For some time this approach was enough to win clients and budgets over. Campaigns became, or at least appeared, to be more targeted; wastage was somewhat reduced and clients were happier.
Whilst this form of behavioural classification can give us an idea of what type of content our audience has been looking at recently it does little to reveal how the audience actually behaves. What makes them tick? Are they more interested in product pricing, technical information or emotional content? Are they likely to engage with ads in the first place? It all matters because it has a direct impact not only on campaign performance but also KPI benchmarking and creative messaging.
More interestingly that impact has been becoming more and more measurable. According to the Watson AI Developer Cloud resource a recent study with retail store data found that people who score high in orderliness, self-discipline, and cautiousness and low in immoderation are 40 percent more likely to respond to coupons than the random population. Therefore, from a media standpoint, we would expect a higher response rate by tailoring our messaging towards offers, cashbacks and coupons for this segment of the population. A second study found that people with specific values showed particular reading interests (Hsieh, Gary, Jilin Chen, Jalal Mahmud, and Jeffrey Nichols 2014) . For example, people with a higher self-transcendence value demonstrated an interest in reading articles about the environment, and people with a higher self-enhancement value showed an interest in reading articles about work. Similarly to the previous example, we could tailor copy to appeal to people demonstrating these traits when targeting relevant environments. A third study of more than 600 Twitter users found that a person’s personality characteristics can predict their brand preference with 65 percent accuracy, meaning that we can produce a profile of an average brand user and the target similar people based on their own words and content.
Media agencies nowadays have an abundance of tools in their arsenal allowing them a glimpse into the consumer psyche. This, however, all but eliminates the challenge of targeting or buying based in this insight.
At Total Media behavioural planning sits at the heart of what we do and its therefore vital that we are able to translate it into media activation. Some of the recent client and new business work we have completed has allowed us to demonstrate just that.
By using search data we were able to show that our in-market tech target audience was more focused on emotionally affirmative content rather than pricing information before making a purchase. Based on the results we targeted content around people using tech to achieve their aspirations and adjusted our creative messaging to match. By analysing twitter handles through Watson AI we are currently helping determine the personality characteristics of a brand’s fanbase, then planning to use Visual DNA 3rd party data to reach users that exhibit traits such as Openness, Agreeableness and Extraversion. Targeting Openness has been shown to increase response and performance and therefore we would expect uplift in results. Insight gathered in Dark Social based on conversations between users showed that consideration for an FMCG purchase usually happens between Tuesday and Wednesday allowing us to upweight our campaigns accordingly.
Whilst its not always easy or straightforward to translate behavioural insight into media buying by doing so we can reach and influence our audience in a much more profound way, change perceptions and speak to them in a much more profound way that truly resonates.