels have become increasingly efficient, more and more products have become available to us. In many situations we are simply overwhelmed by choice. In the seventies the average supermarket carried about 10,000 lines. Nowadays that figure is closer to 50,000. Consider orange juice. There was a time when you had a choice of two or three different types of orange juice at the supermarket. Nowadays it’s not unusual to be faced with options that run well into double figures.

The received wisdom would have us believe that increasing choice will make us happier: after all, amongst all the options available to us we should be able to find the one that really ticks all our boxes. In fact, the reverse can be true. While an increasing range of product variants and brands has accompanied improvements in quality of life in the Western world, in actual fact there comes a point where increased choice becomes stressful.

To experience this for yourself, one simply has to walk into the local purveyor of mobile phones and be bamboozled by the choices on offer. Handsets, price plans, insurance options. I just want a phone! The information I have to take in is too much and I leave befuddled – to go away and do some more research online. That's why the insurance comparison sites have done so well. They curate the mass of choices down to a manageable number for you to compare based on your basic needs.

Implications for research

Thinking back to our initial conundrum of how to evaluate 58 different concept options, many closely related, we can see that the tyranny of choice also has implications for survey design. How does one go about replicating the real-world tyranny of choice in survey design? How do we avoid choice related studies from being too artificial, or inducing mind-numbing respondent fatigue (thereby producing poor quality data)? If consumers are now becoming used to choosing randomly (as suggested by the Harvard Business Review article), without research or using a more open-ended approach what does this mean for survey design?

It’s important that we consider these consumer purchasing and research effects in our work. In relation to the 58 concepts, we worked with our client on a two stage research design, which allowed us to explore a reduced number of concepts in a bit more depth. We also made sure that the concepts were reasonably different from each other. (Asking respondents to distinguish between ‘shades of grey’ is a sure-fire way to upset them and end up with poor quality data).

I was reading about the new Rolls-Royce Phantom last week. Apparently it has an eight speed gear box, but you can’t choose which one you are in at any time. You can go forwards, backwards, or stay where you are. Could it be that having choice removed completely is becoming the ultimate luxury?

Do you know who is analysing your data?

Thu, 03 May 2012 17:23:31 +0100

We are often asked, just what are “Advanced Analytics”? In response to this, I usually begin by saying that what they’re not are web-based techniques that measure social media traffic etc. This has become almost an industry within itself over the last few years, and confusingly is referred to generally as “Analytics”. So it’s important to make this distinction.

Getting curious – exploring hidden relationships

Advanced Analytics are analytical techniques that go beyond just describing the data: instead, they explore the hidden relationships and patterns within data. They allow us to infer things that are going on that can’t be seen with more simple cross-tab analysis. (That’s why, in statistical circles, advanced analytic techniques are often referred to as inferential statistics). And this is one of the things I really enjoy about my job: finding things in the research and data that aren’t immediately apparent, and that other people don’t necessarily see.

There are many techniques that come under the general banner of advanced analytics. Perhaps the most common one people have heard of is correlation analysis, typically used in key drivers analysis. Others include conjoint, maxdiff, factor and principal components analysis, structural equation modelling, cluster analysis, latent class modelling, linear and non-linear regression, Kruskal’s relative importance analysis, chi-square – the list of techniques available to the advanced analyst is extensive. For me, that’s one of the best things about being an analyst – you never run out of things to learn. Trusted techniques such as conjoint analysis are continually evolving, and new ones are being developed. Every year, new software versions are released which offer significant improvements on previous techniques. Keeping up with (and hopefully sometimes leading) these developments is a challenge, but a very interesting and worthwhile one.

Getting creative with the numbers

What doesn’t fit with most people’s perceptions of what advanced analytics is about is that it’s very creative. As the lead analyst on a research project, the choices you make determine the results more directly than any other team member. Whether it’s deciding on a specific data transformation to create a range of segment solutions, or how to group potential drivers in a customer satisfaction analysis, these decisions ultimately rest with the analyst and their knowledge of the business objectives of the analysis.

A lot of companies only have one or two analysts, and in such a context it’s easy to become pigeon-holed as a number crunching boffin. At Bonamy Finch we have a big team of commercially astute analysts who can bounce ideas off one another. We like to work in a way where the analyst has a full understanding of the wider project, its context and business objectives. We don’t just crunch the numbers, we think about an intelligent end solution.

When selecting an agency how much thought do you give to who will be analysing your data?

A short note on the practical significance of being tall

Thu, 19 Apr 2012 15:07:34 +0100

I’m five feet nine inches tall. How would you describe me? Average height? A bit below average? Short? I’m guessing you wouldn’t call me tall. I can’t say that it was a conscious recruitment decision criterion, but amongst my colleagues this does actually make me above average. Only one of my colleagues is clearly taller than me (if you exclude our IT support chap, who at about 6 feet 7 inches is clearly an outlier).

