How has communication changed over time: from one to many to one to one
Following the discovery of the University of Oslo, which found that the infamous "Flynn
effect" or the growth coefficient of IQ, was reversing, in other words, instead of increasing, human intelligence was decreasing, the impoverishment of language was blamed as one of the main causes of this reversal. Language, it found, in order to be understandable to an increasing number of people, had to make strong compromises in terms of details and expressive richness, up to the point of being banal and vulgar.
It hasn't always been like this. Before, humans could apply a model of personalized communication, a 1-to-1 model that has the merit of being rich in content and details. It was the necessary enlargement to communities that caused communication to decay.
Data has allowed us to find our way back to the personalized relationship. Data has also
allowed something more: It has enhanced our emotional intelligence and empathy. But not of humans, of machines. Such machines are driven by humans who progressively become experts in completely new disciplines such as data-telling and video-science. These creators of human-machine relationships will increasingly become part of our daily lives.
The Language Factor
In a 1987 study published in the Psychological Bulletin, Professor Flynn compared the results of some intelligence tests carried out on a sample of children in 1972, with others from 1947. The comparison showed that in the 25 years from one test to the next, the IQ of the children tested had increased by 8 points, resulting in a variable average growth of IQ from one generation to the next of between 5 and 25 points. This phenomenon has been called the "Flynn effect."
Unfortunately, however, this euphoria was dampened in 2004, when the University of Oslo researchers realized that between 1970 and 1993 the "Flynn effect" had actually diminished. In the following years, this slowdown was further confirmed, until the tragic discovery that the trend has now reversed. Instead, from one year to the next, IQ decreases on average by 0.25-0.50. This phenomenon has been called the "reverse Flynn effect."
In October 2020, political scientist Christophe Clavé identified one of the possible causes as the impoverishment of language, namely lexical simplification due to the decrease in the vocabulary used reduces even the subtleties of language that allow the development and formulation of complex thought. Without words to construct reasoning, this hinders complex thinking. The poorer the language, the less thinking that occurs. And what is the cause? Probably the compromise that transformed language into communication, i.e. words that were sufficient for “the few,” gradually had to be adapted to the understanding of many, effectively losing most of those lexical variables and variants that risked not being understood by “the many.”
But let's take a step back and look at why language is considered the factor that has made us the dominant beings on Earth, and therefore, why the impoverishment of language is a definitely not insignificant problem.
Over history, in the process of exploring new habitats and running the greater risk of encountering predators, humans found themselves needing to increase their numbers and live in sufficiently large groups. For this reason, according to Robin Dunbar in ""The Evolution of the Social Brain," they invented language. Initially, the content of the messages was not explicitly necessary; it was sufficient to convey the message to the individual’s social group. Later, it became the means to exchange information about what was happening within the group.
Chimpanzees, who have not acquired language, are limited in what they can know about relationships within their group since they can only know what they see. This was also the condition of the first hominids, and it would be the condition of humanity today if it were based only on personal experiences. In fact, our knowledge of the world would be infinitely less if it were derived from personal experiences and not from the language that allows us to learn from the story of others' experiences.
The historian Yuval Harari in "Sapiens, From Animals to Gods," argues that when you examine the content of human messages, you discover that it is mainly based on the behavior of members of the group. Talking about the experiences of others today is considered gossip. In short, language has proven to be, through gossip, the means for understanding the power relationships within one's own group.
Communicating "Look out, there is a lion!" to one's group is also the prerogative of many animals, but language has allowed us to enter the sphere of the abstract: "You can trust him."
Human societies originated in the ability to tell stories and convince others to believe them. This then gave rise to the cognitive revolution of Homo sapiens, which consisted of creating an increasingly rich and powerful imagined reality alongside the objective (physical) reality. Most human thinking is not about objective reality, but imagined reality (Dumbar).
The language associated with an imagined reality is transformed into thought and then into Communication.
The Time Factor
Over the next 150,000 years, the need to create larger and larger groups to face opportunities and dangers together increased and communication evolved, producing a constant "Flynn effect." The printed page made it possible to activate groups of people no longer united by a habitat but by realities imagined by someone they did not know. This allowed them to still feel as if they belonged to a group where their thoughts could be understood and shared. This is how communities - groups of people united by common thoughts (imagined realities) - were born.
Communities become gigantic. For example, entire nations can know something, even if they don't see it with their own eyes, just as if they are all watching a soccer match with millions of like-minded people, all cheering for the same team.
Communication served to equip humans to face dangers and seize opportunities, but in addition to experiences, things were increasingly transferred. First in exchange, then in sale.
To capture the attention of large numbers of people in order to sell things, the written message was progressively reduced to a few words that contained complex concepts but could be understood by large groups of humans. The visual message was enhanced through graphics. For decades, this "one-to-many" approach enabled levels of extraordinary graphic and literary sophistication in creating effective messages.
But if the "Flynn effect" significantly improved the effectiveness of communication, it also increased the cognitive efficiency of those who received the messages. It was no longer enough to say "buy this because it’s good and beautiful" or even "buy this because it’s the best and the most beautiful," it was now necessary to convince people of the urgency to buy and then to qualify the product for sale. Thus were born the concepts of Positioning, Branding, Purchasing Funnel, and the Marketing Mix, up to Inbound marketing.
