Relevant bibliographies by topics / Customer churn prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Customer churn prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Customer churn prediction"

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Xu, Tianpei, Ying Ma, and Kangchul Kim. "Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping." Applied Sciences 11, no. 11 (May 21, 2021): 4742. http://dx.doi.org/10.3390/app11114742.

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In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.
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B, Senthilnayaki, Swetha M, and Nivedha D. "CUSTOMER CHURN PREDICTION." IARJSET 8, no. 6 (June 30, 2021): 527–31. http://dx.doi.org/10.17148/iarjset.2021.8692.

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Zhao, Ming, Qingjun Zeng, Ming Chang, Qian Tong, and Jiafu Su. "A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China." Discrete Dynamics in Nature and Society 2021 (August 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/7160527.

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Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.
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Coussement, Kristof. "Improving customer retention management through cost-sensitive learning." European Journal of Marketing 48, no. 3/4 (April 8, 2014): 477–95. http://dx.doi.org/10.1108/ejm-03-2012-0180.

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Purpose – Retailers realize that customer churn detection is a critical success factor. However, no research study has taken into consideration that misclassifying a customer as a non-churner (i.e. predicting that (s)he will not leave the company, while in reality (s)he does) results in higher costs than predicting that a staying customer will churn. The aim of this paper is to examine the prediction performance of various cost-sensitive methodologies (direct minimum expected cost (DMECC), metacost, thresholding and weighting) that incorporate these different costs of misclassifying customers in predicting churn. Design/methodology/approach – Cost-sensitive methodologies are benchmarked on six real-life churn datasets from the retail industry. Findings – This article argues that total misclassification cost, as a churn prediction evaluation measure, is crucial as input for optimizing consumer decision making. The practical classification threshold of 0.5 for churn probabilities (i.e. when the churn probability is greater than 0.5, the customer is predicted as a churner, and otherwise as a non-churner) offers the worst performance. The provided managerial guidelines suggest when to use each cost-sensitive method, depending on churn levels and the cost level discrepancy between misclassifying churners versus non-churners. Practical implications – This research emphasizes the importance of cost-sensitive learning to improve customer retention management in the retail context. Originality/value – This article is the first to use the concept of misclassification costs in a churn prediction setting, and to offer recommendations about the circumstances in which marketing managers should use specific cost-sensitive methodologies.
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Mishachandar, B., and Kakelli Anil Kumar. "Predicting customer churn using targeted proactive retention." International Journal of Engineering & Technology 7, no. 2.27 (August 2, 2018): 69. http://dx.doi.org/10.14419/ijet.v7i2.27.10180.

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With the advent of innovative technologies and fierce competition, the choices for customers to choose from have increased tremendously in number. Especially in the case of a telecommunication industry, where deregulation is at its peak. Every year a new company springs up offering fitter options for its customers. This has turned the concentration of the business doers on churn prediction and business management models to sustain their places. Businesses approach churn in two ways, one is through targeted customer retention and through cause identification strategy. The literature of this paper provides a comprehensible understanding of the so far employed techniques in predicting customer churn. From that, it is quite evident that less attention has been given to the accuracy and the intuitiveness of churn models developed. Therefore, a novel approach of combining the models of Machine Learning and Big Data Analytics tools was proposed to deal churn prediction effectively. The purpose of this proposed work is to apply a novel retention technique called the targeted proactive retention to predict customer churning behavior in advance and help in their retention. This proposed work will help telecom companies to comprehend the risk associated with customer churn by predicting the possibility and the time of occurrence.
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Rodan, Ali, Ayham Fayyoumi, Hossam Faris, Jamal Alsakran, and Omar Al-Kadi. "Negative Correlation Learning for Customer Churn Prediction: A Comparison Study." Scientific World Journal 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/473283.

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Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.
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Manasa, Morla. "Telecom Customer Churn Prediction." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2857–62. http://dx.doi.org/10.22214/ijraset.2020.5479.

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D., Jainam, Fenil D., and Mrugendra Rahevar. "Customer Churn Prediction Analysis." International Journal of Computer Applications 182, no. 29 (November 15, 2018): 15–17. http://dx.doi.org/10.5120/ijca2018918145.