When I was a teenager I learnt the difficulties of analytical forecasting the hard way by plotting my own growth chart. Unaware as I was at the time of ceiling and threshold effects, and by ignoring the fact that no member of my family had exceeded five feet eight inches, I predicted for myself a final achieved height (at the age of eighteen) of 6 feet. All went well for a couple of years, with model validity proving high, until at the age of sixteen things started to slow down. And then at some point, having achieved the not too giddying height of five feet nine inches, I stopped growing. Thankfully I had my sense of humour to compensate for lack of imposing physical stature. I also found solace in the fact that at the time the average male height in the UK was exactly five feet nine inches. So I was, in this respect, reassuringly average. And definitely not short.

US TV Advert Shocker

I was in the US last week, and an advert came on the television for shoe inserts that promised an instant height increase of three inches. The advert showed a man standing next to an attractive woman who he clearly fancied and who equally clearly was not the slightest bit interested in him. A quick visit to the “Magi-Lift insoles” website, and a three inch lift later, the same man walks up to the same woman and she is all over him like a bad suit. Watching US TV adverts can quickly immure you to the asinine, but what particularly seized my attention was the man’s “before” height: five feet nine inches. Any illusory beliefs I had clung to in my adult life of average heightiness were cruelly, and irreparably, shattered.

Being short is no small matter

Whatever. As I say I have my sense of humour to fall back on. But it does bear upon an issue that I initially raised in my blog a couple of weeks ago: that of the practical (as opposed to statistical) significance of differences we might report in our research. At five feet nine inches I am, (by US TV advert standards at least), short, but if I had told you I was six feet in my opening sentence, you might well have described me as tall. Yet the latter is only 4% greater than the former. So we have a small difference in absolute terms, but one of very practical significance in terms of how I am perceived by others. For instance, tall people are perceived as more confident, successful and attractive than short people, are more likely to be given a job interview, get more responses from personal adverts, and are also more likely to become President of the United States*.

What does this mean to researchers?

Now consider a research debrief in which one new product concept has 4% higher appeal than another. Or Brand A has 4% higher awareness than Brand B. Or the Southern region has after-sales service satisfaction 4% higher than the Northern region. Whether or not these differences are statistically significant will depend largely on how many respondents the research budget could stretch to, and the variation in peoples’ responses. But are they practically significant? Will the first concept perform better in the marketplace? Does Brand A convert more people to purchase than Brand B? Should the Northern region follow the practices of the Southern region? Who’s to say?

Well, we are. Professional researchers need to understand the limitations of statistical significance, and how to draw conclusions about the practical significance of differences and patterns in our data. If we can’t do this effectively, then we are not in a position to make well founded business recommendations to our clients. My feeling is that it’s not something our industry excels at, but I could of course be wrong!

Although that might not be significant.

* I remember the following factoid from one of my social psychology textbooks: the vast majority of US presidents have been taller than the defeated candidate, and in the few instances where they have been shorter, they are generally actually perceived by voters as being taller.

Money talks: Rewarding better survey design

Thu, 12 Apr 2012 17:35:59 +0100

Long. Boring. Poorly written. Irrelevant. Repetitive. Just some of the widely documented criticisms levelled at many of today’s market research surveys. Much of what we read and hear anecdotally suggests our profession continues to create a negative experience for survey takers. They endure dull questionnaires and are left feeling frustrated, with a negative impression of market research. Why does this situation prevail?

Where’s the motivation to improve?

It strikes me that researchers have no real motive to address this issue. There’s an almost endless supply of panellists out there and it doesn’t matter if we upset a few of them, does it? As long as we get our data, we’re happy. Anyway, the panellists are getting paid for their time. But it’s ironic that, in designing the tools to “listen to the customer”, the MR industry fails to do exactly that. Is anyone really thinking about the poor respondent? What about the data quality?

This made me think about how other industries operate in order to create a competitive market place and weed out the weakest performers. These days we are invited to rate just about anything and everything – the latest book or CD bought on Amazon, your recent stay at a hotel, the service you received at your bank. The list goes on. And people take notice of ratings.

Systematic rating of surveys

Could the same model be applied to data collection in the online world? Why not adopt an Amazon style rating system for surveys and apply it to data collection across the industry? If MR really cares about respondents and we all want top quality data from our research, why not ask the very people completing those surveys what they think? Then reward or penalise the MR agency according to what the people say.

