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Collecting data is useless if you don't know what to do with it

Collecting data is useless
I've chosen a deliberately provocative title for this brief post. The aim is to sketch the boundaries of a small reflection on the tools, methods, and manners of digital communication. In advertising, one of the most famous claims is the one that has accompanied Pirelli for a quarter of a century: "power is nothing without control". But has it truly made a difference, or is it merely a relic of a pre-digital world?

Up until about fifteen years ago, when the best way to gain recognition was an OOH campaign, visibility was the exclusive domain of major media centers or advertising space providers, regardless of the more or less memorable creativity. The expenditure effort to obtain spaces was established based on parameters that, in some cases, were difficult to measure (for example, depending on the geographic location of the posting, statistics on public exposure were provided that were hard to dispute).

Speaking of digital marketing, according to a study by eMarketer, in 2021, global digital advertising spending accounted for 54.2% of total advertising expenditure, while spending on traditional media accounted for 45.8%. Market research follows that the gap between investments in digital media and traditional media is continuing to grow in favor of digital media, marking a decisive separation by 2024. The increase in digital device usage and consumers migrating to new content delivery sources justify the trend.

Alongside the increase in investments, an ever-growing number of tools for measuring equally infinite performance indicators are emerging. My Chrome, for example, is full of banners advertising monitoring software, from Semrush to Buzzsumo, Fanpage Karma, through Google Trends, Analytics, and various tools developed by proprietary platforms. The perspective and potential of data collection are undoubtedly exciting and at the same time frustrating, swinging the projection of oneself from John Nash to Alfred Kinsey when, as a biologist, he studied gall wasps.

But how can one navigate a whirlwind of acronyms and dowsers from editorial plans full of suggestions, advice, and the "5 moves for"?

Between 2009 and 2011, Tim Roth played the role of Cal Lightman, an esteemed and controversial psychologist in the series "Lie to Me". The entire series revolves around the psychological analysis conducted on the non-verbal indicators of human behavior: from how we clasp our hands, to where we look when evoking a memory (up and to the left, btw), to the comforting and unconscious gestures we make when we're nervous. Why this cinematic digression? Simple, to make a trivial yet invisible concept easy to understand. No matter how many signals, indicators, revealing gestures there may be, none can answer one question: Why.

The perspective of skills and analysis we're discussing goes beyond technical abilities and shifts towards what are considered horizontal or soft skills. In marketing, horizontal skills are those abilities that allow for a broad, bird's-eye view. In essence, this practice refers to gathering data from many different sources, instead of focusing on a single one. Most of us are familiar with Romanesco broccoli, but few know that due to the internal homothety that characterizes it, it falls under the theories of Benoît Mandelbrot, a mathematician of Polish origin, who developed a new mathematical model known as fractal geometry. Mandelbrot was able to find this solution thanks to his cross-disciplinary understanding of many different areas, including mathematics, physics, and biology.

Associated with the broad search for sources is the adoption of lateral thinking: a creative approach to problem-solving that involves thinking unconventionally and outside the box. From amusing anecdotes to solving engineering problems, lateral thinking helps break free from the dead ends of vertical thinking and allows data-driven strategies to become truly performant, opening up new market segments, the blue oceans.

Google and Netflix use a combination of data collection and analysis and lateral thinking to improve their products and services. Airbnb uses data and location to enhance the customer experience, such as recommending vacation spots and personalizing content. Spotify applies the same model to develop and recommend personalized playlists to its users.

Crowdsourcing (Amazon Mechanical Turk or CrowdFlower) and gamification (Nike+ Running or Duolingo or the local Fanta Sanremo) are less known but equally widespread methods of applying lateral thinking.

The horizon of a cookieless world, through predictive marketing combined with marketing automation and lateral thinking models, turns the paradox of data loss into a great opportunity for marketers and agencies. A data-driven strategy is incomplete if the analysis does not become the propulsion to improve effort and results; history is full of incorrect analyses and even worse applications of conclusions.

In extreme summary, the emphasis should not be placed so much on one tool over another but rather on a perspective that allows for the inclusion of multiple paths, methodologies, and approaches that must then be verified, and verified, and verified, and verified, and verified.

If, for some reason, it seemed like an easy path, or one that could be comfortably pursued from the couch, I'll close with one last anecdote. To know how long it takes to develop critical thinking and systematize it, we can ask Picasso about the napkin episode.

 

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