How science can help marketers become more effective

From packaging design to global campaign strategy, scientific insight can go a long way in helping brand marketers to become more effective, Decode Marketing's Phil Barden writes.

How many times do we hear the "E" word in our everyday work lives? Perhaps an easier question is when don’t we hear it. Effectiveness is crucial for marketing – and rightly so, because brands provide the revenue that is the lifeblood of any business. So how might we leverage learnings from science to help us be more effective? 

Science? "What’s that got to do with marketing?", I hear you cry. Up until 2008, I would have agreed with you. Then, as brand vice-president for T-Mobile in Europe, I experienced what science can bring to the party; a step-change in effectiveness so powerful that it made me change careers. 

Science was behind T-Mobile’s relaunch, which first manifested itself in the UK with the flashmob "Dance" ad at Liverpool Street station. That increased sales by 49%, share by 6%, tripled brand consideration and, to date, has had more than 40 million YouTube views. Science was subsequently applied to other touchpoints and the UK brand vice-president was happy to attest to this approach being effective in halving customer churn. 

If we use the Oxford English Dictionary definition of effectiveness being "the degree to which something is successful in producing a desired result", then I hope that you’ll agree that what science can bring to marketing might now sound a tad more interesting. 

So how should we think of effectiveness? There are two aspects: first, marketplace effectiveness, which means creating some sort of "value" so that customers are willing to choose your brand rather than another at the same price. It’s not simply about selling more, because that could be achieved by cutting price. The second aspect is internal (within the organisation) effectiveness; if you’re using the wrong model of how brands work, then you won’t be as effective as you could be. 

Let’s look at how science can help with both of these aspects.

Out of sight, out of mind

Customers do not read our strategy papers, so we have only our marketing activities to trigger the desired impact. It might sound obvious, but if something isn’t perceived by our senses, it doesn’t enter the brain and, hence, has zero chance of being effective. 

Perception is a key point here. Nobel laureate Professor Daniel Kahneman’s real insight wasn’t the dual System 1 and System 2 theory (System 1 being fast, instinctive and emotional, and System 2 referring to the slower, more deliberate, logical part of the brain), but that our perception is bounded by stimuli. This is what is meant when he talks about WYSIATI (what you see is all there is), so the first step in mobilising System 1 is that our marketing activities/touchpoints have to be perceived. 

Taking this in the visual sense, the saying "out of sight is out of mind" is literally true for the brain. This is acknowledged by our objective to cut through the clutter; we want attention to be paid to our ad among thousands of other messages, or we want our pack to stand out on shelf. 

So if gaining attention and being correctly perceived are key to cutting through, what does science contribute to how this works and how we can harness the learnings from science to increase our effectiveness? 

Science tells us that the vast majority of our 120° of vision is peripheral, and that this is blurred and loses colour saturation; only a tiny fraction of our vision is in focus and in full colour. So there is a huge difference between a customer walking around a store, surfing online, reading printed matter or passing outdoor advertising, and a marketer or agency executive spending hours focused on a piece of creative. Our System 1 is constantly scanning our environment for threats and rewards, and this will then direct our focused attention, but the majority of perception happens under blurred conditions of vision. 

So what are the implications for marketing? How can we increase branded cut-through under less-than-ideal conditions? A simple, yet powerful, way to check what’s perceptible in peripheral vision is to blur images (you can do this in PowerPoint), show them quickly to someone who is not familiar with the image, and ask them what it is. Figure 1 (below) shows an example from a Tropicana relaunch where the new design, shown on the right, fails this simple test as it’s no longer perceptible as Tropicana. 

Figure 1. The new design (right) is not effectively perceived in peripheral vision

Visual impact

Another insight from science is that faces are powerful magnets of attention and we will attend to faces in preference to objects and text. We used this to help BT understand why a direct mailing was not achieving its desired response rate and how a simple change could significantly increase results. 

Figure 2 (below) shows the original (left) and revised (right) versions, together with a visual-impact prediction. The attentional "hot spot" in the original is the face at the bottom of the page. While faces can be used to direct attention, in this case it was a problem, as the face was drawing attention away from the call-to-action roundels above it. This created dis-fluency (difficulty to process), because reading from the bottom upwards goes against our natural reading pattern. 

So our recommendation for a short-term solution was simply to remove the face. The call-to-action roundels then became the hot spots (because science tells us that contrast is a way to grab attention, and the roundels stand out due to colour and shape). As a result, response rates went up by 31.8%. 

Science knows much more about the principles of attention and perception, and this can really help us not only to achieve cut-through, but to ensure that attention is paid to key elements of communication. This helps us in the critical first step toward effectiveness, but it doesn’t mean that customers will choose our brand. What’s perceived now needs to be understood and assigned to the correct brand.

