Premium brands used to only have to battle against each other. Now they have to compete with rivals that are cheaper and readily available.
For years supermarket store brands, so called private-label brands, were seen as low quality products that were not really comparable to real premium brands. There are still some private labels that are clearly inferior to the brands, but the gap is closing.
To my mind, retailers’ growing focus on and investment in their own products has come about for several reasons:
First, these private labels are profitable. They undercut the premium brands but still make a healthy margin. Second, they present an opportunity for retailers to build loyalty with customers; after all, if you love Coles’ muesli, then you have to shop at Coles to buy it. And third, with the rise of discounters like Aldi, supermarket chains have to work harder to retain shoppers who might otherwise be lured away by cheap, no-frills products elsewhere.
Retailers are also inclined to give an increasing proportion of shelf space to their own products. And retailers are not likely to dispel the misplaced view of many of the general public who think private label products are made by the same people who make the more attractively packaged and advertised products that cost more.
So, with retailer brands increasing share across categories, premium brands face a considerable challenge. Their task is to convince consumers that they’re paying for more than a nice box and the ad they saw last night.
Premium brands, in general, have strong equity and consumer trust and are seen as innovative. Companies like Nivea and Heinz have many years of heritage yet continue to innovate and invest in product development and packaging, making sure they are prominent on the shelf. They invest in advertising to create desirability and differentiation. They do not stand still. They continually seek to justify the fact that they’re more expensive.
In The Paradox of Choice, Barry Schwartz persuasively makes the case that complexity undermines consumer decision making and increases dissatisfaction. Believing that only new news sells and that people will pay a premium for a brand that fulfills their specific needs, marketers of consumer goods have extended their brands to the point where shoppers are faced with a staggering number of alternatives. And as the number of branded alternatives has increased, so has the amount of marketing communications people see and hear (though, of course, many people now have DVRs and PVRs that allow them to skip TV ads), making it less likely that anyone will remember the advertising for an individual brand.
In an increasingly cluttered world, the clarity of a brand’s proposition will become ever more important. A clear and differentiated identity will need to be established. In many cases brands that embody a particular belief or set of values perform best. They say, “This is what we believe; join us if you agree”. Research can help to identify what customers really care about and credibly match values to ensure success.
The ultimate goal is to manage premium brands in such a way that they become icon brands. Then there is no need to ask for alternatives; there are no viable rivals. For example, when I want sticky tape, I ask the assistant for Sellotape.
Similarly, at a recent dinner party, I was asked by my cousin’s 20 year-old girlfriend if I had any Gladwrap (cling film). She didn’t know any other cling film brands. Gladwrap was the best, period.
I worked at DHL some years ago, where we attempted to instill in consumers' minds a perception that DHL was an icon. In research, we found that when DHL users wanted to send a document or parcel they simply “DHL'd it”. This was the language they used. Using this insight we developed a clear and credible campaign that was very successful, and led to increased brand affinity and market share.
In summary, it’s up to premium brands to out-innovate and out-image retailer brands in order to prosper.
Sunday, January 31, 2010
Tuesday, January 12, 2010
Observations, insights and ideas
You know us marketers use the term ‘insights’ as if there is no tomorrow. And yet it amazes me how often it is misused.
To be clear, in market research, we begin with observations of behaviour. These are facts, nuggets of consumer information that can be the raw material for insight generation. For example, US researchers in the 1970s saw kids walking around with big stereos on their shoulders.
An insight is when we get to the heart of people’s thoughts and feelings. It provides inspiration for business growth. Taking the aforementioned example, the insight was that these kids wanted to listen to their music when on the move.
So Sony developed the Walkman.
Interestingly though, when it was researched, consumers said they wouldn’t buy it because they couldn’t use it to record music like they could with a traditional tape recorder. But Sony believed in the core insight, pushed on with producing and marketing the product, and the rest, as they say, is history.
So from an insight comes an idea, in the above example the idea being to produce a small, portable, lightweight machine that can play music (tapes). Good ideas come from great insights which in turn come from deeply understood observations. And good ideas potentially go beyond what consumers say they need. It can require a leap of faith, as in the case of the Walkman.
