Cluster analysis is an important branch of statistics and data analysis that originated as a humanities discipline but now rightfully belongs among the disciplines of study involving artificial intelligence and the professionals who study it. To understand what it is for, it is first necessary to clarify what clusters are and what they are used for.
The definition of clusters and the consequent terms clustering or clustering derive from Robert Tyron's behavioral psychology studies, but today they are part of statistical science and therefore part of data analysis. According to the scholar, a cluster is a set of items with at least one characteristic in common.
In the case of humans, one can create a cluster with all brown-haired individuals and another with blond-haired individuals. Hierarchical clustering allows these groups to be further subdivided into brown-haired and light-eyed individuals; brown-haired and dark-eyed individuals; blond-haired individuals with light eyes, etc. Continuing with this scheme, clustering leads to the definition of groups with increasingly specific and well-defined characteristics.
It seems clear how easily this methodology can be applied to the analysis of user data on a website, for example through information gathered by software or artificial intelligence. Analysis of the clusters thus identified leads to simple user segmentation, which can then be used in numerous areas within a company.
If we take for example types of clusters that group users from the same geographic origin; who have the same buying habits; who connect to a site from the same type of device; we can easily segment our audience.
This allows countless advantages at the level of:
In short, with cluster analysis,conversational AI 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 voicebot in the enterprise.
We know that many conversational artificial intelligences learn from their previous interactions with users. Taking advantage of the discipline of machine learning, or learning machines based on previous experiences, it is possible to do clustering by two methods.
The first is machine learning, the one most widely used by conversational AI. Data are collected from inputs that the programmer provides to the software.
The Natural Language Processing that AIs use to understand and imitate human language relies on machine learning in these cases:
The other type of machine learning is unsupervised training. The software is given no information of any kind about the data, and this is where the discipline of cluster analysis shines.
In fact, it is the artificial intelligence itself that brings out recurring patterns and similar characteristics from the data: in short, it creates the clusters itself. In this way, the generative artificial intelligence software can grasp from the data provided by users and other information (from which devices they connect; how long they spend on the site; with which query they landed on that web page) vital information for the continuation of the relationship with the company.
Not knowing any characteristics of users could be a limitation for conversational artificial intelligence. In this way, in fact, the software starts blindly, without having guidelines, gathering information and finding out as it goes along who it interacts with. This could lead to errors and anomalies in the conversation.
It must be kept in mind, however, that this is not necessarily a limitation for conversational AI: where software recognizes similar patterns of behavior, it will react similarly to input received from users. If, on the other hand, cluster analysis is just one step in a larger search, the collection of this data can provide the starting point for improving the capabilities and functions of conversational software.
What matters in cluster analysis is always the human element. When faced with data and portions of text about which it has no information, artificial intelligence alone may not be enough. The insight of data analysts and machine learning specialists plays a key role.