Removal of the face image increased call to action hits by 31.8% - HOVER TO VIEW HEATMAPS

When decoding incoming data the brain metaphorically asks two questions: what is it, and what does it represent? It answers these questions via System 1 accessing our vast  associative network of memory structures, which has formed over many years. Figure 1 is again useful here, to show how the new design was ineffective; those packaging "codes" (the orange, logo) that had been learned, and which activated the brand, were absent from the new design and, with that, branding disappeared. The new design looked like any other orange juice and was not decoded as Tropicana. 

I was frequently faced with writing a design or comms brief and wondering which codes or assets I should keep and which could be changed or removed. Something we hear often from our clients is: "I don’t want to throw the baby out with the bath water, but I don’t know which is which." Fortunately, we can now bring more precision to our design and comms briefs by identifying those codes that automatically activate our brand, as well as their implicit meaning. 

An example can be seen in Figure 3 (below). This shows a result from our Iconic Asset Tracker study in the ice-cream category. The brain uses shape, among other things, to decode data. This shape has high branding power for Magnum: it activates the brand automatically, with high uniqueness and certainty. The print ad below is for a Häagen-Dazs product. With an average dwell time of 2.1 seconds on print ads, and with the shape attracting attention due to contrast, this ad has a high likelihood of being ineffective for Häagen-Dazs as it will activate thoughts of Magnum

Figure 3: This print ad made most people think of Magnum, rather than Häagen-Dazs

Value versus pain

Assuming that our activity has been perceived and decoded correctly, that still doesn’t mean people will choose our brand. How can science help us in understanding the way that the brain makes a purchase decision, and what we can do to ensure that ours is the brand chosen? 

A seminal study by Knutson et al (2007), using brain imaging, showed that pictures of a product or brand increase the activation of the so-called reward system which is known to be triggered when we value something. It’s as if the brain says: "I want this." This wanting is based upon the value that we expect the product to deliver. In our associative memory we have experiences with the brand – from using it, processing its advertising or seeing other people use it. 

Based on this associative learning, we have an expected value delivered by the brand. If this value is high, then the reward system shows a high level of activation. If it is low, then the level of activation will also be low. 

When the price was exposed to the respondents, an entirely different area of the brain was activated – the insula. This area is normally activated when we experience pain. Prices imply giving away something we already own and that is of significant value to us: money. This is coded as a painful experience in the brain. 

If it isn’t perceived by our senses, it has zero chance of being effective

The scientists uncovered the underlying, and simple, principle that determines whether a brand or product will be bought or not: if the relation between reward and pain exceeds a certain value, people are willing to purchase this item for this price. 

Further studies support this key finding. For example, a study by Kühn et al (2016) showed a high correlation between activity in the reward system and sales; the higher the reward activation, the higher the sales.

A major learning from this study, as well as others, is that reward activation, or "wanting", significantly outperforms explicit, subjective judgements of "liking" in terms of predicting sales. The reason for this is that the two are not causally connected in the brain, so it’s possible to like something, but not buy it. 

The implication for marketing is that we can increase effectiveness by focusing on, and measuring, wanting – but how? How can science help us to understand wanting in a way that we can start to apply it to brand-building? 

Wanting is based on the value that we expect the product/brand to deliver. Put differently, this "value" is the outcome that we desire in a given context. The brain ascribes high value to a brand/product if it’s perceived to be an effective means of accomplishing the desired outcome. It’s useful to think of outcomes as "jobs to be done" (JTBD). These can be functional and neuro-psychological, and we can describe them in terms of what a customer wants to have, do, be or become. 

Let’s look at an example in order to bring this to life. Figure 4 (below) shows some product choices.

Figure 4. Choice is context-dependent

If we wanted to predict sales of these products, typical research questions such as "Which do you like best?" or "How likely is it that you will purchase X, Y, Z next week?" are unhelpful because the answer depends on, and will change with, the context for the decision. 

Imagine that you‘re on a diet, for example. Which products would you choose? Or you want to satisfy your child’s hunger while at the same time getting a hug from your kid. But what if you want to satisfy your child’s hunger while at the same time being recognised as a good parent by others? 

And what about the pain side of the equation? Which would you choose to eat if you were driving? The orange is too effortful, and this equates to pain; therefore the value of the orange is low. 

So context and the desired outcome change the value of exactly the same product. Putting context and the outcome we desire into the equation is therefore critical and, when we do this, "wanting", and hence purchase decisions, become very clear. 

Value varies

So, value (reward activation) is high if our choice (product/brand) is perceived as an effective means to accomplish the desired outcome (in a given context). Perceived value is high if our products/brands "fit" what customers want to do, have, be or become. The fit to a JTBD determines the value and relevance of brands.

We’re all familiar with functional JTBD; these are category-specific benefits or goals that people want to achieve. We want to have clean clothes, satisfy our hunger, rely on our broadband and, while delivering against these is fundamental because brands need to be relevant within a category, we often find that brands perform similarly to each other at this level, particularly in mature categories. So, if we want to be truly effective, we need to find other ways to be distinctive. 

What about the neuro-psychological level? Science can help us here, too. At this level, goals/JTBD are those representing higher-level purpose, universal human needs, personal values, self-identity, emotional and social outcomes that are linked with using the category. 

