“All I know is that I know nothing,” Socrates so wisely maintained.
Since taking off with the baby boom in the 50s and 60s, marketing has slowly but surely evolved into a science of anticipating consumer reactions to various stimuli, a science centred on hypotheses and increasingly sophisticated validation techniques. “If I put a sugar-free strawberry-vanilla flavoured yogurt on the market, Yuppies (young urban professionals) and DINKS (dual income, no kids) will eat it up.”
This approach, for whatever reason, has led to a market that is saturated with products and messages. Today, it is exacerbated by the decline of traditional media and hit hardest by digital. Simply put, mass media has less and less space for a growing number of messages.
In this context, then, it’s no coincidence that two disciplines with seemingly nothing to do with each other are currently on a roll breathing new life into product development: ethnography and big data.
The first, taking the time to move past the facts and really understand peoples’ stories from an emotional angle, brings all sorts of unexpected ideas and possibilities to life. To understand the process, just imagine an anthropologist who, without any preconceived notions, discovers an unknown tribe. It will take time and a careful methodology to decipher the symbols, customs and objects that the tribe members use, but once this culture has been decoded, their real wants and needs will come to light. Thinking of target markets like “cultures” (young mothers, frequent travellers, hair dye users, etc.) and interpreting them using a developed ethnographical approach is an excellent technique to refresh our way of thinking.
As for big data, it is a sort of modern avatar for data mining that consists, without going into too much detail, of diving into the deep end with immeasurable quantities of data while searching for unanticipated correlations that, in turn, become practical insights. At the Boomerang conference, for example, big data expert Hilary Mason described a correlation between where 300 New York ambulance drivers wait for calls and the location of 24-hour coffee shops (indeed, paramedics like to sip a cup of java while they wait). It was then possible to modify the ambulance drivers’ habits by inventing a better division of waiting areas, not surprisingly, in close proximity to coffee shops. As a result, the average waiting time for an ambulance in New York was reduced by one minute.
Both ethnography and big data stem from the principle that we don’t know what we don’t know. This would appear to state the obvious, but it’s really only another way of expressing a Socratic point of view. These two disciplines can produce unexpected results that would otherwise remain unknown. By adopting a human-centred mindset for the research work (going deep enough in the ethnographic field, looking for human behaviour correlations in big data), we can design products and services that meet the real needs of consumers.
This article was originally published on Infopresse.
