Customer Relationship Management and Data Mining

2016 ◽  
pp. 1362-1401
Author(s):  
Niccolò Gordini ◽  
Valerio Veglio
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Customer Value ◽  
Relationship Management ◽  
Global Market ◽  
Customer Relationship ◽  
Digital Revolution ◽  
Marketing Activities ◽  
Potential Customers

In the global market of today, Customer Relationship Management (CRM) plays a fundamental role in market-oriented companies to understand customer behaviors, achieve and maintain a long-term relationship with them, and maximize the customer value. Moreover, the digital revolution has made information easy and fairly inexpensive to capture. Thus, companies have stored a large amount of data about their current and potential customers. However, this data is often raw and meaningless. Within the CRM framework, Data Mining (DM) is a very popular tool for extracting useful information from this data and for predicting customer behaviors in order to make profitable marketing decisions. This research aims to demonstrate the classification decision tree as one of the main computational data mining models able to forecast accurate marketing performance within global organizations. Particular attention is paid to the identification of the best marketing activities to which firms should concentrate their future marketing investments. The criteria is based on the loss functions that confirm the accuracy of this model.

Customer Relationship Management and Data Mining

2015 ◽  
pp. 789-828
Author(s):  
Niccolò Gordini ◽  
Valerio Veglio
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Customer Value ◽  
Relationship Management ◽  
Global Market ◽  
Customer Relationship ◽  
Digital Revolution ◽  
Marketing Activities ◽  
Potential Customers

In the global market of today, Customer Relationship Management (CRM) plays a fundamental role in market-oriented companies to understand customer behaviors, achieve and maintain a long-term relationship with them, and maximize the customer value. Moreover, the digital revolution has made information easy and fairly inexpensive to capture. Thus, companies have stored a large amount of data about their current and potential customers. However, this data is often raw and meaningless. Within the CRM framework, Data Mining (DM) is a very popular tool for extracting useful information from this data and for predicting customer behaviors in order to make profitable marketing decisions. This research aims to demonstrate the classification decision tree as one of the main computational data mining models able to forecast accurate marketing performance within global organizations. Particular attention is paid to the identification of the best marketing activities to which firms should concentrate their future marketing investments. The criteria is based on the loss functions that confirm the accuracy of this model.

Customer Relationship Management and Data Mining

2014 ◽  
pp. 1-40 ◽  
Author(s):  
Niccolò Gordini ◽  
Valerio Veglio
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Customer Value ◽  
Relationship Management ◽  
Global Market ◽  
Customer Relationship ◽  
Digital Revolution ◽  
Marketing Activities ◽  
Potential Customers

In the global market of today, Customer Relationship Management (CRM) plays a fundamental role in market-oriented companies to understand customer behaviors, achieve and maintain a long-term relationship with them, and maximize the customer value. Moreover, the digital revolution has made information easy and fairly inexpensive to capture. Thus, companies have stored a large amount of data about their current and potential customers. However, this data is often raw and meaningless. Within the CRM framework, Data Mining (DM) is a very popular tool for extracting useful information from this data and for predicting customer behaviors in order to make profitable marketing decisions. This research aims to demonstrate the classification decision tree as one of the main computational data mining models able to forecast accurate marketing performance within global organizations. Particular attention is paid to the identification of the best marketing activities to which firms should concentrate their future marketing investments. The criteria is based on the loss functions that confirm the accuracy of this model.

Mobile Customer Relationship Management

2017 ◽  
pp. 309-321
Author(s):  
Tolga Dursun ◽  
Süleyman Çelik
Keyword(s):  
Customer Satisfaction ◽  
Communication Technology ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Marketing Activities ◽  
Management Concept

Electronic platforms provide many advantages both customers and companies due to development of communication technology. Today almost every people have smartphones and tablets. Thus mobile customer relationship management became an significant concept for generating long-term relationships and increasing customer satisfaction, retention and loyalty. In addition companies use mobile CRM to facilitate salespeople for better performance in marketing activities. M-CRM offers interactive relationships between firms and companies. In this study, we define what is customer relationship management and origins of CRM. After that we stated electronic customer relationship management concept and finally we mentioned about mobile CRM especially benefits and characteristics of it.

Enhancing the Electronic Customer Relationship Management through Data Mining

2010 ◽  
pp. 119-139
Author(s):  
M. Vignesh
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Customer Relationship Management ◽  
Business Intelligence ◽  
Relationship Management ◽  
Daily Basis ◽  
Customer Relationship ◽  
Data Driven Approach ◽  
Second Tier

According to the Moore’s law, the number of transistors per microprocessor will double in every two years. In no doubt, this exponential increase in the processing speeds would be flanked by the increasing amount of data that corporates contend on a daily basis. Hence all corporates are literally drowning in data. But definitely there exists a hiatus between the data storage and the information retrieval. One can ask an enigmatic question, how effectively a stored data can be utilised for the decision making in the long-term perspective. The answer is not yet arrived out. Hence the “Organizations are data rich, but information poor!”. If capturing and storing the relevant data is a hectic task, then analyzing and translating this data into the actionable information is the other corner stone in any information systems of a concern. This gap can be bridged or overruled by the concept of business intelligence. Business Intelligence (BI) can be simply defined in terms of data –driven approach rather than information driven which includes methods as decision support systems, online analytical processing (OLAP), statistical analysis, query and reporting, forecasting which can be primarily done by data mining. BI along with customer relationship management (CRM) software forms the second tier of a firm’s IT infrastructure. This chapter holds a bird’s eye view of the usage of datawarehousing approaches for a systematic business intelligence approach and its varied applications in view of electronic customer relationship management.