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Adesua, O., P. A. Danquah, and O. B. Longe. "A Comparative Study of Predicting Customer Churn and Lifetime." advances in multidisciplinary & scientific research journal publication 26, no. 1 (December 10, 2020): 1–6. http://dx.doi.org/10.22624/isteams/v26p1-ieee-ng-ts.

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The problem to be investigated in this research is that of predicting customers who are at risk of leaving the company, a term called churn prediction in telecommunication. The aim of this research is to predict customer churn and further focus on creating customer lifetime profiles. These profiles will allow the company to fit their customer base into n categories and make a long estimation on when a customer is potentially going to terminate their service with the company. The research then proceeds to provide a comparative analysis of neural networks and survival analysis in their capabilities of predicting customer churn and lifetime. . Key words: GSM networks, Base station, Mobile station, Signal strength, GSM service provider
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Almuqren, Latifah, Fatma S. Alrayes, and Alexandra I. Cristea. "An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach." Future Internet 13, no. 7 (July 5, 2021): 175. http://dx.doi.org/10.3390/fi13070175.

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With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company.
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Dissertations / Theses on the topic "Customer churn prediction"

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TANNEEDI, NAREN NAGA PAVAN PRITHVI. "Customer Churn Prediction Using Big Data Analytics." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13518.

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Customer churn is always a grievous issue for the Telecom industry as customers do not hesitate to leave if they don’t find what they are looking for. They certainly want competitive pricing, value for money and above all, high quality service. Customer churning is directly related to customer satisfaction. It’s a known fact that the cost of customer acquisition is far greater than cost of customer retention, that makes retention a crucial business prototype. There is no standard model which addresses the churning issues of global telecom service providers accurately. BigData analytics with Machine Learning were found to be an efficient way for identifying churn. This thesis aims to predict customer churn using Big Data analytics, namely a J48 decision tree on a Java based benchmark tool, WEKA. Three different datasets from various sources were considered; first includes Telecom operator’s six month aggregate active and churned users’ data usage volumes, second includes globally surveyed data and third dataset comprises of individual weekly data usage analysis of 22 android customers along with their average quality, annoyance and churn scores by accompanying theses. Statistical analyses and J48 Decision trees were drawn for three different datasets. From the statistics of normalized volumes, autocorrelations were small owing to reliable confidence intervals, but confidence intervals were overlapping and close by, therefore no much significance could be noticed, henceforth no strong trends could be observed. From decision tree analytics, decision trees with 52%, 70% and 95% accuracies were achieved for three different data sources respectively.      Data preprocessing, data normalization and feature selection have shown to be prominently influential. Monthly data volumes have not shown much decision power. Average Quality, Churn Risk and to some extent, Annoyance scores may point out a probable churner. Weekly data volumes with customer’s recent history and necessary attributes like age, gender, tenure, bill, contract, data plan, etc., are pivotal for churn prediction.
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Hadden, John. "A customer profiling methodology for churn prediction." Thesis, Cranfield University, 2008. http://dspace.lib.cranfield.ac.uk/handle/1826/3508.

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As markets have become increasingly saturated, companies have acknowledged that their business strategies need to focus on identifying those customers who are most likely to churn. To address this, a method is required that can identify these customers, so that proactive retention campaigns can be deployed in a bid to retain them. To further complicate this, retention campaigns can be costly. To reduce cost and maximise effectiveness, churn prediction has to be as accurate as possible to ensure that only the customers who are planning to switch their service providers are being targeted for retention. Current techniques and research as identified by literature focus primarily on the instantaneous prediction of customer churn. Much work has been invested in this method of churn prediction and significant advancement has been made. However one of the major drawbacks of current research is that the methods available do not provide adequate time for companies to identify and retain the predicted churners. There is a lack of time element in churn prediction. Current research also fails to acknowledge the expensive problem of misclassifying non-churners as churners. In addition, most research efforts base their analysis on customer demographic and usage data that can breach governing regulations. It is proposed in this research that customer complaints and repairs data could prove a suitable alternative. The doctoral research presented in this thesis aims to develop a customer profiling methodology for predicting churn in advance, while keeping the misclassification levels to a minimum. The proposed methodology incorporates time element in the prediction of customer churn for maximising future churn capture by identifying a potential loss of customer at the earliest possible point. Three case studies are identified and carried out for validating the proposed methodology using repairs and complaints data. Finally, the results from the proposed methodology are compared against popular churn prediction techniques reported in literature. The research demonstrates that customers can be placed into one of several profiles clusters according to their interactions with the service provider. Based on this, an estimate is possible regarding when the customer can be expected to terminate his/her service with the company. The proposed methodology produces better results compared to the current state-of-the-art techniques.
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Ljungehed, Jesper. "Predicting Customer Churn Using Recurrent Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210670.

