A Reactive Approach for High-Accuracy and Data-Driven Customer Behaviour Analysis and Prediction

Understanding the differences of high accuracy and data driven customer behaviour is one of the essential components of success in the e-commerce industry because customer behaviour varies from person to person depending on their segmentations. Owners will be able to recognise their desired customers by comprehending customer behaviour. They will be able to better target their marketing efforts, boost sales, and control costs. Applications of artificial intelligence in this area have a significant positive effect on operations. The most attributes that can influence a customer’s behaviour can be found and predicted by business partners using a data mining prediction model. The current study therefore identifies better ways to improve business decision-making through the use of AI and data analytics, which will aid in comprehending customer behaviour. This research, brings forth ideas and concepts to make models more data driven and more accurate through a more reactive approach on model designing, through clever infrastructural designing. This paper proves that the proposed method can assure users to get better results through reactive-data methodologies. This paper proves it practically by taking a simple classification problem, in contract to customer-behaviour prediction.