Integrating Data Mining Into Business Intelligence

Data Mining is a broad term often used to describe the process of using database technology, modeling techniques, statistical analysis, and machine learning to analyze large amounts of data in an automated fashion to discover hidden patterns and predictive information in the data. By building highly complex and sophisticated statistical and mathematical models, organizations can gain new insight into their activities. The purpose of this document is to provide users with a background of a few key data mining concepts and business intelligence and about benefits of integrating business intelligence and data mining.Business Intelligence, platform, data mining

Leveraging Deep-learning and Field Experiment Response Heterogeneity to Enhance Customer Targeting Effectiveness

Firms seek to better understand heterogeneity in the customer response to marketing campaigns, which can boost customer targeting effectiveness. Motivated by the success of modern machine learning techniques, this paper presents a framework that leverages deep-learning algorithms and field experiment response heterogeneity to enhance customer targeting effectiveness. We recommend firms run a pilot randomized experiment and use the data to train various deep-learning models. By incorporating recurrent neural nets and deep perceptron nets, our optimal deep-learning model can capture both temporal and network effects in the purchase history, after addressing the common issues in most predictive models such as imbalanced training, data sparsity, temporality, and scalability. We then apply the learned optimal model to identify customer targets from the large amount of remaining customers with the highest predicted purchase probabilities. Our application with a large department store on a total of 2.8 million customers supports that optimal deep-learning models can identify higher-value customer targets and lead to better sales performance of marketing campaigns, compared to industry common practices of targeting by past purchase frequency or spending amount. We demonstrate that companies may achieve sub-optimal customer targeting not because they offer inferior campaign incentives, but because they leverage worse targeting rules and select low-value customer targets. The results inform managers that beyond gauging the causal impact of marketing interventions, data from field experiments can also be leveraged to identify high-value customer targets. Overall, deep-learning algorithms can be integrated with field experiment response heterogeneity to improve the effectiveness of targeted campaigns

The role of IT/IS in combating fraud in the payment card industry

The vast growth of the payment card industry (PCI) in the last 50 years has placed the industry in the centre of attention, not only because of this growth, but also because of the increase of fraudulent transactions. The conducted research in this domain has produced statistical reports on detection of fraud, and ways of protection. On the other hand, the relevant body of research is quite partial and covers only specific topics. For instance, the provided reports related to losses due to fraudulent usage of cards usually do not present the measures taken to combat fraud nor do they explain the way fraud happens. This can turn out to be confusing and makes one believe that card usage can be more negative than positive. This paper is intended to provide accumulative and organized information of the efforts made to protect businesses from fraud. We try to reveal the effectiveness and efficiency of the current fraud combating techniques and show that organized worldwide efforts are needed to take care of the larger part of the problem. The research questions that will be addressed in the paper are: 1) how can IT/IS help in combating fraud in the PCI?, and 2) is the implemented IT/IS effective and efficient enough to bring progress in combating fraud? Our research methodology is based on a case study conducted in a Macedonian bank. The research is explorative and will be mostly qualitative in nature; however some quantitative aspects will be included. The findings indicate that fraud can take up many forms. A classification of the different forms of data theft into different fraudulent appearances was made. We showed that the benefits from implementing the fraud reduction efforts are multiple. Results show that a bank has to be very small to experience losses from fixed expenditures coming from the implementation of the fraud reduction IT/IS. Medium-sized and large banks should not even see any problems arising from those expenditures. Based on the empirical data and the presented facts we can conclude that the fraud reduction IT/IS do have a positive effect on all sides of the payment process and fulfills the expectations of all stakeholders

PSEUDO-MULTIVARIATE LSTM NEURAL NETWORK APPROACH FOR PURCHASE DAY PREDICTION IN B2B

This research focuses on trying to predict the moment of the next purchase for a customer in vendor-customer B2B scenario using an LSTM neural network and comparing prediction results from different input features. In a previous research we performed predictions for a specific customer product pair and used previous purchases for that pair as input data, but  the number of such previous purchases was often very limited which resulted in low accuracy of predictions. By aggregating purchase data for all products a customer purchased, we were able to get more precise predictions of the next purchase. Additionally, expanding our input feature set yielded even better results. We performed an evaluation of LSTM networks trained with the most successful combination of input features for a six month period. Each of the networks was trained with purchase data up to the starting point of the selected period and the predictions were performed, after which additional input for the following seven days was added to the network. This process was then repeated for the entire six month period and a slight downward trend can be noticed for error metrics, leading to the conclusion that the network would perform even better over time with the addition of future purchases