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Churn prediction is used to identify customers that are becoming less loyal and is an important tool for companies that want to stay competitive in a rapidly growing market. In retail, a dynamic definition of churn is needed to identify churners correctly. Customer Lifetime Value (CLV) is the monetary value of a customer relationship. No change in CLV for a given customer indicates a decrease in loyalty. This thesis proposes a novel approach to churn prediction. The proposed model uses a Recurrent Neural Network to identify churners based on Customer Lifetime Value time series regression. The results show that the model performs better than random. This thesis also investigated the use of the K-means algorithm as a replacement to a rule-extraction algorithm. The K-means algorithm contributed to a more comprehensive analytical context regarding the churn prediction of the proposed model.
Illojalitet prediktering används för att identifiera kunder som är påväg att bli mindre lojala och är ett hjälpsamt verktyg för att ett företag ska kunna driva en konkurrenskraftig verksamhet. I detaljhandel behöves en dynamisk definition av illojalitet för att korrekt kunna identifera illojala kunder. Kundens livstidsvärde är ett mått på monetärt värde av en kundrelation. En avstannad förändring av detta värde indikerar en minskning av kundens lojalitet. Denna rapport föreslår en ny metod för att utföra illojalitet prediktering. Den föreslagna metoden består av ett återkommande neuralt nätverk som används för att identifiera illojalitet hos kunder genom att prediktera kunders livstidsvärde. Resultaten visar att den föreslagna modellen presterar bättre jämfört med slumpmässig metod. Rapporten undersöker också användningen av en k-medelvärdesalgoritm som ett substitut för en regelextraktionsalgoritm. K-medelsalgoritm bidrog till en mer omfattande analys av illojalitet predikteringen.
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Axén, Maja, and Jennifer Karlberg. "Binary Classification for Predicting Customer Churn." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171892.

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Predicting when a customer is about to turn to a competitor can be difficult, yet extremely valuable from a business perspective. The moment a customer stops being considered a customer is known as churn, a widely researched topic in several industries when dealing with subscription-services. However, in industries with non-subscription services and products, defining churn can be a daunting task and the existing literature does not fully cover this field. Therefore, this thesis can be seen as a contribution to current research, specially when not having a set definition for churn. A definition for churn, adjusted to DIAKRIT’s business, is created. DIAKRIT is a company working in the real estate industry, which faces many challenges, such as a huge seasonality. The prediction was approached as a supervised problem, where three different Machine Learning methods were used: Logistic Regression, Random Forest and Support Vector Machine. The variables used in the predictions are predominantly activity data. With a relatively high accuracy and AUC-score, Random Forest was concluded to be the most reliable model. It is however clear that the model cannot separate between the classes perfectly. It was also visible that the Random Forest model produces a relatively high precision. Thereby, it can be settled that even though the model is not flawless the customers predicted to churn are very likely to churn.
Att prediktera när en kund är påväg att vända sig till en konkurrent kan vara svårt, dock kan det visa sig extremt värdefullt ur ett affärsperspektiv. När en kund slutar vara kund benäms det ofta som kundbortfall eller ”churn”. Detta är ett ämne som är brett forskat på i flertalet olika industrier, men då ofta i situationer med prenumenationstjänster. När man inte har en prenumerationstjänst försvåras uppgiften att definera churn och existerande studier brister i att analysera detta. Denna uppsats kan därför ses som ett bidrag till nuvarande litteratur, i synnerhet i fall där ingen tydlig definition för churn existerar. En definition för churn, anpassad efter DIAKRIT och deras affärsstruktur har skapats i det här projektet. DIAKRIT är verksamma i fastighetsbranschen, en industri som har flera utmaningar, bland annat en extrem säsongsvariaton. För att genomföra prediktionerna användes tre olika maskininlärningamodeller: Logistisk Regression, Random Forest och Support Vector Machine. De variabler som användes är mestadels aktivitetsdata. Med relativt hög noggranhet och AUC-värde anses Random Forest vara mest pålitlig. Modellen kan dock inte separera mellan de två klasserna perfekt. Random Forest modellen visade sig också genera en hög precision. Därför kan slutsatsen dras att även om modellen inte är felfri verkar det som att kunderna predikterade som churn mest sannolikt kommer churna.
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MOUNIKA, REDDY CHANDIRI. "Customer Churn Predictive Heuristics from Operator and Users' Perspective." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13452.

