What is Automatic Segmentation?
We are drowning in an ocean of information, and it is very common not knowing exactly what we are looking for in data. Setting well-defined categories or establishing fixed measurements according to which classify and organise data may be a very complex task, despite having in mind a clear objective. “I want to know my customers better” is a very frequent objective, but questions related to it, such as “What interest groups are there among my customers?”, still have no simple answer.
Segmentation techniques analyse high volumes of data and detect automatically related groups of elements, or even opposing cases that do not follow the standards. They are tools useful to:
Find related groups
Find related groups among customers within a CRM who share demographic characteristics or who share a preference for certain services or products.
Detect anomalous activities
Detect anomalous activities in information systems or networks, suspected to be intruders or illegitimate behaviour (zero-day attacks).
Group documents in sets automatically
Find out automatically different topics dealt with in large documented sources, and group documents in sets sharing similar topics.
As a result of applying segmentation techniques, valuable information is obtained from rough data, without the need of an expert to define previously the groups or elements needed to be detected.
How does it work?
Automatic Segmentation is grounded on the scientific discipline known as unsupervised machine learning. This transforms data into a numerical representation that allows calculating similarities between elements and detecting related groups or elements that are far more different than expected.
A process of segmentation results in a list of related groups detected in the data to be later studied by the analysts of the client. Segmentation may be carried out many times, whenever data under study are updated, which allows finding out changes on the groups identified, such as the emergence of new interests in a database of customers, or new topics in a set of documents.
As far as the service of detection of anomalies is concerned, the result of the segmentation process is a model of detection which can be displayed to identify anomalous data or behaviours in real time. As is the case with classification systems, the detection model generates a certainty score that indicates the degree of irregularity identified, so it would be possible to set business rules, for instance, to control accesses to a network and block automatically the ones showing a high score.
Benefits and value of IIC Automatic Segmentation solutions
Automatic Segmentation offers:
A better knowledge
A better knowledge of the business data, such as the customers databases, without manual studies.
Dynamic update of the patterns outlined to detect changes on clients or on the market and be able to react quickly.
Automatic real-time detection
Automatic real-time detection of unprecedented non-typical cases or anomalies (a complement to the existing rules and systems), with the aim to prevent security or management breaches.
Guaranteed efficiency of IIC Automatic Segmentation solutions
Over 25 years at the IIC vouch for our experience in data projects, where high value solutions have been designed for sectors such as interest groups detection in databases of customers.
IIC has a professional team of experts who combine their activity at the Institute with works associated to research, teaching and collaboration with universities, thus keeping up-to-date with the latest technologies. This knowledge and practical experience in the different Automatic Segmentation techniques allows us to develop customised solutions which fulfil the response times and efficiency required for each individual project.
Segmentation techniques analyse high volumes of data and detect related groups of elements or opposing cases that do not follow the standards.