Müşteri İlişkileri Yönetiminde etkinliği arttırmak için bir bulanık model önerisi

Bu çalışmada, dünya çapında yoğun bir ilgi görmekte olan Müşteri İlişkileri Yönetimi (MİY) uygulamalarının performans ölçümünü sağlamak amacıyla sayısal bir model önerilmiştir. Kurumsal Karne kavramı temelinde geliştirilen ölçüm modelinde, MİY ölçümünün 4 boyutta gerçekleşmesi önerilmektedir. MİY çıktıları boyutunda, firmanın MİY çalışmalarından beklediği temel faydalar yer almaktadır. Müşteri boyutu ise, müşterilerin firmaya karşı olan tutumunu ifade etmektedir. Süreçler boyutu, firmaların müşteri ile olan ilişkilerini yönettikleri süreçlerin ölçümünü içermektedir. Altyapı boyutu ise kurum kültürü, organizasyonel uyum ve teknoloji gibi MİY çalışmalarına yardımcı olan öğeleri barındırır. Oluşturulan mantıksal ilişki haritaları, Bulanık Analitik Ağ Süreci ile sayısallaştırılmıştır. Böylece temel boyutlar arasındaki ilişkiler göz ardı edilmeden bütünsel bir MİY başarı notu elde edilmesi sağlanmıştır. Ölçüm modelinde yer alan göstergelerin bir kısmı literatürden adapte edilirken, süreçler boyutunun ölçümü ile ilgili istatistiki bir çalışma gerçekleştirilmiştir. Bu çalışma ile MİY süreçlerinin yedi temel fonksiyon ile ölçümlenebileceği belirlenmiştir. Bunlar; Müşteri Hedefleme, Müşteri Tanıma, Ürün/Hizmet Özelleştirme, Müşteri Genişletme, Tavsiye Yönetimi, Müşteri Yeniden Kazanma ve İlişki bitirme süreçleri olarak tespit edilmiştir. Faktör analizi ile belirtilen süreçlerin ölçümünde kullanılabilecek 31 soruluk bir ölçü oluşturulmuş ve bu ölçü kullanılarak süreçlerin firma içinde yer alan alt yapı öğeleri, müşteri tatmin ve müşteri sadakati ve ekonomik performans ile olan ilişkileri incelenmiştir. Anahtar Kelimeler: Müşteri İlişkileri Yönetimi, performans, bulanık mantık, ANP.Customer Relationship Management (CRM), has been a new paradigm for business success. As a result of the promised outcomes, a huge amount of investment is done in CRM. But the result of these investments rarely fulfills the expected outcomes. In this study a model for performance measurement of Customer Relationship Management (CRM) is proposed, in order to maintain enhance the effectiveness of CRM. In this manner, a measurement model is developed basing on previous Balanced Scorecard studies. The model has two major steps. In the first step a causal map is developed which describes the cause and effect relationship between the major CRM concepts. The map is developed based on the current CRM literature and is confirmed by the CRM professionals. Four perspectives are proposed to be used in the model. The CRM outputs perspective is composed of the main goals of CRM. Customer Perspective covers the attitudes of the customers towards the company as a preliminary sign of the future behaviors. The process perspective covers the process that companies use to manage the relations with the customers. The Infrastructure perspective is composed of organizational alignment, organizational culture and technology which supports the CRM implementations and customer processes. The studies in second step of the model are concerned with quantifying the performance measures. In the manner, firstly the measures that will be used in the CRM components are selected for the case study. The goal for the measures and the minimum potential values for each measure is decided. These values are utilized in normalization formulas in order to convert the observed values of the measure to measure scores. Current CRM and performance literature sheds light on the measures that can be used in model. So most of the measures allocated in the model is adapted from the current literature. On the other hand, a new scale is developed for the process perspective. The steps that are proposed by Churchill (1979) and  Devellis (2003) are followed to maintain the measurement scale. After defining the items depending on the CRM literature and confirming them with the professionals a survey is established. 167 useable survey responses are collected. Explanatory factor analysis with principle component analysis and varimax rotation is allocated for the analysis. As a result 7 major factors, that have eigen value higher than 1 are reached. As a result the customer process are found as; targeting, getting to know, customization, expending the customer, referrals management, win back and terminating the relationship. After the item purification, a measure that is composed of 31 questions is maintained. The measure is utilized in order to define the relationship between, infrastructure components, customer satisfaction and organizational performance. The results of the finding of the analysis support the proposed causal maps. At the final step, Fuzzy Analytical Network Process (F-ANP) is allocated in order to find the global performance. As the causal map defines there is dependence between the perspectives. Analytical Network Process is a method enables to model the problem as a hierarchy and take the dependence between the components. In order to find the relative weights of the components, the pair wise comparison matrixes for the perspectives, components and the dependence between the perspectives are formed. Using Buckley (1985)'s fuzzy analytical hierarchy process procedures with triangular fuzzy numbers, the weights of the perspectives and the components are calculated. Multiplying the result of the each measure with the related weight and summing the results; an overall numeric value is reached that represents the both overall and performance of each perspective. As a result the model provides the firms a framework that can be used to track the performance of the CRM implementations. The model both provides an overall performance value and the value of each perspective. Using the model, firms can define the areas that needs to be improved and tracks the effects of the current CRM activities within the company. Keywords: Customer Relationship Management, performance, customer process measurement