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Telecommunication organizations are confronting in expanding client administration weight as they launch various user-desired services. Conveying poor client encounters puts client connections and incomes at danger. One of the metrics used by telecommunications companies to determine their relationship with customers is “Churn”. After substantial research in the field of churn prediction over many years, Big Data analytics with Data Mining techniques was found to be an efficient way for identifying churn. These techniques are usually applied to predict customer churn by building models, pattern classification and learning from historical data. Although some work has already been undertaken with regards to users’ perspective, it appears to be in its infancy. The aim of this thesis is to validate churn predictive heuristics from the operator perspective and close to user end. Conducting experiments with different sections of people regarding their data usage, designing a model, which is close to the user end and fitting with the data obtained through the survey done. Correlating the examined churn indicators and their validation, validation with the traffic volume variation with the users’ feedback collected by accompanying theses. A Literature review is done to analyze previous works and find out the difficulties faced in analyzing the users’ feeling, also to understand methodologies to get around problems in handling the churn prediction algorithms accuracy. Experiments are conducted with different sections of people across the globe. Their experiences with quality of calls, data and if they are looking to change in future, what would be their reasons of churn be, are analyzed. Their feedback will be validated using existing heuristics. The collected data set is analyzed by statistical analysis and validated for different datasets obtained by operators’ data. Also statistical and Big Data analysis has been done with data provided by an operator’s active and churned customers monthly data volume usage. A possible correlation of the user churn with users’ feedback will be studied by calculating the percentages and further correlate the results with that of the operators’ data and the data produced by the mobile app. The results show that the monthly volumes have not shown much decision power and the need for additional attributes such as higher time resolution, age, gender and others are needed. Whereas the survey done globally has shown similarities with the operator’s customers’ feedback and issues “around the globe” such a data plan issues, pricing, issues with connectivity and speed. Nevertheless, data preprocessing and feature selection has shown to be the key factors. Churn predictive models have given a better classification of 69.7 % when more attributes were provided. Telecom Operators’ data classification have given an accuracy of 51.7 % after preprocessing and for the variables we choose. Finally, a close observation of the end user revealed the possibility to yield a much higher classification precision of 95.2 %.
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Tryggvadottir, Valgerdur. "Customer Churn Prediction for PC Games : Probability of churn predicted for big-spenders usingsupervised machine learning." Thesis, KTH, Optimeringslära och systemteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254198.

