A few days ago the Reuters Institute of the University of Oxford published a very interesting chart that emerged after an investigation in which digital leaders, among other things, said that “the best ideas” arose basically from “audience and data insights”, as compared to other sources such as multi-disciplinary teams or “other media companies”.
The first thought one has when reading this information is to understand that this type of knowledge source is relatively new, basically the product of the internet revolution that began approximately 20 years ago and brings more surprises day by day.
It was Google, and its Google Trends (thanks to the massive penetration of this search engine), which showed us that searches reflected human behavior in an incredible way. Possibly one of the first was when it became clear that the volume of Google searches for car models, matched perfectly with the subsequent purchase of those models. In other words, it was known which brands would be the best-selling in the coming three months just by looking at Google Trends.
The most influential person on the planet
From then on, many researchers, marketing and advertising professionals have seen that platforms such as Facebook or Instagram have made great progress in their advertising effectiveness thanks to the exhaustive and intelligent use of data. This same data which allowed Mark Zuckerberg to become one of the richest and most influential people on the planet.
There are many questions about the data. A test of how this network operated was shown when, on the Deep Web, some unfaithful employee of the platform uncovered more than 30 thousand categories that Facebook used to characterize its users. Those 30,000 categories are very reminiscent of a story by Borges – John Wilkins’ analytical language – which in turn was used by Michel Foucault as a preface for his anthological book “The Words and the Things”.
In this tale, Borges is talking about a Chinese encyclopedia, which lists the animals that make up the universe and that include:
“(a) those that belong to the Emperor, (b) embalmed ones, (c) those that are trained, (d) suckling pigs, (e) mermaids, (f) fabulous ones, (g) stray dogs, (h) those that are included in the present classification, (i) those that tremble as if they are mad, (j) innumerable ones, (k) those drawn with a very fine camelhair brush, (l) others, (m) those that have just broken a flower vase, (n) those that look like flies from a long way off.”
The game and the joke in Borges’s story actually makes us think about the arbitrariness of classifications, but also the inevitability of their use.
Data as inspiration
That said, the use of data as a source of inspiration to innovate leaves us with a lot of questions – what does the data mean, what are the biases and how do you interpret it?
Looking at the data and the insights which can be prized from it can give us something invaluable – the reading of trends. Much of our work today consists not only of understanding the present but also the ability to read where the market and human behavior is going. Today, thanks to technology, we have a very fast understanding of data sets which allows us to simultaneously be able to look backward and, therefore, look forward. From all this, at least two interesting opportunities emerge: the first is the construction of possible scenarios, and the second is to think about the possibility of the generation of new biases which bring more human insight into our daily work.
The challenge is to add trained analysts in different social disciplines who can correctly interpret this data. It will be necessary to understand who these analysts are, where they are and what type of training these specialists need. In time they may well form the creation not of a new discipline but of a new category of analyst.
When it comes to thinking innovatively about product insights, an article from 2008 by Thomas Friedman on the eve of the financial crisis wrote a memorable paragraph about the automakers crisis: “Over the years, Detroit bosses kept repeating: “We have to make the cars people want.” That’s why they’re in trouble. Their job is to make the cars people don’t know they want but will buy like crazy when they see them. I would have been happy with my Sony Walkman had Apple not invented the iPod. Now I can’t live without my iPod. I didn’t know I wanted it, but Apple did. Same with my Toyota hybrid.”
This NYTimes paragraph reinforces that interpreting insights well is not enough to make good products or advertisements.
Going forward, we are probably facing several interesting challenges when it comes to the digital world of marketing, research and advertising. With the Google announcement of a ‘cookieless’ world as of 2022, large advertisers are going to have to choose two unavoidable paths: use the information from the large platforms and be content with it or slowly start collecting your own data (many large advertisers are already doing this) and use this data to understand the audience. Undoubtedly, a huge opportunity opens up for researchers, planners, and advertising who will be able to own their own insights build their own future scenarios and other products linked to the correct interpretation of trends.
With the accumulation of user data and potentially new technologies that may arise, countless opportunities will become apparent. Focus on evaluating and re-evaluating the cognitive biases within the data that we currently have, could provide us with sources of knowledge never seen before. This is what is exciting about the profession in which we are all involved, sometimes it seems that everything has been measured, but then technology opens up new paths to knowledge, methodologies and research that we never imagined we would see.
Thomas Friedman: “Cars, Kabul and Banks”, New York Times, December 13, 2008.
Foucault, Michel, Les mots et le choses, Gallimard, 1990.
Jorge luis Borges: “The Analytical language of John Wilkins” from “Other Inquisitions”, Emecé 1953.
Journalism, Media, and Technology Trends and Predictions 2021, Nic Newman, Reuters Institute, Oxford University, January 2021.
