Yeliz Ekinci

Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model

The general aim of this study is to provide a guide to the future marketing decisions of a firm, using a model to predict customer lifetime values. The proposed framework aims to eliminate the limitations and drawbacks of the majority of models encountered in the literature through a simple and industry-specific model with easily measurable and objective indicators. In addition, this model predicts the potential value of the current customers rather than measuring the current value, which has generally been used in the majority of previous studies. This study contributes to the literature by helping to make future marketing decisions via Markov decision processes for a company that offers several types of products. Another contribution is that the states for Markov decision processes are also generated using the predicted customer lifetime values where the prediction is realized by a regression-based model. Finally, a real world application of the proposed model is provided in the banking sector to show the empirical validity of the model. Therefore, we believe that the proposed framework and the developed model can guide both practitioners and researchers.

Fuzzy rule-based demand forecasting for dynamic pricing of a maritime company

In this study, the pricing problem of a transportation service provider company is considered. Our goal is to find optimal prices by using probabilistic dynamic programming. A fuzzy IF-THEN-rule based system is used to identify the demand levels under different prices and other characteristics of the journey. The results obtained by optimal price policies show that the revenue increases by applying dynamic pricing policy instead of fixed pricing. Thus, the diversification of pricing policies under different conditions is beneficial for the company.

A customer lifetime value model for the banking industry: a guide to marketing actions

Purpose – The aim of this study is to develop an applicable and detailed model for customer lifetime value (CLV) and to highlight the most important indicators relevant for a specific industry – namely the banking sector. Design/methodology/approach – This study compares the results of the least square estimation (LSE) and artificial neural network (ANN) in order to select the best performing forecasting tool to predict the potential CLV. The performances of the models are compared by the hit ratio, which is calculated by grouping the customers as “top 20 per cent” and “bottom 80 per cent” profitable. Findings – Due to its higher performance; LSE based linear regression model is selected. The results are found to be highly competitive compared with the previous studies. This study shows that, beside the indicators mostly used in the literature in measuring CLV, two additional groups, namely monetary value and risk of certain bank services, as well as product/service ownership-related indicators, are also ...

Using customer lifetime value to plan optimal promotions

The purpose of this study is to develop a methodology to guide managers in determining the optimal promotion campaigns to be directed towards different market segments in order to maximize the value of customers. For the purposes of this study, a two-step methodology is used, based on stochastic dynamic programming and the classification and regression tree. This methodology groups the customers according to their value. Within this framework, an experiment is conducted in which each of the different promotion campaigns is assigned to different randomly selected groups. The impact of each type of promotion on each type of market segment is analysed in order to find the optimal promotion campaigns appropriate for each. In contrast to previous research, this study takes into account a firm that provides more than one specific type of product or service. In addition, it analyses the impact of widely used types of promotion campaigns compared with the narrow scope of those investigated in previous studies. Therefore, this research presents important insights into managing relations with the customers in a more interactive and profitable way.

ANALYSIS OF THE RESEARCH AND DEVELOPMENT EFFICIENCIES OF EUROPEAN UNION COUNTRIES

Research and Development (R&D) activities of the countries are of crucial importance in order to compete in the emerging market. Although this importance is widely recognized, the efficiency of these activities has been rarely examined in the literature. Therefore, this study is an attempt to analyze the R&D efficiencies of European Union (EU) member countries. EU countries are selected for this study since the competition between these countries is very high and they invest a significant amount of resources in this area. Data Envelopment Analysis (DEA) is used in order to measure the relative efficiency scores. Then, the effect of political and regulatory environment on R&D efficiencies of EU countries is analyzed via hypothesis testing. The relative efficiency scores and hypothesis test results give valuable information for social policy makers in making decisions about planning R&D activities. The findings will also be useful for the countries aiming to participate the union, such as Turkey.

Evaluating R&D performance of EU countries using categorical DEA

ABSTRACT Over the coming decade, Research and Development (R&D) performance will be the key component of bringing innovation and the determinant of global competitiveness of nations. Therefore, this paper presents categorical Data Envelopment Analysis (DEA) for evaluating R&D performance of European Union (EU) countries. We utilise the output-oriented constant returns to scale (CRS) and variable returns to scale (VRS) DEA models with categorical data, namely, CAT-O-C and CAT-O-V models. In addition to DEA based framework, to examine the relationship between R&D performance and political-regulatory-economic situation of the countries; three research hypotheses are stated and their results are analysed. Policy implications about R&D activities can be derived for EU countries from the findings of this study.

Special Control Charts Using Intelligent Techniques: EWMA Control Charts

In this chapter we consider the economical design of EWMA zone control charts for set of machines operating under JPS (Jidoka Production System). We provide an extensive literature review of intelligent systems in quality control deductively to fit our purposes. It starts with an overview of quality control charts; then, reviews charts designed for special purposes such as EWMA, CUSUM and zone control charts. Finally, as particularly related to this study, reviews of economical design and intelligent applications of EWMA are provided. We discuss and review Jidoka Production System and motivation of operating such a system. We suggest an intelligent control and repair system such that in a production system, machines are individually controlled and repaired when an out-of-control signal is triggered in the zone with the tight control limits, however a system-wide shut down and repair is conducted when the out-of-control signal is from beyond the inner (tight) control limits which is considered as an opportunity for repair and calibration of all machines. We illustrate and investigate the behaviour of control parameters, namely sample size, sampling interval and control limits, via a numerical study of a three-machine system through simulation. We also provide insights for implementation of several metaheuristics for the system setting discussed in this chapter.

A segmentation based analysis for measuring customer satisfaction in maritime transportation

AbstractThis study was conducted to profile customers according to the level of satisfaction with the service attributes of maritime public transport provided by Seabus Service Company (SSC), the sole provider of maritime transport in Istanbul. Such analysis needs to be conducted by considering market segments in terms of maritime transportation usage and post purchase behavior. This was accomplished by conducting quantitative research through face-to-face surveys of SSC passengers. According to the results by multivariate data analysis, including factor analysis and cluster analysis, six segments are revealed in terms of customer satisfaction level with the maritime service attributes. Moreover, there are significant differences among the segments in terms of usage frequency (travel frequency in this study), age and education level. Different strategies for different customer segments within the maritime passenger market to increase customer usage and satisfaction of maritime transportation in Istanbul a...

Intelligent Classification-Based Methods in Customer Profitability Modeling

The expected profits from customers are important informations for the companies in giving acquisition/retention decisions and developing different strategies for different customer segments. Most of these decisions can be made through intelligent Customer Relationship Management (CRM) systems. We suggest embedding an intelligent Customer Profitability (CP) model in the CRM systems, in order to automatize the decisions that are based on CP values. Since one of the aims of CP analysis is to find out the most/least profitable customers, this paper proposes to evaluate the performances of the CP models based on the correct classification of customers into different profitability segments. Our study proposes predicting the segments of the customers directly with classification-based models and comparing the results with the traditional approach (value-based models) results. In this study, cost sensitive classification based models are used to predict the customer segments since misclassification of some segments are more important than others. For this aim, Classification and regression trees, Logistic regression and Chi-squared automatic interaction detector techniques are utilized. In order to compare the performance of the models, new performance measures are promoted, which are hit, capture and lift rates. It is seen that classification-based models outperform the previously used value-based models, which shows the proposed framework works out well.