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Paradox Interactive is a Swedish video game developer and publisher which has players all around the world. Paradox’s largest platform in terms of amount of players and revenue is the PC. The goal of this thesis was to make a churn predic-tion model to predict the probability of players churning in order to know which players to focus on in retention campaigns. Since the purpose of churn prediction is to minimize loss due to customers churning the focus was on big-spenders (whales) in Paradox PC games. In order to define which players are big-spenders the spending for players over a 12 month rolling period (from 2016-01-01 until 2018-12-31) was investigated. The players spending more than the 95th-percentile of the total spending for each pe-riod were defined as whales. Defining when a whale has churned, i.e. stopped being a big-spender in Paradox PC games, was done by looking at how many days had passed since the players bought something. A whale has churned if he has not bought anything for the past 28 days. When data had been collected about the whales the data set was prepared for a number of di˙erent supervised machine learning methods. Logistic Regression, L1 Regularized Logistic Regression, Decision Tree and Random Forest were the meth-ods tested. Random Forest performed best in terms of AUC, with AUC = 0.7162. The conclusion is that it seems to be possible to predict the probability of churning for Paradox whales. It might be possible to improve the model further by investi-gating more data and fine tuning the definition of churn.
Paradox Interactive är en svensk videospelutvecklare och utgivare som har spelare över hela världen. Paradox största plattform när det gäller antal spelare och intäk-ter är PC:n. Målet med detta exjobb var att göra en churn-predikterings modell för att förutsäga sannolikheten för att spelare har "churnat" för att veta vilka spelare fokusen ska vara på i retentionskampanjer. Eftersom syftet med churn-prediktering är att minimera förlust på grund av kunderna som "churnar", var fokusen på spelare som spenderar mest pengar (valar) i Paradox PC-spel.För att definiera vilka spelare som är valar undersöktes hur mycket spelarna spenderar under en 12 månaders rullande period (från 2016-01-01 till 2018-12-31). Spelarna som spenderade mer än 95:e percentilen av den totala spenderingen för varje period definierades som valar. För att definiera när en val har "churnat", det vill säga slutat vara en kund som spenderar mycket pengar i Paradox PC-spel, tittade man på hur många dagar som gått sedan spelarna köpte någonting. En val har "churnat" om han inte har köpt något under de senaste 28 dagarna.När data hade varit samlad om valarna var datan förberedd för ett antal olika maskininlärningsmetoder. Logistic Regression, L1 Regularized Logistic Regression, Decision Tree och Random Forest var de metoder som testades. Random Forest var den metoden som gav bäst resultat med avseende på AUC, med AUC = 0, 7162. Slutsatsen är att det verkar vara möjligt att förutsäga sannolikheten att Paradox valar "churnar". Det kan vara möjligt att förbättra modellen ytterligare genom att undersöka mer data och finjustera definitionen av churn.
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Tandan, Isabelle, and Erika Goteman. "Bank Customer Churn Prediction : A comparison between classification and evaluation methods." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-411918.

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This study aims to assess which supervised statistical learning method; random forest, logistic regression or K-nearest neighbor, that is the best at predicting banks customer churn. Additionally, the study evaluates which cross-validation set approach; k-Fold cross-validation or leave-one-out cross-validation that yields the most reliable results. Predicting customer churn has increased in popularity since new technology, regulation and changed demand has led to an increase in competition for banks. Thus, with greater reason, banks acknowledge the importance of maintaining their customer base.   The findings of this study are that unrestricted random forest model estimated using k-Fold is to prefer out of performance measurements, computational efficiency and a theoretical point of view. Albeit, k-Fold cross-validation and leave-one-out cross-validation yield similar results, k-Fold cross-validation is to prefer due to computational advantages.   For future research, methods that generate models with both good interpretability and high predictability would be beneficial. In order to combine the knowledge of which customers end their engagement as well as understanding why. Moreover, interesting future research would be to analyze at which dataset size leave-one-out cross-validation and k-Fold cross-validation yield the same results.
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Sergue, Marie. "Customer Churn Analysis and Prediction using Machine Learning for a B2B SaaS company." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-269540.

