Customer profile classification using transactional data

Customer profiles are by definition made up of factual and transactional data. It is often the case that due to reasons such as high cost of data acquisition and/or protection, only the transactional data are available for data mining operations. Transactional data, however, tend to be highly sparse and skewed due to a large proportion of customers engaging in very few transactions. This can result in a bias in the prediction accuracy of classifiers built using them towards the larger proportion of customers with fewer transactions. This paper investigates an approach for accurately and confidently grouping and classifying customers in bins on the basis of the number of their transactions. The experiments we conducted on a highly sparse and skewed real-world transactional data show that our proposed approach can be used to identify a critical point at which customer profiles can be more confidently distinguished

Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

Predicting the class of a customer profile is a key task in marketing, which enables businesses to approach the right customer with the right product at the right time through the right channel to satisfy the customer's evolving needs. However, due to costs, privacy and/or data protection, only the business' owned transactional data is typically available for constructing customer profiles. Predicting the class of customer profiles based on such data is challenging, as the data tends to be very large, heavily sparse and highly skewed. We present a new approach that is designed to efficiently and accurately handle the multi-class classification of customer profiles built using sparse and skewed transactional data. Our approach first bins the customer profiles on the basis of the number of items transacted. The discovered bins are then partitioned and prototypes within each of the discovered bins selected to build the multi-class classifier models. The results obtained from using four multi-class classifiers on real-world transactional data from the food sales domain consistently show the critical numbers of items at which the predictive performance of customer profiles can be substantially improved

The Role of Internet in Marketing Strategies

The use of the Internet has increased in recent years remarkably. Conducting business in the digital economy means using Web- based systems on the Internet and other electronic networks to do some form of electronic commerce. Many research findings confirm and support being of positive effects of Internet on an enterprise's competitive advantage. In this paper, I will illustrate that enterprises can acquire relational and informational competency through Internet technology, and based on these competencies they can succeed in competitive cyber markets. According to the Internet competencies, Internet marketing strategies can be divided into five categories: Transactional, Profile, Customer-oriented, Relationship, and Knowledge strategies. Choosing and implementing any category of strategies depends on the degree of internet competencies (informational and relational) that a firm has. When both are high, proper internet marketing strategy seems to be knowledge strategies; and when both are low, transactional internet marketing strategies would be the suitable category.Internet marketing strategies; Information technologies; Network computing; Digital economy; Information system.

Mining Bad Credit Card Accounts from OLAP and OLTP

Credit card companies classify accounts as a good or bad based on historical data where a bad account may default on payments in the near future. If an account is classified as a bad account, then further action can be taken to investigate the actual nature of the account and take preventive actions. In addition, marking an account as "good" when it is actually bad, could lead to loss of revenue - and marking an account as "bad" when it is actually good, could lead to loss of business. However, detecting bad credit card accounts in real time from Online Transaction Processing (OLTP) data is challenging due to the volume of data needed to be processed to compute the risk factor. We propose an approach which precomputes and maintains the risk probability of an account based on historical transactions data from offline data or data from a data warehouse. Furthermore, using the most recent OLTP transactional data, risk probability is calculated for the latest transaction and combined with the previously computed risk probability from the data warehouse. If accumulated risk probability crosses a predefined threshold, then the account is treated as a bad account and is flagged for manual verification.Comment: Conference proceedings of ICCDA, 201

The interface between transactional and relational orientation in small service firm's marketing behaviour

This paper presents and discusses findings of a cross-country study of small service firm marketing behavior. These findings demonstrate that small service firms are flexible in the marketing approaches that they adopt. They reveal that such firms are transactional and relational orientated in their marketing activities and that for growing firms, marketing activities are used to create short-term transactions and form relations with key stakeholders. This finding implies that transactional and relationship marketing should be regarded as complementary. The findings presented also demonstrate that the marketing approach selected by participating small firms is determined by a range of customer characteristics of which repeat business is only one. An integrated framework containing elements of transactional and relational approaches is proposed as an appropriate way of describing the marketing behaviours of investigated firms

Marketing practices and performance in a post-crisis scenario

This research explores the link between contemporary marketing practices, market orientation and business performance in Uruguay, an emergent country that has recovered from an economic crisis. These approaches seem to be related, but there is no existing evidence to confirm this impression. Lessons can be learned from understanding how effective is the adoption of marketing practices under a crisis scenario. Using data from interviews with 143 micro and small enterprises’ managers, we identify three clusters dependant on the combination of marketing practices: a multi-marketing cluster, a medium-level relationship marketing cluster and a transactional cluster. A model relating market orientation components and various performance measures is tested for the three clusters, showing that the multi-marketing and transactional clusters are more effective in translating efforts and resources into business outcomes

Current marketing practices and market orientation in the context of an emerging economy: the case of Uruguay

This research explores the link between contemporary marketing practices, market orientation and business performance in Uruguay, an emergent country that has recovered from an economic crisis. These approaches seem to be related, but there is no existing evidence to confirm this impression. Lessons can be learned from understanding how effective is the adoption of marketing practices under a crisis scenario. Using data from interviews with 143 micro and small enterprises’ managers, we identify three clusters dependant on the combination of marketing practices: a multi-marketing cluster, a medium-level relationship marketing cluster and a transactional cluster. A model relating market orientation components and various performance measures is tested for the three clusters, showing that the multi-marketing and transactional clusters are more effective in translating efforts and resources into business outcomes.Contemporary marketing practices, Market orientation, Performance, Clusters, Structural equation modeling, Uruguay