written by Francesca Morpurgo
User profiling is the key element of any winning marketing strategy.
Especially now that the available amount of data has increased vertiginously, and at same time getting third party data is every day more difficult, is absolutely essential to take advantage of most advanced tools available on the market to achieve a user profiling granular and accurate to the right point.
To make an example, is nowadays universally accepted that, particularly for publishers, taking advantage of AI can remarkably increase monetization.
Machine learning can therefore help your business, especially in what refers to user profiling: let’s see how.
- Profiling and segmentation as the road to an effective marketing
- Machine learning and how to use it
- Limits and advantages of using machine learning for user profiling
Profiling and segmentation as the road to an effective marketing
We can know a lot (and need to) about a user: not only his personal data (gender, sex, age etc.) but his interests and preferences: what he bought in the past, his online habits, the how and when of his interactions, what stimulates his engagement and so on.
All of the above constitutes a user profile. All of these data is then collected, ideally in a Data Management Platform (DMP) and used to create segments; in other words, profiled groups of users that share given characteristics, which are then split based on given parameters, more or less useful depending on the business marketing goals. To cite some: Customer Lifetime Value, Retention Rate, Customer Satisfaction and so on.
The goal must be that of establishing a positive relationship, a conversation, as personalized as possible with our customer; or, at least, a relationship addressed to small groups, durable in time and productive of the expected results for our business.
Beware that the only way of achieving it is a previous, accurate profiling of data, a deep knowledge of company users.
The enormous amount of data now available (demographic variables to be considered, for example, can easily exceed 300!) makes segmentation, as well as a reasonable previous profiling, every time more difficult.
Here is where Machine Learning comes into play.
Machine learning and how to use it
Machine Learning is a branch of Artificial Intelligence, based on statistical and probabilistic techniques (which, in the case of Deep Learning, extends to neural networks).
Its goal is enabling a computer to learn autonomously, with or without human supervision.
In a nutshell, after due training a Machine Learning algorithm is able to identify rules and structures in enormous sets of data and extrapolate predictable behaviors.
Potentialities of Machine Learning techniques in user profiling are therefore self-evident: monitoring, collecting, cleansing and analyzing data manually would be extremely time consuming and difficult, not mentioning the need of resources normally not available to businesses, in terms of time as well as in terms of training.
Besides that, customers are not normally inclined to be profiled.
But Machine Learning techniques make it possible by examining customer’s habits, taking advantage of even the most subtle ones. At least in the case of non-exclusively virtual companies, ML can even use audio and video data, which can be analyzed and interpreted by using its algorithms.
Limits and advantages of using machine learning for user profiling
You might ask why, considering that user profiling has been used long before Machine Learning and Artificial Intelligence came to existence, we should use time and resources to make with the help of machines something that humans are perfectly able to do autonomously.
The answer is in the amount of data to be processed, as well as the speed and context of their evolution.
No serious investigation could be possible without the support of Machine Learning techniques.
Moreover, once the process is set up, we can handle a set of data gradually increasing, facing up to the scalability as well as automating some actions relying on a customization of increased complexity that would be unbelievable if only relying on traditional methods.
Finally, we need to consider that people often take with them prejudices or assumptions, implicit or explicit, while machines not only are exempt from these limits but are able to identify patterns that humans would never be able to notice.
It is obvious that human intervention is always needed (for example to understand what really drives the impulse to buy, or finding the correct balance between privacy and personalization).
A hybrid approach is therefore without question a winning choice, at least at the moment.
Whatever has to do with more sophisticated analysis, with real comprehension of human behavior in relationship with context, escapes to the moment to algorithms, which instead excel in simpler and circumscribed tasks, like analyzing tendencies of specific user clusters in relationship with specific characteristics obtained throughout profiling.
Hyper Profiling obtained through Artificial Intelligence techniques is indeed one among the winning strategies to allow the monetization of data, and is worthwhile to explore and take advantage of it in full.