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This past decade, the majority of services have been digitalized and data more and more available, easy to store and to process in order to understand customers behaviors. In order to be leaders in their proper industries, subscription-based businesses must focus on their Customer Relationship Management and in particular churn management, that is understanding customers cancelling their subscription. In this thesis, churn analysis is performed on real life data from a Software as a Service (SaaS) company selling an advanced cloud-based business phone system, Aircall. This use case has the particularity that the available dataset gathers customers data on a monthly basis and has a very imbalanced distribution of the target: a large majority of customers do not churn. Therefore, several methods are tried in order to diminish the impact of the imbalance while remaining as close as possible to the real world and the temporal framework. These methods include oversampling and undersampling (SMOTE and Tomek's link) and time series cross-validation. Then logistic regression and random forest models are used with an aim to both predict and explain churn.The non-linear method performed better than logistic regression, suggesting the limitation of linear models for our use case. Moreover, mixing oversampling with undersampling gives better performances in terms of precision/recall trade-off. Time series cross-validation also happens to be an efficient method to improve performance of the model. Overall, the resulting model is more useful to explain churn than to predict it. It highlighted some features majorly influencing churn, mostly related to product usage.
Under det senaste decenniet har många tjänster digitaliserats och data blivit mer och mer tillgängliga, enkla att lagra och bearbeta med syftet att förstå kundbeteende. För att kunna vara ledande inom sina branscher måste prenumerationsbaserade företag fokusera på kundrelationshantering och i synnerhet churn management, det vill säga förståelse för hur kunder avbryter sin prenumeration. I denna uppsats utförs kärnanalys på verkliga data från ett SaaS-företag (software as a service) som säljer ett avancerat molnbaserat företagstelefonsystem, Aircall. Denna fallstudie är speciell på så sätt att den tillgängliga datamängden består av månatlig kunddata med en mycket ojämn fördelning: en stor majoritet av kunderna avbryter inte sina prenumerationer. Därför undersöks flera metoder för att minska effekten av denna obalans, samtidigt som de förblir så nära den verkliga världen och den tidsmässiga ramen. Dessa metoder inkluderar översampling och undersampling (SMOTE och Tomeks länk) och korsvalidering av tidsserier. Sedan används logistisk regression och random forests i syfte att både förutsäga och förklara prenumerationsbortfall. Den icke-linjära metoden presterade bättre än logistisk regression, vilket tyder på en begränsning hos linjära modeller i vårt användningsfall. Dessutom ger blandning av översampling med undersampling bättre prestanda när det gäller precision och återkoppling. Korsvalidering av tidsserier är också en effektiv metod för att förbättra modellens prestanda. Sammantaget är den resulterande modellen mer användbar för att förklara bortfall än att förutsäga dessa. Med hjälp av modellen kunde vissa faktorer, främst relaterade till produktanvändning, som påverkar bortfallet identifieras.
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Shan, Min. "Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods." Thesis, Umeå universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-142515.

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Abstract:
With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector Machine, Boosted Decision Tree and Artificial Neural Network are used and compared as learning algorithms. The experiment shows the model trained by Boosted Decision Tree delivers the best result in this project. Moreover, the discussion about the findings in the project are presented.
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Osman, Yasin, and Benjamin Ghaffari. "Customer churn prediction using machine learning : A study in the B2B subscription based service context." Thesis, Blekinge Tekniska Högskola, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21872.

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The rapid growth of technological infrastructure has changed the way companies do business. Subscription based services are one of the outcomes of the ongoing digitalization, and with more and more products and services to choose from, customer churning has become a major problem and a threat to all firms. We propose a machine learning based churn prediction model for a subscription based service provider, within the domain of financial administration in the business-to-business (B2B) context. The aim of our study is to contribute knowledge within the field of churn prediction. For the proposed model, we compare two ensemble learners, XGBoost and Random Forest, with a single base learner, Naïve Bayes. The study follows the guidelines of the design science methodology, where we used the machine learning process to iteratively build and evaluate the generated model, using the metrics, accuracy, precision, recall, and F1- score. The data has been collected from a subscription-based service provider, within the financial administration sector. Since the used dataset is imbalanced with a majority of non- churners, we evaluated three different sampling methods, that is, SMOTE, SMOTEENN and RandomUnderSampler, in order to balance the dataset. From the results of our study, we conclude that machine learning is a useful approach for prediction of customer churning. In addition, our results show that ensemble learners perform better than single base learners and that a balanced training dataset is expected to improve the performance of the classifiers.
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Book chapters on the topic "Customer churn prediction"

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Figalist, Iris, Christoph Elsner, Jan Bosch, and Helena Holmström Olsson. "Customer Churn Prediction in B2B Contexts." In Lecture Notes in Business Information Processing, 378–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33742-1_30.

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Lee, Jae Sik, and Jin Chun Lee. "Customer Churn Prediction by Hybrid Model." In Advanced Data Mining and Applications, 959–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_104.

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Seymen, Omer Faruk, Onur Dogan, and Abdulkadir Hiziroglu. "Customer Churn Prediction Using Deep Learning." In Advances in Intelligent Systems and Computing, 520–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73689-7_50.