How would this work in practice? Across all panel companies, respondents would be asked to rate every survey they take on a standard “enjoyment” (or similar) scale. Authors of the most enjoyable surveys (as rated by respondents) would be rewarded with favourable data collection cost rates (because fewer drop-outs, more enjoyment / satisfaction, less churn etc.). Authors of the worst surveys would be penalised with higher cost rates. This is no different to, say, the car insurance market – those displaying the best behaviour are effectively subsidised by those deemed most risky / likely to offend.

Is this the future?

This would surely provide a tangible motive for every survey writer to brush up his/her survey design skills. It would create a currency (think NPS) through which MR agencies could sell themselves to research buyers. Which agency wouldn’t covet the title “best survey writer 2013”? Data quality would inevitably improve. Competition would intensify and standards would be driven up. And let’s not forget the ever suffering respondent, who would encounter more innovative, thoughtful and shorter surveys.

Could an industry-wide solution work in practice? Survey writers – how would you feel about your ratings being publically available? Research buyers – would this solution influence who you choose to work with?

Red Meat, 'Wonder Drugs' and Flossing

Fri, 30 Mar 2012 11:42:27 +0100

A real brouhaha followed the recent wide reporting of research into the perilous effects of red meat consumption. Having been a veggie for several years in my youth my interest was well and truly piqued. The headline finding was that “eating an extra portion of red meat every day will increase your risk of death by 13% annually”. Given that, with the notable exception of earthbound deities, our risk of death is nominally considered to be 100%, the headline by itself raises some interesting issues about our use and interpretation of statistics.

But what does this actually mean?

No matter. What was particularly interesting in the reporting of this finding was what it actually means to people. Enter the dubiously titled Professor Spiegelhalter, a Cambridge University biostatistician (no, I didn’t know there were such things either) who is currently the Winton Professor of the Public Understanding of Risk (ditto). He said to imagine two versions of yourself at 40, one who eats just 3 ounces (85g) of red meat a day, and one who eats 6 ounces (170g). The second version of you would die at 79, the former at 80. Cue a big sigh of relief from the committed carnivores amongst us, for whom this difference seems rather small, and not nearly as important as a 13% increase in risk of death! But before breathing a collective sigh of relief and flossing the bits of meat from between their teeth, Prof Spiegelhalter suggests the committed meat eaters could instead view it as each time they are eat their extra daily hamburger, they are actually shortening their lives by more than half an hour! Suddenly it seems a bit more serious...

The way we frame data is all important

This is a very clear demonstration of the importance of how we, in the research and analytics business, not just analyse data, but how we frame it. We have probably all been in meetings discussing not just the statistical but practical significance of a 0.4 mean increase in customer satisfaction on a 10 point scale. What does it actually mean to the client? Is it enough to poach customers, drive loyalty, or differentiate their offering? The illustrations put forward by Prof. Spiegelhalter remind us of the power of framing data in a meaningful way.

Another debate surfaced again last week – that of the potential benefits versus risks associated with aspirin. Advocates portray it as a ‘wonder drug’, with continued low dosage drastically reducing risk of heart disease and cancer. Others, however, point to increased risks of internal bleeding as outweighing these potential benefits. During a considered, extensive (and very dry) debate on the latest aspirin research to be published in The Lancet, the research’s author repeatedly demurred as to whether people should take a daily aspirin. He suggested instead they ask their doctor. Finally the interviewer said “The people listening to this interview will want to know what you do. Do you take a daily aspirin?” To which he replied simply, “Yes”. And then there was closure – a clear signal on what the data was telling us, and what it actually means for people. The data and subtle points of argument were distilled into a simple question with a decisive answer. Again, there are clear parallels with how we (in the research and analytics business) should approach our own communication of research findings.

Keep flossing…

So, taken together with last week’s advice from the Dalai Lama, for a healthy and happy life, it seems that we should be compassionate to others, only eat a little red meat, and take a daily low dose aspirin (if we are over 40 and in a high risk group for heart disease or cancer). Oh, and something that hasn’t been so widely reported – floss every day. It’ll add years to your life. Really.

blog

The Most Important Piece of Research a Company Will Commission

How DEFRA Is Using Market Tier Definition to Save Our Bees

Segmentation: A programme, not a project

How long is that piece of string, exactly?

Ch-Ch-Ch-Ch-Changes to segmentation

Our Top Blog Posts of 2012

It’s Like Deja Vu All Over Again

This Blog Post is Not For Men: The Dangers of Targeting a Broad Demographic

A Bad Apple

The Dalai Lama Does Key Drivers Analysis

The Most Important Piece of Research a Company Will Commission

5 things clients should be asking their agencies about data quality

500 Up: 7 Years of Segmentation at Bonamy Finch

What Does It All Mean?

The tyranny of choice

Do you know who is analysing your data?

A short note on the practical significance of being tall

Money talks: Rewarding better survey design

Recommended Reading For Easter

Red Meat, 'Wonder Drugs' and Flossing