In this evolutionary path, time has played a two-fold fundamental role. While it has increased the effectiveness of communication so that it can include billions of people all at the same time, it has also become an element of competition between the cognitive abilities of communicators and those of the recipients, who in the meantime were adapting, becoming increasingly sophisticated, and finally rejecting messages that were too generic and banal.
Who I am for you
Today, communicating means first of all thinking about if what I am saying will make sense and if it will be credible for the majority of the people that I want to reach.
This is a factor that is increasingly complex to manage because communities are becoming tremendously different. Potential buyers of the same product belong to extremely different social groups, with different levels of education, different needs, different contexts, and different habitats.
Already in 1948, Louise Engel understood the theme of cognitive time and that of differentiated communities and launched one of the most successful campaigns in history.
The United States was experiencing the beginning of an economic boom and the rate of education was growing exponentially. Louis Engel was tasked with promoting Merrill Lynch, unknown at the time, and finding new customers. These were the days of the first shouted, crude, and cheesy marketing slogans of "Buy! Buy! I'm Great and Beautiful!" But Engel understood that what he was selling had to first be understood, be considered reliable, and be suitable for each of the individual potential customers involved. So he decided to address some of their customers’ most common questions. The result was a 6,450-word ad in The New York Times that answered each of these questions and the value was extraordinary: For a $5,000 investment, he got more than 3 million inquiries.
Engel understood that it was time to move from "who do I want to look like to you" to "who AM I to you." This was a way of presenting the company to customers, replacing a manipulative message with one that reflected the credibility and reliability of the communicator, despite the risk associated with an extremely long ad. The sense was: if you can't or won't read it all, you're not really interested.
The Data Factor
It’s 2021. All human knowledge, or almost all of it, is now digital. People, companies, autonomous computers, every device connected to the network produces and exchanges data in the form of files, documents, images, and videos. Every day, 500 million tweets are published, and 294 billion emails are sent, almost 25 emails for every person on Earth. Four petabytes of data are created by Facebook alone, not to mention that an estimated 65 billion messages are sent on WhatsApp.
Using internet traffic reports from Cisco and other network operators, we can estimate that the entire digital universe is roughly 44 zettabytes, or 440 billion gigabytes (Gb). If this estimate is correct, it means that we have available in bytes 40 times the number of observable stars in the universe.
Selecting these sources of data production in 2025 we could estimate an amount of 463 exabytes of daily information, the equivalent of 212765957 DVDs per day. This is not only what is transmitted machine-to-machine, but also what will be generated with 5G communications. If everything goes as it should, within two years a new internet will overlap with the current one. Unlike the former, these mobile networks will contain mainly non-human data, and they will be capable of handling human processes and knowledge.
The data we produce every day is not just about social and online shopping: it's also scientific and institutional data, data dealing with research, business, and family data. We are always connected, we share our entire lives, sometimes even unconsciously, which are automatically tracked by countless channels. This monstrous amount of data that emerges is now within reach of many companies that have decided or will decide to exploit its enormous potential. This is all done with our approval, because after all, if I tell you that I prefer blue over red, it costs me nothing if you give me a free service in return.
From the processing of this data we can forecast elections, we can identify information that will allow us to prevent epidemics, to optimize transportation, to take advantage of the Internet of Things, and of course, to develop increasingly personalized communication.
In 2016, a giant advertisement in downtown Manhattan displayed the following inscription: “Dear person who played ‘Sorry’ 42 times on Valentine’s Day, what did you do?”
Even without the name, if I had been the one who had listened to "Sorry" 42 times, they would have identified me by my face flushed with shame. This was Spotify's brilliant campaign put together to welcome the year 2017 that would arrive within days. The goal was not to entertain passersby but to showcase the future.
Spotify was proving that it could capture its users' data and turn them into a personalized message, but the future it was representing was its ability to automatically transform inert and inherently cold data into empathy. Machines that autonomously generate emotional languages represented a technology that not only surpassed personalized emails, basically the same texts for everyone with only the personalization of the name, whose effectiveness was already on the horizon, but concretely projected the world of communication from "who am I for (all of) you" to "who am I for you (the individual)."
The Personas Factor
In the 20th century, the work of marketing and communications agencies was centered on identifying targets and clusters of humans to whom to address specific messages.
They worked on statistics based on a few parameters: Place of residence; age, gender, activity. These parameters allowed us to draw a profile that was too approximate and rarely correspond to reality. The consequence was to weaken the messages with details that risked not being understood by all members of the target audience. If, on the one hand, this problem stimulated creatives to invent more and more comprehensive and effective messages, on the other, it generalized and standardized the language with the consequence that emerged in the "reverse Flynn effect."
The beginning of the 21st century, however, saw the introduction of the "Personas" factor.
Personas are the sketches of a fictitious human, but one that is realistic and corresponds to the ideal customer.
The idea of Personas came from software developer Alan Cooper. Dissatisfied with the ease of use of computer programs at that time (we are talking about the 1980s), Cooper developed a concept in which the user was at the center of development. Therefore, an application initially linked to the world of computer science and on data from interviews and analysis, but in the new millennium, thanks to the collection of countless data on each individual, has allowed us to move from an abstract idea of a target to a prototype of a concrete individual. This was populated every year with more characteristics, not only age, gender, and residence but preferences, tastes, and behaviors that define his diversity.
There are new characteristics that can be extracted from social networks, online purchases, Google searches, and interactive digital content that promoted the user from passive reader to active producer of data.