Customer segmentation in the telecommunications industry

Bayer, Judy

doi:10.1057/dbm.2010.21

Cited by 43 publications

(22 citation statements)

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“…I choose to model heterogeneity in this fashion not only for its flexibility (I do not impose any parametric relationship between the treatment effect and the level of RISK or LIFT, thus allowing for linear, U-shaped relationships, etc.) but also because decile split is a segmentation method commonly employed by firms (e.g., Bauer 1988; Bayer 2010). Moreover, a metric commonly used to assess the performance of a churn model is the “top-decile lift,” which implies that firms would target the top 10% of the customers in terms of risk of churn.…”

Section: Lift-based Targeting For Proactive Churn Managementmentioning

confidence: 99%

Retention Futility: Targeting High-Risk Customers Might be Ineffective

Ascarza

2018

Journal of Marketing Research

176

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Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models that identify customers at the highest risk of churning, no research has investigated whether it is indeed optimal to target those individuals. Combining two field experiments with machine learning techniques, the author demonstrates that customers identified as having the highest risk of churning are not necessarily the best targets for proactive churn programs. This finding is not only contrary to common wisdom but also suggests that retention programs are sometimes futile not because firms offer the wrong incentives but because they do not apply the right targeting rules. Accordingly, firms should focus their modeling efforts on identifying the observed heterogeneity in response to the intervention and to target customers on the basis of their sensitivity to the intervention, regardless of their risk of churning. This approach is empirically demonstrated to be significantly more effective than the standard practice of targeting customers with the highest risk of churning. More broadly, the author encourages firms and researchers using randomized trials (or A/B tests) to look beyond the average effect of interventions and leverage the observed heterogeneity in customers' response to select customer targets.

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Section: Lift-based Targeting For Proactive Churn Managementmentioning

confidence: 99%

Retention Futility: Targeting High-Risk Customers Might be Ineffective

Ascarza

2018

Journal of Marketing Research

176

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“…Marketing and sales are considered the largest domain of big data usage in telecom industry. Suitable big data analytics allows the operators to create more intelligent marketing campaigns based on customer profiling and segmentation [12], and do sales analytics to improve the sales. It can also be utilized to get better results from marketing promotions, increase revenue and implement geo marketing and real-time marketing.…”

Section: Marketing Sales and Customer Loyaltymentioning

confidence: 99%

Predicting customer’s gender and age depending on mobile phone data

2019

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IntroductionNowadays, the mobile phone is one of the fastest growing technologies in the developing world with global penetration rates reaching 90% [1]. This makes it a huge warehouse for customer's data. That is, every action taken by the customer (short message service (SMS), Call or Internet session) gets recorded within the telecom operator, in the so called (CDRs). There are many types of CDRs used mainly by telecom billing systems. CDR contains a lot of information, (type of event, who is involved in this event, datetime, cell identifier where this event has taken place). This raw data represents a valuable source for analyzing human and social behavior [2]. In the agricultural domain [3] mobile phone data is used to analyze mobility and seasonal activity patterns related to livelihood zones in Senegal, by creating mobility profiles for population and segmentation. While in energy domain [4] this data is used to analyze human activity, facilitate population growth estimation in rural areas and extrapolate electricity needs. In health sector [5,6] mobile phone data is used to study the relation between human mobility AbstractIn the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…Pengembangan pemasaran dalam mencapai tujuan bisnis seperti: bertahan hidup, memperoleh keuntungan dan berkembang (Kotler dan Armstrong, 2008) merupakan bagian terpenting dalam pengembangan bisnis perusahaan. Memahami bagaimana konsumen menanggapi tawaran perusahaan, memilih dan memutuskan produk (Bayer, 2010) yang digunakan merupakan kunci sukses dalam mencapai tujuan perusahaan. Oleh karena itu, perlu memahami bagaimana segmentasi dan pola bertransaksi konsumen agar menjadi bahan pertimbangan manajemen perusahaan dalam merencanakan strategi pemasaran (Mabrur dan Lubis, 2012).…”

Section: Pendahuluanunclassified

Pembentukan Target Pasar Berdasarkan Data Stream Transaksi Kartu Kredit (Clustering dan Association Rule) pada PT Bank Bukopin

Riza

Maulana

2018

JABM

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the purpose of research is to analyze the formation of the target market customer segmentation based on job characteristics, income, education, age, region of origin, and patterns of credit card merchant. The data were analyzed using two data mining techniques of clustering with K-Means and Association Rule Mining (ARM) and supported by Apriori and Random Sampling technique with the Slovin formula and Principal Component Analysis (PCA). The clustering tests in 10 replications on the sampling of 350 clients supported by Pca produced the three best clusters that had the silhouette value close to 1 i.e. 0.39 to 0.40. meanwhile, the arm testing with apriori using a minimum support of 1% and a minimum confidence of 40% produced two patterns of credit card merchant transactions. In the first pattern, when the Hotel merchant type (hhl) was transacted, the Restaurant merchant type (RRT) was also transacted, and in the second pattern, if the Service Station merchant type (RSS) was transacted, the Restaurant merchant type (RRT) was also transacted. The three clusters and two types of merchant patterns obtained can generate inputs for the company to identify its potential customers based on the characteristics of the target customers by connecting them to the merchant type pattern frequently used.

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Customer segmentation in the telecommunications industry

Cited by 43 publications

References 0 publications

Retention Futility: Targeting High-Risk Customers Might be Ineffective

Retention Futility: Targeting High-Risk Customers Might be Ineffective

Predicting customer’s gender and age depending on mobile phone data

Pembentukan Target Pasar Berdasarkan Data Stream Transaksi Kartu Kredit (Clustering dan Association Rule) pada PT Bank Bukopin

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