Data is outdated

ByAlexis Jourrou

12 March 2019

Condé Nast International's Social Media Strategist, Alexis Jourrou, explains why data needs the human factor

With all this data, how can we ever really make sense of it? Social platforms such as Facebook and Instagram paint a picture of the persona you choose to create, whereas Google and Amazon know your searches and browsing behavior. They have enough data to know pretty much who you are and how you generally behave. We can learn a huge amount from data, from helping to improve healthcare, and learning more about societies, to how to optimise advertising campaigns to increase sales results. Statistical genius Andrew Pole detected different data points that could understand when a woman was pregnant. It was so effective that US retailer, Target, sent a lady vouchers for maternity products before she even knew she was carrying a child.

Data is information converted into binary digital form, a collection of facts or numbers, collected to be examined, considered and used to help with decision-making. Anything can be data, the brands you wear and the items in your bag, can all reveal clues about who you are. The data is a footprint etched in digital history to be observed and interpreted. However, the analysis of these imprints is often one step behind, or the data trail is blurred when trying to track down the ‘Ideal Consumer'. It’s too easy to rely only on data or misinterpret it, either by error, or deliberately due to a preconceived notion of what you’re looking to find. Think about how tobacco companies found ways to read the research and spin the smoking story in a more seductive light.

Despite the biggest brands, data agencies and tech companies knowing almost everything about you, it takes objectivity to interpret all this data effectively. Algorithms often churn out insights like, “80% of your audience enjoy watching films” or “75% of people are in market for a new car” but if a brand goes after this exact audience it doesn’t necessarily see its profits soaring. Data that churns out archetypal profiles is more often misread and misused. For example, ‘Lucy’, who loves adventure and is a vegan, or ‘Jim’, who goes to the gym and likes drinking with friends, aren’t necessarily nuanced representations of people. Lucy’s last “adventure” was actually to the Cotswolds and she just chomped down 20 chicken nuggets with her best friend at 3.00am. As for Jim, although he watches workout videos and has had Fitness First membership for the last three years, he’s only been once this month and all his friends are married. Jim is now a lonesome, chubby, video-gamer, but the data tells us otherwise. The data is outdated.

The records remain the same though, regardless of Jim's lifestyle changes. Although, there is real-time data on how we interact online and we have recognisable habits, people are multifaceted and fickle. We often change our minds and lifestyles, we change our favourite brand depending on the best deal available. We are constant contradictions, continuously in flux and no amount of data or surveys can determine exactly how we really feel in any given moment.

Marketing agencies invest millions researching and developing “types” of people to target – “The Fashionistas”, “The Music Lover”, “The Traveler”. It may look good in presentations, but it doesn’t always work well. If it did, then the biggest brands would always succeed, and challenger brands couldn’t compete against the companies with access to big data. Brands with an intimate knowledge of their fans can be more agile in the way they embrace social media and can call upon a deep knowledge of their products and the people that believe in them. Rather than relying only on a massive spreadsheets of data metrics, it can also be worthwhile to also read the comments and see what people are really saying. The human factor is crucial to making good decisions, using data as a guide combined with creativity and objectivity.

Companies like Coca Cola have huge amounts of data, from in-store purchase behavior, to actions on social media and massive global marketing campaigns, but there is no magic formula that tells the company exactly who to target or what to do. We only have to look at failures like Coke Life – the ‘healthy’ Coke marketed in the UK. Mountains of data research, focus groups, and taste tests may have suggested its ‘millennial’ consumers wanted a healthier Coke, that the can colour should be green and that the company should use organic sativa sweetener. Despite millions pumped into marketing efforts, it fizzled away. That’s not to say companies shouldn’t try new things, rather that even behemoths of data don’t know exactly what will work.

Pepsi also fell flat with their Kendall Jenner campaign which cost over £40 million. The data may have signified that Kendall Jenner is ‘Liked’ by young people, and that they are talking about multi-culturalism, police brutality, and that #BlackLivesMatter was trending. Studies also demonstrate that effective ads also have an ‘emotional’ factor. However, combining these data sets doesn’t necessarily spell success. The ad was so heavily criticised that Pepsi was forced to pulled it off air. Although, some may think that even bad press is good press, there is no denying that this Pepsi campaign left a bitter taste in the minds of a misunderstood generation.

Data is more crucial than ever in terms of the way we make important decisions, from our global economies to the development of advanced technology. However, it is crucial to be objective when interpreting data and to question how reliable the data source is. How recent is it? Does the data tell you the whole story about the audience, or only show a piece of the person? When examining data, it can help to also exercise empathy, and common sense; it might have saved Pepsi millions. Although algorithms are making remarkable headway in interpreting data and learning automatically, humans are still a key component in unlocking creativity and understanding data for the foreseeable future. Whether man, woman, or machine, why depend entirely on data? Once the information has been collected, it only becomes valuable data through analysis and cross-examination to reveal discoveries that the data imprints alone may miss or misrepresent.

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