scholarly journals The implementation of Customer Relationship Management (CRM) on textile supply chain using k-means clustering in data mining

2018 ◽  
Vol 204 ◽  
pp. 04017 ◽  
Author(s):  
Anik Dwiastuti ◽  
Aisyah Larasati ◽  
Endang Prahastuti
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Customer Relationship Management ◽  
Textile Industry ◽  
Customer Value ◽  
Relationship Management ◽  
Customer Relationship ◽  
Clustering Methods ◽  
Data Set ◽  
Textile Supply Chain

Supply chain in textile industry requires an involvement of several other related industry therefore it divide into several sub-sector industry. The market dynamic and complexity of supply chain network are causing problem. This study aims to classify the market base on consumers behaviour through their preferences in textile product in East Java. Analysis of data using data mining approach. Algorithm K-means type clustering is use as clustering methods by integrating with Customer Relationship Management (CRM) concept. The simulation result of data set using five cluster depends on their variability value are Lumajang, Malang, Madura, Tulungagung, and Ponorogo. The clusters formed have the highest importance predictor in “way of purchase” and the lowest in “purchase flexibility”. The result in this study is generally indicate that consumers of textile products in East Java prioritize values in customer value compared to product quality.

Mobile Customer Relationship Management

2017 ◽  
pp. 59-72
Author(s):  
Tolga Dursun ◽  
Süleyman Çelik
Keyword(s):  
Customer Satisfaction ◽  
Communication Technology ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Marketing Activities ◽  
Management Concept

Electronic platforms provide many advantages both customers and companies due to development of communication technology. Today almost every people have smartphones and tablets. Thus mobile customer relationship management became an significant concept for generating long-term relationships and increasing customer satisfaction, retention and loyalty. In addition companies use mobile CRM to facilitate salespeople for better performance in marketing activities. M-CRM offers interactive relationships between firms and companies. In this study, we define what is customer relationship management and origins of CRM. After that we stated electronic customer relationship management concept and finally we mentioned about mobile CRM especially benefits and characteristics of it.

scholarly journals Penerapan Customer Relationship Management (Crm) Dengan Menggunakan Metode Lrfm Analysis

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Nurdin Lubis
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Customer Relationship Management ◽  
Customer Loyalty ◽  
Clustering Analysis ◽  
Customer Value ◽  
Relationship Management ◽  
Customer Relationship ◽  
Economic Community ◽  
Asean Economic Community

Masyarakat Ekonomi ASEAN (MEA) atau ASEAN Economic Community telah dibuka. hal ini tak hanya membuka peluang, namun menjadi tantangan pula bagi sektor industri, termasuk industri farmasi nasional. Peningkatan kemampuan industri farmasi nasional dapat dilakukan melalui riset dan peningkatan kemampuanPenelitian ini dilakukan untuk mengetahui informasi mengenai karakteristik pelanggan dari PT Phapros Tbk, sebuah perusahaan yang bergerak pada bidang farmasi. Data yang digunakan yakni data pelanggan dan data transaksi penjualan historikal. Data tersebut kemudian akan diolah dengan LRFM model (Length, Recency, Frequency, Monetary), dan dua tahapan clustering (two stage clustering) yaitu metode ward’s untuk mengetahui jumlah cluster terbaik dan algoritma k-means yang merupakan distance-based cluster analysis untuk melakukan proses operasional clustering. Setelah proses operasional clustering dilakukan, hasil cluster akan dipetakan dengan customer value matrix dan customer loyalty matrix untuk mengetahui karakteristik tiap segmen pelanggan.Hasil yang didapatkan dari studi kasus PT Phapros Tbk menunjukkan bahwa segmen atau grup pelanggan yang terbentuk semuanya memiliki perbedaan statistik yang signifikan, dan dapat dijelaskan dalam konteks strategi marketing. Oleh karena itu, penelitian ini berguna untuk menentukan strategi pengelolaan pelanggan tiap segmen.Key Words: Clustering Analysis, Customer Relationship Management, Data mining, LRFM model.

scholarly journals Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods

2020 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Rahma Wati Sembiring Brahmana ◽  
Fahd Agodzo Mohammed ◽  
Kankamol Chairuang
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Cluster Center ◽  
Cluster Group ◽  
Rfm Model ◽  
Silhouette Index ◽  
Marketing Activities ◽  
Potential Customers

A problem that appears in marketing activities is how to identify potential customers. Marketing activities could identify their best customer through customer segmentation by applying the concept of Data Mining and Customer Relationship Management (CRM). This paper presents the Data Mining process by combining the RFM model with K-Means, K-Medoids, and DBSCAN algorithms. This paper analyzes 334,641 transaction data and converts them to 1661 Recency, Frequency, and Monetary (RFM) data lines to identify potential customers. The K-Means, K-Medoids, and DBSCAN algorithms are very sensitive for initializing the cluster center because it is done randomly. Clustering is done by using two to six clusters. The trial process in the K-Means and K-Medoids Method is done using random centroid values ??and at DBSCAN is done using random Epsilon and Min Points, so that a cluster group is obtained that produces potential customers. Cluster validation completes using the Davies-Bouldin Index and Silhouette Index methods. The result showed that K-Means had the best level of validity than K-Medoids and DBSCAN, where the Davies-Bouldin Index yield was 0,33009058, and the Silhouette Index yield was 0,912671056. The best number of clusters produced using the Davies Bouldin Index and Silhouette Index are 2 clusters, where each K-Means, K-Medoids, and DBSCAN algorithms provide the Dormant and Golden customer classes.

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