Customer churn is a term used to describe customers leaving a company by no longer using their services or products. Companies should develop and target retention strategies towards customers at risk of churning, because customer acquisition is more costly than customer retention. At-risk customers can be identified using predictive machine learning. Previously, predictive churn modelling has typically been made for companies offering contractual products, where payments are made on a regular basis following a subscription or other contract. In these cases, the moment a customer churns is intuitively identified. Defining when a customer churns from a company offering non-contractual products, where the purchase occasions are sporadic, is more difficult, as the exact churn moment is both subjective and hard to identify. No studies of non-contractual customer churn have been made in the winter tourism industry, the industry in which non-contractual churn is defined and predicted in this thesis. The purpose of this thesis is to define and predict non-contractual customer churn in the winter tourism industry. The purpose is fulfilled by creating two different definitions of customer churn; one where the complexity of non-contractual churn is captured through the integration of industry knowledge and the theoretical background, and one that is based solely on the theoretical background. Five frequently used machine learning classifiers are evaluated for the prediction, revealing that our first definition of churn yields the highest AUC performance when predicting customer churn in this case. We conclude that if the definition of churn is sufficiently complex, non-contractual churn in the winter tourism industry can be predicted with a high performance using an XGBoost classifier. When data of previous reservation and purchase patterns is considered, the classifier achieves what is considered to be an excellent AUC performance at nearly 86%.

Retaining high-value and profitable customers is a major strategic objective for many companies. In mature mobile phone markets where growth has slowed, the defection of customers from one network to another has intensified and is strongly fuelled by poor Customer Experience. Trends in the service economy suggest that experience can be exploited as a means of supplying the basis of a new economic offering, ignited in part by the shift that is taking place in the analysis of people’s interaction with digital products. In this light, the research describes a strategic approach to the use of Information Systems as a means of improving Customer Experience. Using Action Research in a mobile telecommunications operator, a Customer Experience Monitoring and Action Response model (CEMAR) is developed that evaluates disparate customer data, residing across many systems, builds experience profiles and suggests appropriate contextual actions where experience is poor. The model provides value in identifying issues, understanding them in the context of the overall Customer Experience (over time) and dealing with them appropriately. The novelty of the approach is the synthesis of data analysis with an enhanced understanding of Customer Experience which is developed implicitly, in real-time and in advance of any instigation by the customer.

Customer retention is an important issue for any business, especially in mature markets such as the UK mobile market where new customers can only be acquired from competitors. Different methods and techniques have been used to investigate customer retention including statistical methods and data mining. However, due to the increasing complexity of the mobile market, the effectiveness of these techniques is questionable. This study proposes Agent-Based Modelling and Simulation (ABMS) as a novel approach to investigate customer retention. ABMS is an emerging means of simulating behaviour and examining behavioural consequences. In outline, agents represent customers and agent relationships represent processes of agent interaction. This study follows the design science paradigm to build and evaluate a generic, reusable, agent-based (CubSim) model to examine the factors affecting customer retention based on data extracted from a UK mobile operator. Based on these data, two data mining models are built to gain a better understanding of the problem domain and to identify the main limitations of data mining. This is followed by two interrelated development cycles: (1) Build the CubSim model, starting with modelling customer interaction with the market, including interaction with the service provider and other competing operators in the market; and (2) Extend the CubSim model by incorporating interaction among customers. The key contribution of this study lies in using ABMS to identify and model the key factors that affect customer retention simultaneously and jointly. In this manner, the CubSim model is better suited to account for the dynamics of customer churn behaviour in the UK mobile market than all other existing models. Another important contribution of this study is that it provides an empirical, actionable insight on customer retention. In particular, and most interestingly, the experimental results show that applying a mixed customer retention strategy targeting both high value customers and customers with a large personal network outperforms the traditional customer retention strategies, which focuses only on the customer‘s value.

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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.

