How predictive models work and how they can help (but not replace) us in listening to people’s real needs and designing quality experiences.
In recent years, in the research field but also at the corporate level, there has been enormous interest in what are defined as “predictive models”. But what are these tools really? What are they or are they not capable of and why has interest only developed now?
Without getting too technical, if you search online for a definition of a predictive model, you get something similar to this:
“Statistical analysis tools that provide a more accurate view of the future, based on past and present data.”
However, digging deeper into corporate contexts, the perception is more that of holding a crystal ball that somehow manages, through very complex mathematical calculations, to provide a certain answer: “a Palantír”, or seer stone, for the most accustomed to fantasy. Paraphrasing Tolkien, however, predictive models, just like Palantír, do not explain everything to us, but instead, give us an idea of what the future could be.
It is therefore essential to understand that these statistical tools incorporate, like everything in this world, an error component that must be taken into account in their final application. The predictive model is therefore a tool which, based on past events, provides us with insights into what could be future events.
Predictive models are now back in vogue mainly for two reasons:
Currently therefore, thanks to these two satisfied conditions, the models are now able to take into consideration a sufficient amount of data relating to events that have occurred over time, to recognize patterns within them and be able to recreate that type of event/variable of interest in a future time. This skill has turned out to be incredibly useful in many forms and contexts. Indeed, thanks to predictive models, organizations can:
And these are just some of the application examples.
Some of the first approaches to the use of predictive models come from the production field: in fact, here, the possibility of saving different types of data has allowed companies, for example, to develop models which, by monitoring the status of the devices, are able to understand where the most frequent faults are present and to develop a specific strategy to deal with the problem, for example, optimizing the maintenance service or anticipating the occurrence of the malfunction by optimizing the most prone components.
And in the digital context however, how could these models be used?
In addition to the applications seen above, predictive models can also be used within agencies. How?
For example, to optimize marketing campaigns. Using these tools can help companies identify which people are more likely to buy a specific product or service, and can also help plan the most effective marketing campaigns. Predictive models, therefore, are an indispensable tool for any organization that wants to make informed decisions and improve its productivity.
In conclusion, the use of these rediscovered techniques can be of great help if used with criteria and can be an almost inexhaustible source of insight, it is important to keep in mind however that predictive models are only a tool and cannot be used as a substitute for human decision.
The person continues to be indispensable and it is essential to use technology and skills as listening tools to listen to their deepest needs and respond to specific needs with products and services created to give quality experiences.