scholarly journals Estimating Customer Lifetime Value Using Machine Learning Techniques

Data Mining ◽  
2018 ◽  
Author(s):  
Sien Chen
Keyword(s):  
Machine Learning ◽  
Customer Lifetime Value ◽  
Machine Learning Techniques ◽  
Customer Lifetime ◽  
Learning Techniques ◽  
Lifetime Value

A comparative study of hybrid machine learning techniques for customer lifetime value prediction

Kybernetes ◽  
2013 ◽  
Vol 42 (3) ◽  
pp. 357-370 ◽  
Author(s):  
Chih‐Fong Tsai ◽  
Ya‐Han Hu ◽  
Chia‐Sheng Hung ◽  
Yu‐Feng Hsu
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Customer Lifetime Value ◽  
Machine Learning Techniques ◽  
Value Prediction ◽  
Customer Lifetime ◽  
Learning Techniques ◽  
Lifetime Value ◽  
Hybrid Machine

Customer Lifetime Value Measurement using Machine Learning Techniques

2014 ◽  
pp. 565-573
Author(s):  
Tarun Rathi ◽  
Vadlamani Ravi
Keyword(s):  
Machine Learning ◽  
Customer Value ◽  
Customer Lifetime Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Customer Lifetime ◽  
Learning Techniques ◽  
Lifetime Value ◽  
Additive Regression ◽  
Or Organization

Customer Lifetime Value (CLV) is an important metric in relationship marketing approaches. There have always been traditional techniques like Recency, Frequency and Monetary Value (RFM), Past Customer Value (PCV) and Share-of-Wallet (SOW) for segregation of customers into good or bad, but these are not adequate, as they only segment customers based on their past contribution. CLV on the other hand calculates the future value of a customer over his or her entire lifetime, which means it takes into account the prospect of a bad customer being good in future and hence profitable for a company or organization. In this paper, we review the various models and different techniques used in the measurement of CLV. Towards the end we make a comparison of various machine learning techniques like Classification and Regression Trees (CART), Support Vector Machines (SVM), SVM using SMO, Additive Regression, K-Star Method and Multilayer Perception (MLP) for the calculation of CLV.

Customer lifetime value prediction for gaming industry: fuzzy clustering based approach

2021 ◽  
pp. 1-10
Author(s):  
Ahmet Tezcan Tekin ◽  
Tolga Kaya ◽  
Ferhan Cebi
Keyword(s):  
Machine Learning ◽  
Fuzzy Clustering ◽  
Customer Lifetime Value ◽  
Gaming Industry ◽  
Value Prediction ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Membership Value ◽  
Different Characteristics ◽  
Ensemble Algorithms

The use of fuzzy logic in machine learning is becoming widespread. In machine learning problems, the data, which have different characteristics, are trained and predicted together. Training the model consisting of data with different characteristics can increase the rate of error in prediction. In this study, we suggest a new approach to assembling prediction with fuzzy clustering. Our approach aims to cluster the data according to their fuzzy membership value and model it with similar characteristics. This approach allows for efficient clustering of objects with more than one cluster characteristic. On the other hand, our approach will enable us to combine boosting type ensemble algorithms, which are various forms of assemblies that are widely used in machine learning due to their excellent success in the literature. We used a mobile game’s customers’ marketing and gameplay data for predicting their customer lifetime value for testing our approach. Customer lifetime value prediction for users is crucial for determining the marketing cost cap for companies. The findings reveal that using a fuzzy method to ensemble the algorithms outperforms implementing the algorithms individually.

Prediction of Customer Lifetime Value Using Machine Learning

2021 ◽  
pp. 271-278
Author(s):  
Kandula Balagangadhar Reddy ◽  
Debabrata Swain ◽  
Samiksha Shukla ◽  
Lija Jacob
Keyword(s):  
Machine Learning ◽  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Lifetime Value

scholarly journals PEMODELAN DAN PENERAPAN METODE RFM PADA ESTIMASI NILAI KONSUMEN (CUSTOMER LIFETIME VALUE) MENGGUNAKAN K-MEANS CLUSTERING MACHINE LEARNING

2021 ◽  
Vol 2020 (1) ◽  
pp. 1277-1285
Author(s):  
Bagaskoro Cahyo Laksono ◽  
Ika Yuni Wulansari
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Learning Model ◽  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Machine Learning Model ◽  
Lifetime Value

Krisis Covid-19 berdampak pada revenue perusahaan, jika perusahaan tidak meningkatkan strategi pemasaran yang tepat terhadap konsumen, akan beresiko gulung tikar karena tidak memiliki target pasar yang jelas. Disamping itu, perusahaan dapat mengembangkan bisnisnya menggunakan big data untuk mendukung decision making. Big data dalam industry e-commerce yang mencakup ukuran dan kecepatan transaksi yang tinggi dapat digunakan untuk menganalisis perilaku konsumen bahkan memprediksi nilai konsumen. Pada zaman sekarang perusahaan mulai mengembangkan ketertarikan bisnis yang berorientasi konsumen daripada berorientasi produk. Salah satu cara yang dapat digunakan untuk menentukan nilai konsumen yaitu dengan menghitung Customer Lifetime Value (CLV). Dengan mengetahui CLV di level individu, akan berguna untuk membantu pengambil keputusan untuk mengembangkan segmentasi konsumen dan alokasi sumber daya. Penting dilakukan segmentasi atau pengelompokkan konsumen yang menggambarkan kelompok loyalitas konsumen. Oleh karena itu tujuan dalam penelitian ini adalah melakukan penghitungan CLV dan segmentasi konsumen dengan menggunakan metode analisis RFM dengan K-Means Clustering Machine Learning Model. Tahapan analisis diantaranya mendefinisikan RFM Segmentation Value yang merupakan clustering yang dibangun dari angka kumulatif yang berisi penjumlahan Recency, Frequency dan Monetary Level yang dimiliki masing-masing konsumen. Kombinasi nilai level yang tercipta berkisar antara 0,1,2,3,4,5,6 yang artinya semakin tinggi nilainya maka semakin berharga konsumen tersebut. Pada akhirnya, metode segmentasi konsumen yang di bangun penulis dapat digunakan untuk optimasi strategi perusahaan untuk mendapat profit yang maksimum. Metode ini dapat diterapkan pada berbagai kasus dan perusahaan lain. Hasil penelitian ini diharapkan dapat membantu perusahaan untuk bertahan di tengah krisis akibat Covid-19.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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