The Manifesto for Marketing challenges marketers to mobilise the whole organisation around the customer. One obstacle to this has been a lack of common approach to customer understanding across business functions. In particular, we often see an apparent opposition in marketing departments between a ‘data science’ view of the customer and a view rooted in psychological insights.
In fact, we believe that a combination of psychology and data science is the only way for marketing leaders to align around customers and to unlock value from insights that are unlikely to be found purely through data mining.
24,000 consumers, 790 brands, 13 markets and 1 psychological model
At opento, we routinely use a number of psychological models to guide data analysis for customer segmentation, brand positioning and other marketing projects. With the help of our partners at TGI Insights, we’ve been applying our psychological models to their extensive data on customers, markets and brands for many years. Recently, TGI’s data scientists have tested hypotheses based on our models against their data. (Importantly, this is done with a high level of craft and iteration and not by taking a ‘one size fits all’ data analytics approach.) In this post, we’ll look at just one model, based on archetypal psychology, and show how linking it to customer data can inspire and mobilise ideas across a wide range of marketing challenges.
Myths, meaning and machine learning
There are many myths about brand archetypes including the common belief that archetypes are at the ‘soft and fluffy’ end of marketing. Connecting the model to data helps dispel that myth. In this post we’ll concentrate on the customer dimension of our ‘Marquetypes’ model (though the model also includes brand vision and competitive market).
TGI’s data scientists applied smart machine learning techniques to our model. This helped us to develop an understanding of customer motivation and meaning triggers then link them to brand and category use. In essence, we produce detailed Customer Personas for each category and develop new kinds of customer, brand and market insights from those personas.
New insights on customers, brands and markets
1. Category leaders generally operate in a ‘reflective’ way:
How do you build category leadership? In the fragrance category as we showed in our previous post, people with ‘lover motivations’ are highly involved in the category so it makes sense that the category as a whole is centred on intimacy and sensuality and that leading, iconic brands reflect those motivations back to customers. From our broader analysis, this seems to be a general rule for category leaders. In other words, if you want to build a leading brand in any category, it makes sense to reflect the dominant customer motivations need to pay attention to those dominant motivations – either to reflect them or to consciously disrupt them.
2. A new way to look at category and sub-category segmentation:
'What business are you in?' is a perennial question for marketers and this approach gives us a new way to think about it. Building on the point above about dominant motivations, we would expect a category to be centred on one of the 4 main motivational ‘poles’ of the map (stability, freedom, change, belonging). The chocolate market is an interesting example. We see customer motivations in all 4 areas. Of course, that is because the chocolate market is actually composed of a number of sub-categories, such as tablet chocolate, bars etc. that each centre on different motivational poles. This means that different brands can act as brand leaders in each sub-category. If your market shows this type of pattern, it could suggest an opportunity to focus on building leadership of a sub-category.
3. Communication Triggers:
How should brands talk? What type of language triggers the different customer personas? In the digital world, what is your brand’s social media voice? The typical advice from social media experts is to be authentic, open and transparent and that makes sense. However, this still leaves open the question of what to talk about - the specific triggers that will help you attract customers online. We can get some clues by profiling the concepts that trigger positive emotions in different archetypal Customer Personas. For example, in one category of the Financial Services market, we looked at two Customer archetypes that are important in the category and profiled their trigger concepts. You won’t be surprised to find that ideas such as ‘prudence’, for Persona 1 or ‘discipline’ for Persona 2 are relevant or that the shared trigger of ‘logic’ is there but we also find some less obvious ideas such as ‘noble’. 
These conceptual triggers work across different categories – though brands should use language that is relevant to the category language. For example, although Metrobank positions itself as a revolution in banking with a tagline of “love your bank at last”, it also emphasises safety and security of its product and services.
'All models are wrong, but some are useful.' (George Box)
Of course, it’s always reassuring to get support from data for the models we use, but it’s equally important to know how useful and fertile a model can be. So far, as well as the examples above, we’ve used the Data Science + Psychology approach to gain new insights about:
Innovation of products and services
Brand Extensions
How to create new categories
Portfolio Architecture
Competitive Analysis
We’re sure that marketers have many other challenges where this approach can help.
How can marketing leaders benefit from this approach?
Although we’ve focused on the use of one psychological model (Marquetypes) in this post, the real learning is about the value of combining solid psychology with smart data science. Without the data science, it can be time-consuming and expensive to apply the psychological models; without the psychology, the data science can lead people to focus on statistical significance and miss the insights based on human understanding.
We recommend these 5 key steps to ensure success:
Use psychological models that are universal, not category-dependent. They will give you unique insights that will not emerge from your market data alone.
Before briefing any data analytics, define the underlying customer psychology.
Treat analysis as a test of hypotheses that come from understanding the psychology.
Work iteratively with psychologists and data scientists to develop shared understanding. Good analysis is carefully crafted, not bluntly applied.
Pay particular attention to the insights from data outside your market and category – your competitors are less likely to know about them!
Sandra Pickering is founding partner of www.opento.com a brand consultancy specialising in practical applications of neuromarketing and consumer science. Read more from her and opento in the Gym.
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By Sandra Pickering
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Customer
Tagging:
2014
big data
Sandra Pickering
Opento
Marquetypes
WPP
Kantar
TGI
financial services
online personas
semiotics
data science
neuroscience psychology
Science
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Big Data: from mining to meaning by Sandra Pickering, Opento | Marketing Society bloggers 2014
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Big Data: mining to meaning
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