Cluster analysis is an important branch of statistics and data analysis that, founded as a humanistic discipline, is currently one of the disciplines of study involving artificial intelligence and the professionals who study it. To understand what they are for, it is first necessary to clarify what clusters are and what they are used for.
What are clusters?
The definition of cluster and the subsequent terms clustering or clusterization derive from studies on the behavioral psychology of Robert Tyron, but today they are part of statistical science and therefore of data analysis. According to the scholar, a cluster is a set of elements with at least one characteristic in common.
In the case of human beings, a cluster can be created with all the brown-haired individuals and another with the blond individuals. Hierarchical clustering makes it possible to further divide these groups into individuals with brown hair and light eyes; individuals with brown hair and dark eyes; blond individuals with light eyes, etc. Continuing with this scheme, clusterization leads to the definition of groups with increasingly specific and well-defined characteristics.
It is clear how this methodology can be easily applied to the analysis of user data on a website, for example through information collected by software or artificial intelligence. The analysis of the clusters identified in this way leads to a simple segmentation of users, which can then be used in numerous sectors within a company.
Types of clusters in which to segment your audience
If we take, for example, types of clusters that group users from the same geographical origin; that have the same buying habits; that connect to a site from the same type of device; we can easily segment our audience.
This allows countless advantages in terms of:
- local marketing;
- customer service;
- content optimization for mobile devices;
- automatic suggestions of content, products and services that may interest the user.
In short, with the analysis of the clusters theConversational artificial intelligence is able to recognize each user, place them in the right target group and behave with them accordingly. This allows for a more natural and personalized relationship between conversational AI and users, but also an increase in sales and conversion rates. Which, in the end, is the main purpose of using a Chatbot Or a voicebot in the company.
Two Ways to Do Clustering
We know that many conversational AIs learn from their previous interactions with users. By exploiting the discipline of machine learning, that is, machine learning based on previous experiences, it is possible to do clustering with two methods.
Machine learning
The first is machine learning, the most widespread and used by conversational AI. The data collection takes place starting from inputs that the programmer provides to the software.
The Natural Language Processing that AIs use to understand and imitate human language is based on machine learning in these cases:
- classification, that is, the recognition of characteristics of the user, the place, the device starting from known characteristics;
- the disambiguation between names, places, associations and organizations with the same name based on the context in which they are used;
- text-to-speech transcriptions;
- the causality patterns between a text and the reactions it evokes (for example the number of little hearts received on Twitter from messages containing certain words and topics).
Supervised training
The other type of machine learning is unsupervised training. The software does not provide information of any kind on the data, and this is where the discipline of cluster analysis shines.
In fact, it is the same artificial intelligence that brings out recurring patterns and similar characteristics from the data: in a nutshell, it creates clusters on its own. In this way, the software of Generative artificial intelligence You can obtain from the data provided by users and other information (from which devices they connect; how much time they spend on the site; with what query they landed on that web page) vital information for the continuation of the relationship with the company.
The relationship between cluster analysis and AI
The fact of not knowing any characteristics of users could be a limit for conversational artificial intelligence. In this way, in fact, the software starts blindly, without having guidelines, collecting information and gradually discovering who it interacts with. This could lead to errors and anomalies in the conversation.
Keep in mind, however, that this is not necessarily a limit for conversational AI: where the software recognizes similar behavior patterns, it will react in a similar way to the inputs received from users. If, on the other hand, cluster analysis is only one step in larger research, the collection of this data can provide the starting point for improving the capabilities and functions of conversational software.
What counts in cluster analysis is always the human element. Faced with data and portions of text on which it has no information, artificial intelligence alone may not be enough. The intuition of data analysts and machine learning specialists plays a fundamental role.