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Huang, B. Q., M.-T. Kechadi, and B. Buckley. "Customer Churn Prediction for Broadband Internet Services." In Data Warehousing and Knowledge Discovery, 229–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03730-6_19.

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Momin, Saifil, Tanuj Bohra, and Purva Raut. "Prediction of Customer Churn Using Machine Learning." In EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, 203–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19562-5_20.

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Amin, Adnan, Faisal Rahim, Muhammad Ramzan, and Sajid Anwar. "A Prudent Based Approach for Customer Churn Prediction." In Beyond Databases, Architectures and Structures, 320–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18422-7_29.

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Jha, Nilay, Dhruv Parekh, Malek Mouhoub, and Varun Makkar. "Customer Segmentation and Churn Prediction in Online Retail." In Advances in Artificial Intelligence, 328–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_33.

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Zheng, Hanming, Ling Luo, and Goce Ristanoski. "A Clustering-Prediction Pipeline for Customer Churn Analysis." In Knowledge Science, Engineering and Management, 75–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82153-1_7.

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Karimi, Nooria, Adyasha Dash, Sidharth Swarup Rautaray, and Manjusha Pandey. "A Proposed Model for Customer Churn Prediction and Factor Identification Behind Customer Churn in Telecom Industry." In Lecture Notes in Electrical Engineering, 359–69. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7511-2_34.

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Dias, Joana, Pedro Godinho, and Pedro Torres. "Machine Learning for Customer Churn Prediction in Retail Banking." In Computational Science and Its Applications – ICCSA 2020, 576–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58808-3_42.

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Conference papers on the topic "Customer churn prediction"

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Yildiz, Mumin, and Songul Albayrak. "Customer churn prediction in telecommunication." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7129808.

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Limei Zhang. "Churn prediction in telecom using the customer churn warning." In 2012 7th International Conference on System of Systems Engineering (SoSE). IEEE, 2012. http://dx.doi.org/10.1109/sysose.2012.6333546.

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Liao, Hsiu-Yu, Kuan-Yu Chen, Duen-Ren Liu, and Yi-Ling Chiu. "Customer Churn Prediction in Virtual Worlds." In 2015 IIAI 4th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2015. http://dx.doi.org/10.1109/iiai-aai.2015.265.

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Qi, Jiayin, Yangming Zhang, Yingying Zhang, and Shuang Shi. "TreeLogit Model for Customer Churn Prediction." In 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06). IEEE, 2006. http://dx.doi.org/10.1109/apscc.2006.111.

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Yabas, Utku, Hakki Candan Cankaya, and Turker Ince. "Customer Churn Prediction for Telecom Services." In 2012 IEEE 36th Annual Computer Software and Applications Conference - COMPSAC 2012. IEEE, 2012. http://dx.doi.org/10.1109/compsac.2012.54.

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Patil, Annapurna P., M. P. Deepshika, Shantam Mittal, Savita Shetty, Samarth S. Hiremath, and Yogesh E. Patil. "Customer churn prediction for retail business." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8389557.

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Jinbo, Shao, Li Xiu, and Liu Wenhuang. "The Application ofAdaBoost in Customer Churn Prediction." In 2007 International Conference on Service Systems and Service Management. IEEE, 2007. http://dx.doi.org/10.1109/icsssm.2007.4280172.

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Cao Kang and Shao Pei-ji. "Customer Churn Prediction Based on SVM-RFE." In 2008 International Seminar on Business and Information Management (ISBIM 2008). IEEE, 2008. http://dx.doi.org/10.1109/isbim.2008.174.

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Cui, Shaoying, and Ning Ding. "Customer churn prediction using improved FCM algorithm." In 2017 3rd International Conference on Information Management (ICIM). IEEE, 2017. http://dx.doi.org/10.1109/infoman.2017.7950357.

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Qaisi, Laila M., Ali Rodan, Kefaya Qaddoum, and Rizik Al-Sayyed. "Customer churn prediction using data mining approach." In 2018 Fifth HCT Information Technology Trends (ITT). IEEE, 2018. http://dx.doi.org/10.1109/ctit.2018.8649494.

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