The Concept of Decision Intelligence in Artificial Intelligence

Intelligent decision making is an engineering discipline that complements data science with  social science theory, decision theory, and management science. Its application provides a framework for best practices in organisational and process decision-making to apply machine learning at scale. The basic idea is that decisions are based on our understanding of how actions lead to results. Smart decisions are a discipline for analysing these chains of cause and effect, and decision modelling is a visual language for representing these chains. Smart decisions are a discipline for analysing these chains of cause and effect, and decision modelling is a visual language for representing these chains. 

Decision Intelligence (DI) solves the world's most complex problems or the toughest problems, as they say. It connects human decision makers with technologies like machine learning, AI, deep learning, visual decision modelling, complex systems modelling, big data, predictive analytics, UX design , statistical analysis, business intelligence, business process management, causal reasoning, evidence analysis, and more. 

As marketing campaigns become more and more personalised, business intelligence can work for marketers. They can use predictive analytics to deliver personalised recommendation insights  to  customers. Predictive models can tell businesses clearly which customer segments will like a certain product and what the best price for that product would be. With this information, marketers can build effective data-driven sales and marketing strategies.

In digital marketing, marketers can use DI to study customers' online behaviour and understand their preferences. They can then analyse this data to identify leads and ensure  marketing activities are targeted and timely to convert. For example, when sending a discount offer or motivating customers to check out products in the cart. DI models with Natural Language processing can also help analyse customer queries and feedback to check if customers are satisfied with a product or service. They can also infer possible reasons for customer satisfaction or disappointment. Organisations can use all of this information to take appropriate action to prevent customer churn. Meanwhile, machine learning can run algorithms on  customer data, identify ideal customers, and generate lists of leads for targeted marketing campaigns.