“I simply don’t trust technology and online banking,” John, a policeman from New Jersey, told us during an ethnographic study Ipsos was conducting about online banking apps. Yet over two hours he showed us how he uses Vivint, a home-security App that allows him to watch his children in any room in his home through his phone. Like many of us, John’s relationship with technology is complicated. If we had simply asked him about technology and online banking, we might have got some simple and straightforward answers. But they might have misled us.
In market research there is an assumption that if we ask the right questions, we’ll get meaningful answers, and this will be sufficient to answer our business questions. Yet these kinds of big data can create a swarm of numbers. And making broad assumptions from stated behavior can lead us to overlook a very important layer of data we use every day: Visual data.
By that we mean the things we watch people doing, the environments they live in, the interactions between people and groups. To glean insights out of the data beyond just modeling and analysis, observing humans in a systematic way can help you find the meaning. That’s why tools like ethnography are helpful to make deeper sense of the data so marketers can make better brand decisions.
As with John above, if we’d simply asked him about online banking and learned of his distrust, we might have incorrectly assumed that he’s not open to using technology, he doesn’t trust big corporations and he sees security with apps as a big barrier. But it was deeper than this. John didn’t grow up with money and was worried about providing for his family.
His distrust was not about technology per se. He did not perceive any threat from the home security app. He was worried that he would do something stupid that would put his family at risk (“I could just push a button and our life savings are gone”). He wanted this responsibility to lie with a professional, at the bank.
What we gain when we listen and watch
These kinds of insights about the “why” of a statement are hard to glean from survey data alone. The why question, however, can be the most valuable. The problem is that collecting and analyzing visual data is extremely time-consuming and difficult. It’s also a specialized skill. It is certainly easier not to capture ethnographic data – but in failing to do so we miss the intricacies of human psychology and often the ‘white space’ that marketers are so keen to mine.
Asking consumers what they want from an online banking app (what our banking client originally wanted) is far easier than understanding underlying drivers of trust and a consumer’s historical relationships with money alongside their value and aspirations for future security (what we accomplished via ethnography, a la John above).
Visual data is useful not only to understand behavior (what people actually do). It is also useful to understand the impact of culture, environment, space, people and relationships on any given behavior. The influence of these factors is often hard for consumers to articulate because they are taken for granted, they are social norms, they are non-conscious.
In recent work for a pet care company, our ethnographers observed a range of culturally specific ways families cared for their pets. In Russia, one cat ate at the table with a bib, beside the baby. In Brazil, mum cooked an entirely separate meal for the dog. To say they loved their pets and that they were a member of the family took on all new meaning when we observed the lengths people went to for their pets.
Indeed, the greatest insight was that people loved their pets far too much to give them pet food. So the client positioned pet food pouches in the fruit, vegetable and meat sections of the supermarket to denote freshness and associations with scratch cooking.
Another example of where observation led to insight that changed strategy is for work with an NGO in South Africa trying to get young men in deprived areas to test and treat for HIV. One factor that nobody mentioned but that was noticed after observing many hours of video data was the lack of male spaces in the community.
Indeed, the spaces which were predominantly male (the pub) were what lead to risky behaviors (unprotected sex). The clinics where men could go to get tested were full of women and children and felt like spaces for taking care of the family. As of result, the client created mobile testing vans that went to pubs, football games and workplaces (mines and factories) which were predominately male spaces.
Relying on what consumers simply tell us gives us an important part of the picture. Adding visual data lets us further understand human behavior, which is a complex interplay of culture, environment, space, people and relationships. Looking forward, the need to watch increases so that we can create better strategy and brands.