Driving Business Growth from Research to Innovation in the Deployment of Business Intelligence

Abstract

This research is concerned with the application of BI algorithms in business growth and innovation as demonstrated by the various experiments and analyses in this scientific study. Among four types of BI algorithms which are Linear Regression, Decision Trees, K-Means Clustering, and Association Rule Mining (Apriori Algorithm), all these four were chosen as they are useful when dealing with a large set of data that contains transaction information, customers’ profile, and market indexes from a retail company. Overall the study findings suggest useful additions to improving operation effectiveness and managerial decisions. Mean Squared Error (MSE) through Linear Regression was established to be 120. 5 with an R-squared (R2) of 0. 75, offering reliable and accurate information on customers’ buying pattern. The result revealed that Decision Trees obtained the accuracy of 0. In customers’ classification: 85, which allows to differentiate the approaches to the market’s management. Based on K-Means Clustering, different customer segments were obtained with silhouette scores affirming the clusters’ strength. The results of Association Rule Mining were indeed practical and patterns in degree of confidence for strategic product positioning & Cross-sell Suggestions. The comparison with the related work emphasizes the significance of these algorithms, presenting their success in various business applications. As such, these outcomes indicate the BI algorithms’ great potential in promoting efficient resource management, increasing customer satisfaction, and supporting the sustainable development of businesses.