Cognitive Analytics is a field of Analytics that tries to mimic the human brain by drawing inferences from existing data and patterns, draws conclusions based on existing knowledge bases, and then inserts this back into the knowledge base for future inferences – a self-learning feedback loop.
Cognitive Analytics brings together a series of intelligent technologies that include semantics, artificial intelligence algorithms, deep learning, and machine learning. By applying these techniques, a cognitive application can become more intelligent and effective over time by learning from its interactions with data and with humans.
Cognitive Work Analysis (CWA) is a framework that was developed to model complex socio-technical work systems. The framework models different types of constraints, building a model of how work could proceed within a given work system.
Applied in the company, Cognitive Analysis can be used to close the important gap between large volumes of information and the need to make decisions in real-time.
In-depth knowledge of information helps companies take advantage of the wide variety of information sources in their knowledge base to improve the quality of business knowledge, competitive positioning and provide an in-depth and personalized approach to customer service.
Commercial Applications of Cognitive Analytics:
1) Audience segmentation
Audience segmentation is a crucial part of providing relevant messages to consumers. Most marketing specialists use demographic data to segment their audience into groups with similar desires and needs.
In contrast, Cognitive Marketing systems look for massive data sets from a wide variety of sources, such as web analytics, social networks, and purchasing behaviour, to find customer segments that exhibit similar behaviour.
2) Customizing the content
After receiving the original content, a cognitive marketing engine could redesign the message so that virtually every person saw something different.
The system would use data from social networks, browsing behaviour, and even the feeling of communications with customer service to reformat content for a particular individual.
3) Customer Support
Digital assistants on our phones, such as Siri, have preprogrammed answers to a limited number of requests and questions. Still, customer service technology with cognitive computing capabilities can understand natural language, accurately answer questions from people, and run customer service more efficiently.