RISK ASSESSMENT IN ELECTRICITY MARKET INTEGRATING VALUE-AT-RISK APPROACH AND FORECASTING TECHNIQUES

Under the new regulations in Turkey, due to the electricity regulations the electricity supply chain has changed. Eligible customers have privilege to buy electricity from different suppliers. Electricity prices are determined in a dynamic market based on the consumption forecasts and production plans. The intermediaries carry a financial risk since they buy the electricity from the market at a dynamic price but sell to their customers based on a constant price. This study aims determining value at risk (VaR) calculated due to forecasting errors and compare the forecasting techniques. Forecasting methods including; ARIMA, Grey Prediction with Rolling Mechanism (GPRM), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Holt’s model are used for predicting electricity consumption of a factory and the techniques are compared based on the risk they cause regarding historical VaR. Results show ANN and SVM are the leading forecasting techniques cause a minimum VaR values

Gender prediction from classified indoor customer paths by fuzzy c-medoids clustering

International Conference on Intelligent and Fuzzy Systems, INFUS 2019 -- 23 July 2019 through 25 July 2019 -- -- 228529Customer oriented systems provides advantages to companies in competitive environment. Understanding customers is a fundamental problem to present individualized offers. Gender information, which is one of the demographic information of customers, mainly cannot be obtained by data collection technologies. Therefore, various techniques are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy set theory. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the study is to classify customer data into the gender classes using indoor paths. © 2020, Springer Nature Switzerland AG

From indoor paths to gender prediction with soft clustering

Customer-based practices enable benefits to organizations in a contentious business. Offering individualized proposals increase customer loyalty to be able to afloat. Understanding customers is a vital difficulty to perform personalized recommendations. As a demographic feature, gender information essentially cannot be captured by human tracking technologies. Hence, several procedures are improved to predict undiscovered gender information. In the research, the followed indoor paths in a shopping mall are used to predict customer genders using fuzzy c-medoids, one of the soft clustering techniques. A Levenshtein-based fuzzy classification methodology is proposed the followed paths as string data. Although some studies focused on gender prediction, no research has centered on path-oriented. The novelty of the investigation is to analyze customer path data for the gender classes.Research Fund of the Istanbul Technical UniversityIstanbul Technical University [MGA-2019-41949]This work was supported by Research Fund of the Istanbul Technical University. Project Number: MGA-2019-4194

Genders prediction from indoor customer paths by Levenshtein-based fuzzy kNN

Companies have an advantage over the competitors if they can present customized offers to customers. Demographic information of customers is critical for the companies to develop individualized systems. While current technologies make it easy to collect customer data, the main problem is that demographic data are usually incomplete. Hence, several methods are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy sets. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the research is to classify customer data into the gender classes using indoor paths. (C) 2019 Elsevier Ltd. All rights reserved

Supply chain management under fuzziness: recent developments and techniques

Supply Chain Management Under Fuzziness presents recently developed fuzzy models and techniques for supply chain management. These include: fuzzy PROMETHEE, fuzzy AHP, fuzzy ANP, fuzzy VIKOR, fuzzy DEMATEL, fuzzy clustering, fuzzy linear programming, and fuzzy inference systems. The book covers both practical applications and new developments concerning these methods. This book offers an excellent resource for researchers and practitioners in supply chain management and logistics, and will provide them with new suggestions and directions for future research. Moreover, it will support graduate students in their university courses, such as specialized courses on supply chains and logistics, as well as related courses in the fields of industrial engineering, engineering management and business administration

A Fuzzy Inference System for Supply Chain Risk Management

Risk management is the identification, assessment, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability and/or impact of unfortunate events. In the last decade risk management has become a vital part of supply chain management. The risk sources of supply chain are identified in five areas namely: transport/distribution, manufacturing, order cycle, warehousing, and procurement. The aim of the study is to build a supply chain risk measurement system using Fuzzy Inference Systems (FIS)

A Fuzzy Inference System for Supply Chain Risk Management

Risk management is the identification, assessment, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability and/or impact of unfortunate events. In the last decade risk management has become a vital part of supply chain management. The risk sources of supply chain are identified in five areas namely: transport/distribution, manufacturing, order cycle, warehousing, and procurement. The aim of the study is to build a supply chain risk measurement system using Fuzzy Inference Systems (FIS)