Mining Retail Telecommunication Data to Predict Profitability Small or earlystage businesses often require sources of equity, loans, andor debt funding to support their growth. An important part of the documentation accompanying funding requests can be derived from analytical data associated with the business. There are numerous commercial business intelligence (BI) tools to monitor data and generate business insights. However, most of the retail entrepreneurs still use manual andor simple techniques, having little time to dedicate to sophisticated BI tools. In this work, we consider how supervised learning models can be used for retail telecommunications businesses. Specifically, we examine how nearest neighbour techniques, feed forward artificial neural networks, Bayesian classifiers, and support vector machines can be used with retail telecommunication data. As indicated by our initial results we have been able to achieve precision of 95.5%, recall of 94.7%, and fmeasure of 95.1% which demonstrates that we can categorize retail telecommunication data based on the profitability.

CustomerBase Sequential Data Analysis: An Application of Attentive Neural Networks to Sales Forecasting This study applies a machinelearning model to analyze marketing sequential data to improve the performance of sales forecasting. Marketing data such as pointofsale data reflects the heterogeneity of customers, and it is difficult to understand the feature quantity, unlike image data. To properly understand the characteristics of customers and implement high accurate marketing measures, a model analysis that considers customer heterogeneity is required. This study focuses on artificial intelligence, which has been rapidly developing in recent years, and the study incorporates customer heterogeneity into machinelearning model parameters. To extract a certain trend from a customeru0027s purchase history and utilize this information for sales prediction, we focus on convolutional neural networks and attention mechanisms. We conducted experiments using real data to verify that the proposed model demonstrates good analytical performance.