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Tuesday, 29 July 2014

What does 'meaningfully different' actually mean?

This month, Millward Brown releases an update to its 20-year-old BrandDynamics service, incorporating its new Meaningfully Different Framework for brand equity measurement. Josh Samuel reviews the work that went in to the creation of the framework, and explains why ‘power’, ‘premium’ and ‘potential’ will be crucial metrics for assessing brand success.

In the same way that brands must innovate to remain relevant, research models must evolve with the times, particularly those that are focused on brands and the consumers that buy them. Which is why we decided to overhaul BrandDynamics.

BrandDynamics has been a leading measure of brand equity for nearly 20 years, quantifying and diagnosing the loyalty consumers have to brands and segmenting their relationships from basic brand knowledge (presence) through to brand loyalty (bonding).

While BrandDynamics has certainly been updated and refined over the years, and while there remains a strong correlation between high bonding scores and sales, we wanted to extract deeper insight that related more directly to specific financial outcomes. We also wanted the model to reflect the latest understanding of the human brain and consumer decision-making.

We’ve always been able to show how brand impacts sales, but what marketers really need is to quantify the precise financial contribution their brand is making now and what it will deliver in the future and understand how to optimise that.

So we set-out to develop a new equity measurement system, starting with the crucial but often neglected task of clearly defining the ways in which brands constitute a financial asset (or equity) for the brand owner.

  1. The brand delivers greater volume thanks to consumer predisposition to choose the brand more often.
  2. The brand justifies a higher price point because consumers are willing to pay more for it.
  3. The brand provides a platform for future growth because consumers are predisposed to stick to the brand or try it in future.

Next we studied our BrandDynamics database of over 100,000 brands to identify the characteristics of the brands that deliver against those three outcomes.

Our analysis pointed to three qualities of brands, underpinned by five existing survey metrics, that delivered these financial returns:

  • Meaningful: the brand has affinity and meets needs. This indicates the extent that brands build an emotional connection and are seen to deliver against consumer needs.
  • Difference: the brand is unique and sets trends. This indicates which brands set themselves apart from the category by leading the trends or simply offering something that others don’t (can be intangible).
  • Salience: spontaneous awareness, which indicates how quickly and easily a brand comes to mind.

Validating the key drivers
Next we needed to ensure that these qualities were actual drivers, rather than simply the outcome of brand success. We also needed to achieve the following:

  1. Validate that these three qualities accounted for the full role of brand in delivering the desired financial outcomes.
  2. Optimise our survey measures of these dimensions and ensure they capture the implicit/automatic consumer decision-making processes.
  3. Quantify the contribution that each of these brand qualities makes to the financial outcome.

We embarked on a pilot study in five countries, interviewing more than 8,000 consumers about 400 brands. The study coupled neuroscience measures to survey metrics right through to actual purchase behaviour, for the same individual respondents.

The neuroscience metrics allowed us to measure the role of so-called system one processes, such as the automatic emotional response and intuitive brand associations that people have.

We then tested a variety of survey-based attitudinal measures to see which best captured these affects and all others. Finally, we were able to monitor the same respondents’ actual shopping behaviour through shopper panels and SMS diaries. Factor regression modelling of the survey metrics was used to predict the volume of brands people bought and the price they paid.

This work confirmed that we could summarise the full role of the brand in purchase decisions with the measures of meaningful, difference and salience. This modelling process also allowed us to generate and validate three headline measures of brand equity, built from different combinations of meaningful, difference and salience, to predict each of the financial outcomes identified earlier:

  1. Power: This predicts the volume share delivered by brand equity. Generally, we found that being meaningful was most important to this, with salience next and difference less crucial, but this varies by category. Our pilot work showed that brands with high power generate five times the volume share of those with lower power.
  2. Premium: This predicts the price point that the brand equity can justify charging. Again it varies by category, but usually being meaningful is the most important quality although difference is almost as important. Our pilot work showed that brands with high premium score can justify charging a 13% price premium.
  3. Potential: This predicts the likelihood of value share growth in the next 12 months. The pilot work showed that brands with high potential are four times more likely to grow value share than those with low potential.

Josh Samuel is European development director for brand equity at Millward Brown

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Readers' comments (4)

  • How refreshing to read about a quality research project, well thought out ,sound and interviewing real respondents rather than using access panels composed of people completing 5 surveys a week for 4 or 5 different access panels i.e 20 - 25+ surveys a week. The indusrtry really must stop the rot and demonstrate to in-house researchers the dangers of using access panels when they really need accurate insight.

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  • Well done to Millward Brown for doing this. Glad to see a company doing it properly ! It would be interesting to know if work is being undertaken within different countries to establish if variations exist by country and if the dynamics change as people get older/their life stage changes, particularly with the neuroscience stuff. And will MB contact me to answer these points ?????

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  • Hi Vic,
    Thanks for your thoughts. The initial pilot work was across 5 countries and 12 categories. Since then, we have been applying and testing the framework across 000s of brands globally in our brand equity databases and ongoing studies. This has turned up some interesting different dynamics across countries and categories, but we need to do much more work to confirm and fully understand these dynamics.
    One interesting variation is in the importance of difference across markets. Difference seems to be more important in developing markets (where there may be more opportunity to differentiate in a way that has real impact) and in technology categories, where again there is more opportunity to demonstrate true difference and leadership.
    Much more to be to understand and confirm these dynamics and pull out others, so watch this space, i guess!
    We haven't yet used the pilot work or new equity framework to dig into differences by life-stage, but I agree it would be interesting. We'll just have to find the time to do that work.
    Thanks,
    Josh

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  • Mike - I am pretty sure that MB would use access panels for the n=8,000 interviews they conducted

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