In a data-driven world, companies not only need to receive data, they need insights that can be immediately used for strategic decisions. So what is the difference between analytics and insights and what opportunities does their analysis offer?
Analytics are the result of systematic computational analysis of data: they are used for the discovery, interpretation and communication of meaningful patterns in data.
They transform data into statistical series, i.e., they tell the frequency with which an action is performed (e.g., how many people click on a button in an e-commerce store) or the frequency with which a certain phenomenon occurs (e.g., how many people mention a brand across different social networks).
They are represented with dashboards, graphs and tag clouds, tell the story of the current situation and, thanks to statistical formulas, can be used to make future predictions of the frequency and likelihood of an event happening.
Analytics track a behavior or an occurrence, but without explaining why it happens, an answer that is instead provided by insights.
Insight is a word that has become fashionable on the Web, which is why those who provide analytics services are increasingly using it, but often incorrectly.
Insights are that complex information that gives real value to data and analytics: through an interpretation of data, we turn a statistical number or percentage into comprehensive information that is functional for strategic and operational decision making.
Insights are also based on the analysis of the historical past and, starting from complex information, allow us to predict the future emotional behavior of people, whether they are consumers or workforce.
Insights are thus meaningful concepts that answer a specific question that always begins with "why?" In their most complex form, they are qualitative-quantitative data that do not simply track a behavior but explain the reasons for it: why that behavior occurs.
An insight is thus the complex information that explains the apparent incongruence of consumption and work behaviors, highlighting the emotional part of decisions and actions. Elements not detectable by analytics.
Deep insights are just that: complex qualitative-quantitative insights that are useful for a thorough understanding of the myriad facets (and sometimes inconsistencies) in people's consumption behaviors.
Like the pieces of a jigsaw puzzle, they show the full picture with a rich perspective.
Insights are the purchasing drivers, needs, values and emotional levers that influence our behavior in the workplace, for example: the reason why in the midst of a recession the phenomenon of the great resignation was exacerbated.
It is now clear that insights are not simple analytics, because they are not rationalized data in dashboards, but complex, detailed and all-encompassing information about all aspects of people's online and offline lives.
This is why relying on social and web listening softwares alone is not sufficient to fully understand the insights of target consumers or employees.
By combining the potential of quantitative surveys (i.e. online surveys) with the qualitative investigation of open data, InTribe has developed innovative consumer insight analysis technology: thanks to consumer insight data intelligence, it is possible to increase the relevance and depth of study of people's needs, lifestyles, and habits.
The real potential of consumer insight data intelligence is the ability to do predictive analysis on people's emotional behaviors, taking advantage of enhanced market research using open data.