To be clear, in market research, we begin with observations of behaviour. These are facts, nuggets of consumer information that can be the raw material for insight generation. For example, US researchers in the 1970s saw kids walking around with big stereos on their shoulders.
An insight is when we get to the heart of people’s thoughts and feelings. It provides inspiration for business growth. Taking the aforementioned example, the insight was that these kids wanted to listen to their music when on the move.
So Sony developed the Walkman.
Interestingly though, when it was researched, consumers said they wouldn’t buy it because they couldn’t use it to record music like they could with a traditional tape recorder. But Sony believed in the core insight, pushed on with producing and marketing the product, and the rest, as they say, is history.
So from an insight comes an idea, in the above example the idea being to produce a small, portable, lightweight machine that can play music (tapes). Good ideas come from great insights which in turn come from deeply understood observations. And good ideas potentially go beyond what consumers say they need. It can require a leap of faith, as in the case of the Walkman.
Tuesday, January 5, 2010
Analysing market research data
At a recent Xmas party I attended, a friend of mine, who considered himself quite numerate, asked me how I went about analysing market research data. Did I use the same techniques and steps for each piece of work? And if not, how did it differ?
Now, I would not describe myself as a statistician, but over the years I have gained an awareness of the methods and techniques that may be employed to tease out insights from data. And so, after a few moments to think, I tried to give an answer in simple, clear terms. It went something like this…
There are, to my mind, essentially two basic groups of techniques used to analyse research data:
On the one hand there are structural techniques. These identify the relationship among variables, for instance, when a researcher wants to know which product variables are related to one another or how consumers group into homogeneous clusters. Factor analysis, cluster analysis, etc., belong to this class of techniques.
And then there are functional techniques, that concentrate on how a set of variables influence a variable we are interested in, for example, identifying purchase drivers, and what attributes distinguish users and non-users of a certain brand.
I went on to say that historically the general approach to data analysis is sequential: first obtain the cross-tabs, then use structural and functional techniques to sharpen our understanding of the data. This approach has been, and will continue to be, useful in analysing data.
However, at AMR Interactive (a market research agency I worked for some years ago), I came across a new approach that combined the 2 classes of techniques into one single analysis procedure. In doing so, it provides considerable insight into how brands and attributes are related to one another and which attributes (or demographics) are crucial in distinguishing brands.
In addition, this Correspondence Analysis also provides Perceptual Maps that can be used to strengthen a current brand position or find opportunities for a new or existing brand. It puts important patterns in the data into bold relief by visually depicting the prominent relationships. Richer interpretation of data and more relevant cross-tabs may be generated once we identify the important patterns.
Now, I would not describe myself as a statistician, but over the years I have gained an awareness of the methods and techniques that may be employed to tease out insights from data. And so, after a few moments to think, I tried to give an answer in simple, clear terms. It went something like this…
There are, to my mind, essentially two basic groups of techniques used to analyse research data:
On the one hand there are structural techniques. These identify the relationship among variables, for instance, when a researcher wants to know which product variables are related to one another or how consumers group into homogeneous clusters. Factor analysis, cluster analysis, etc., belong to this class of techniques.
And then there are functional techniques, that concentrate on how a set of variables influence a variable we are interested in, for example, identifying purchase drivers, and what attributes distinguish users and non-users of a certain brand.
I went on to say that historically the general approach to data analysis is sequential: first obtain the cross-tabs, then use structural and functional techniques to sharpen our understanding of the data. This approach has been, and will continue to be, useful in analysing data.
However, at AMR Interactive (a market research agency I worked for some years ago), I came across a new approach that combined the 2 classes of techniques into one single analysis procedure. In doing so, it provides considerable insight into how brands and attributes are related to one another and which attributes (or demographics) are crucial in distinguishing brands.
In addition, this Correspondence Analysis also provides Perceptual Maps that can be used to strengthen a current brand position or find opportunities for a new or existing brand. It puts important patterns in the data into bold relief by visually depicting the prominent relationships. Richer interpretation of data and more relevant cross-tabs may be generated once we identify the important patterns.
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