The link between data and behavioural science

3 May

When Lou Montulli invented the cookie in 1994, he changed the way marketing would operate forever. It’s perhaps not a surprise that he has some regrets. In today’s world, tracking has become the norm and the clamour surrounding digital data has created a headwind that has been all-consuming.

However, we are now almost three decades down the line, and we have almost reached a point where almost all the value that can be extracted from online data can and has been harvested. Of course, it is unquestionably that this revolution has transformed the success of online businesses, changed career paths and made a handful of billionaires in the process.

But, it’s also fair to say that marketing has moved away from a pursuit of understanding people to a reliance on data as a crutch. Marketers often now replace the art of exciting people with ‘this gets more clicks’ so I’ll do more of it. So, this leads us to a new age where digital data is supplemented with behavioural science to put an understanding of the market back into marketing.

Data doesn’t provide complex answers

To make this point more clearly, a typical content marketer or SEO writer is likely to use multiple marketing tools to assist them. They may, for example, investigate keyword difficulty, popular search terms and competitive articles before writing a piece of content such as this article you’re reading now. Once written, they’re then likely to studiously analyse the website traffic to evaluate its performance.

This approach is necessary given the online competition for views and the sheer amount of content being created every day. However, it can easily lead to an over-reliance on limited and inconsequential data if it’s not combined with an understanding of human behaviour and behavioural heuristics.

For example, if you wanted to assess the success of this article, you may just analyse its views and whether visitors were engaged enough to look at other content pieces. However, behavioural science can set the scene for interpreting the data. And in doing so, it’s natural to realise that this article is unlikely to get thousands of views, simply because of the topic's specialism.

Data as a crutch for understanding marketing

The growth in data analysis, particularly in marketing given its inherent unpredictability, has led to a natural erosion of the understanding of marketing at a strategic level. The best way to consider this is by thinking about the common approach to launching a startup. Given the millions of startups that propel web-design companies, and the vocal nature of entrepreneurs, it has often become the expectation for how marketing works.

The process usually begins with an idea in the form of an app, SaaS, or ecommerce product simply due to the reduced barriers to entry. They’ll then get to work on an MVP, while considering the name, website, logo and finally growth strategy. When they’ve completed these tasks, they’ll then launch and iterate thereafter.

This approach, however, misses a fundamental of marketing. There is typically little or no assessment of the target audience, and further still, no idea of whether they’ll actually want it. So frequently, a startup’s marketing strategy and capabilities will be determined by the founder’s subjective likes and dislikes, but not by the target market’s. They can coincide, but it’s certainly not guaranteed. As a result, a brand, or website may look great and work perfectly to the team behind it, but not the target market.

The consequence is that it makes the startup’s data incredibly difficult to interpret. For example, the data shows what is happening but not why. So, it’s difficult to determine whether a site’s bounce rate is high because visitors don’t think it looks good, they don’t know enough about it, or it isn’t clear to them why they should need it. All of these factors can be easily interpreted by using behavioural science to analyse the target market.

Data creates a lack of authenticity

Given the overwhelming benefits that data can provide, it’s only natural that its over reliance is likely to have some downsides. The most notable example is a lack of connection with the target market. For example, the more people are determined as clicks or likes, the less they are valued as rounded individuals, with their actions representing just a fleeting glimpse into their overall personality.

Behavioural science fills this gap by freeing marketers to think more authentically about who they are targeting and why. In doing so, they can be inspired to create engaging campaigns that have a more meaningful impact on the target audience.

Human behaviour can’t be perfectly automated

Data science naturally aims to create predictability, and taken to extension, to create a way to automate marketing activities. However, we know that humans are innately irrational, which makes this process a worthy and highly profitable endeavour to a point, but an ultimately flawed one.

Instead, behavioural science’s aim is to naturally understand the unpredictability and acknowledge that on any given day, we all plan to do things we don’t end up doing, and equally do things we never planned. Merely acknowledging this frees marketers to conceive campaigns that will never be 100% effective, or can’t easily be measured using data, or have a major impact on a smaller number of people.

We hope you’re inspired

If you’re interested in learning more, then you may be interested in our guide to behavioural science. For anything further, please feel free to arrange a call.