To find out which goals drive purchase in a category, how brands match with these to create relevance and distinctiveness, as well as how touchpoints trigger goal associations, we use a model derived from neuro-psychology and test using implicit methods. 

Figure 5 (below) shows how this deepens our understanding of why the Tropicana redesign failed. All customers have to work with are the codes we send, so a core benefit of the JTBD  approach is it links directly to codes. Codes  are how we signal the match between our pro-duct/ brand and the JTBD. The design codes used trigger very different goals; hence the new design has a poor match with the JTBD of the original. The orange and straw trigger the concepts of "everyday" and "natural"; the fluted glass conveys "special occasion" and "processed".

Figure 5. Decode’s Goal Model shows why the new Tropicana design was ineffective

Context matters

The JTBD approach is validated not only in science, but in the market. The successful T-Mobile relaunch cited at the start of this article was based on JTBD. More recently, for a big FMCG client, we studied more than 60 brands in 20 categories among 100,000 customers and found a correlation of .86 between brand equity (defined as relevance and distinctiveness versus JTBD in a category) and "willingness to pay" (based on actual market shares). 

So how should we think differently if we want to use this approach to increase effectiveness?

First, stable segmentations such as socio-demographics are not particularly useful, because humans are very flexible in determining value based on context and the outcome we desire. When these change, so do our behaviours and decisions (remember the apple, orange and doughnut example), so the same person can inhabit multiple segments.

Second, we need to know how to communicate "value" so that customers can decode the fit with their JTBD (think Tropicana and see further commentary below).

Third, we need to recognise that many metrics we use, rather than being drivers of purchase, are actually outcomes of goal/JTBD achievement. We may ask about "brand love", but the causes of this can be very different. 

We tested four different skincare brands, all of which had similar brand-love scores among their users, and found significantly different matches with JTBD. All the users loved their respective brands, but for different reasons. 

The same holds true for attitudes, trust and satisfaction. As marketers, we need to work with the causes, not the outcomes. Not only is this crucial for effectiveness, but science tells us that JTBD also drives other mental processes such as attention, mental availability and brand-recognition. 

Another common issue that marketers face is how to communicate something new while retaining consistency in campaigns or designs. The solution to this dilemma can be found in science as well. We need to distinguish between two types of consistency: perceptual (if it looks the same as before, the brain treats it as the same) and conceptual (it looks different but, at a conceptual meaning level, it is the same as before). 

Lynx was a good example of managing this effectively; variants were introduced and creative executions refreshed, but the concept remained the same. In each ad, an average guy gets the most attractive girl(s), but this underlying story is executed in many different ways and, importantly, the brand is integral to the story, thus strengthening associations with the JTBD.

This novelty versus familiarity dilemma is, in my view, the root cause of Tropicana’s problem. The design brief called for the new design to be more modern, cleaner and fresher than the existing one, and there is no doubt that it met these objectives when viewed on the designer’s Mac, the brand manager’s desk or in a focus group. The issue is that those are irrelevant objectives when it comes to what drives purchase. Aside from the perceptual and brand-assignment issues cited earlier, the new design simply didn’t signal a match to the JTBD. Therefore its perceived value was low, and the brand lost $27m in sales in six weeks.

Insight you can use

Science can certainly help us to increase our in-market effectiveness. In the short term, we can adopt the principles by which the brain processes information (perception, attention) to make our activities more effective. Try blurring images of any visual touchpoint, such as POS, displays or web pages, to check for per-ception, for example. In the longer term, we can increase our effectiveness by using the same model for how the brain deals with brands (goal/JTBD). Indeed, using the wrong model for how brands "work" mitigates against effectiveness. 

The clear insights from science are that when people buy brands:

  • Context matters
  • The JTBD in the (sub-)categories is the consumer reference point
  • A brand intuitively signals high fit to consumers’ current JTBD
  • It offers superior value as it is the superior means to accomplish the current JTBD

Marketers, therefore, need to know the JTBD at the category and brand level, as well as knowing the brand codes with the best fit to JTBD at the functional and neuro-psychological levels.    

Phil Barden is managing director, UK, at Decode. He has more than 25 years brand-management experience. His first book, Decoded. The Science Behind Why We Buy, was published in 2013. He regularly speaks at industry events, most recently The Indie Summit and FT Marketing Innovators Summit.


Further reading

Barden, P (2013). Decoded. The Science Behind Why We Buy. (Wiley)
Kahneman, D (2002). Maps of Bounded Rationality. http://bit.ly/NobelKahneman. 
Knutson, B; Rick, S; Wimmer, E; Prelec, D; Loewenstein, G (2007). Neural Predictors of Purchases. Neuron, 53, 147-156.
Kühn, S; Strelow, E; Gallinat, J (2016) Multiple "buy buttons" in the brain: Forecasting chocolate sales at point-of-sale based on functional brain activation using fMRI. NeuroImage, 2016 Aug 1; 136:122-8.