Den här studien syftar till att undersöka användarbortfall på webbaserad faktureringstjänst för småföretagare. Målsättningen var att, genom kartläggning av enkäter och intervjuer med användare, ta fram åtgärdsförslag för att förhindra framtida användarbortfall från tjänsten. Företaget bakom faktureringstjänsten har inte någon precis kunskap om varför nya användare väljer att lämna tjänsten. Detta eftersom man i dagsläget inte har några tydliga rutiner för att samla in denna information. Studien finner att tidigare användare av faktureringstjänsten har övergett denna för att de ansåg att designen på systemet var lite för inkonsekvent (bildbeskrivningen av webbsidorna motsvarar inte hur webbsidorna faktiskt ser ut) samt att priset inte stämde överens med förväntningarna de hade.

Kundbortfall är en utmaning vilket flertalet organisationer står inför. Tidigare studier påvisar att digital marknadsföring kan vara en lösning för att minska kundbortfall, detta på grund av att digital marknadsföring kan bidra till ökad kundnöjdhet vilket i sin tur leder till kundlojalitet. Denna lojalitet gör att kunden stannar hos organisationen trots att de får erbjudanden från konkurrenter. Syftet med denna kvalitativa studie är att undersöka hur och varför digital marknadsföring nyttjas för att hämma kundbortfall inom telekommunikationsbolag. Valet av bransch och avgränsning baseras på att telekommunikationsbranschen påverkas kraftigt av kundbortfall. Detta på grund av den hårda konkurrensen samt att marknaden inom branschen är mättad. Leverantörer och konkurrenter erbjuder liknande produkter och tjänster vilket är en bidragande faktorer för kundbortfall inom telekommunikationsbranschen. Uppsatsens slutsats utformas från en analys av insamlad empiri från semistrukturerade intervjuer som analyseras med stöd av teoretiskt referensram, tidigare forskning samt bakgrund till ämnet. Resultatet tydliggör vikten av digital marknadsföring i kommunikationen för att möjliggöra lönsamhet i form av kundnöjdhet. Resultatet tydliggör även hur digital marknadsföring nyttjas i syfte att förstå kundbeteende samt ge stöd i utformningen av recovery strategies. / Organisations are facing a crucial challenge in terms of customer churn. Previous studies show that digital marketing can be a solution to reduce customer churn. This is because digital marketing can contribute to increased customer satisfaction, which in turn leads to customer loyalty. This loyalty makes the customer stay with the organisation despite receiving offers from competitors. The purpose of this qualitative research is to investigate how and why digital marketing is used to inhibit customer churn in telecommunications companies. The choice of industry and demarcation is based on the fact that the telecommunications industry is heavily affected by customer churn. This is due to the fierce competitive environment and the saturation of the market within the industry. Suppliers and competitors offer similar products and services, which is a contributing factor to customer churn in the telecommunications industry. The conclusion of this paper is formulated from an analysis of collected empirical data from semi-structured interviews which are analysed with the support of theoretical framework, previous research and background to the topic. The findings clarify the importance of digital marketing in communication to enable profitability through customer satisfaction. The study also clarifies how digital marketing is used to understand customer behaviour and to support the development of recovery strategies.

El presente trabajo tiene como objetivo encontrar el patrón de características del cliente Premium desertor de tarjetas de crédito, tomando como foco principal la oficina Chacarilla del banco BBVA puesto que, ayudará a identificar al cliente desertor usuario de tarjetas de crédito y, además podrá ser usada para mejorar la gestión del cliente y personalizar los productos según comportamiento. La metodología aplicada se basa en la ciencia de datos, tomando en cuenta diversos estudios de pronósticos de deserción, para luego correlacionar y analizar el conjunto de datos utilizado para este caso, que comprende 1174 datos. Así mismo, se valida las correlaciones e impacto significativo a la agencia para poder quedarnos con 217 clientes desertores, que pertenecen a una categoría premium. Así mismo, cabe mencionar que la deserción y fuga de usuarios de tarjetas de crédito incide con mayor frecuencia, a comparación de otros productos en todas las entidades financieras del Perú puesto que, las entidades bancarias ofrecen a los clientes mejores tasas y beneficios cada mes. / The purpose of this work is to find the pattern of characteristics of the Premium customer credit card defector, with the main focus of the Chacarilla office of the BBVA bank since, to identify the customer defending customer credit card user and, in addition, it can easily be to improve customer management and customize products based on behaviour. The methodology applied is based on data science, taking into account various studies of attrition analysis, to then correlate and analyse the set of data used for this case, which comprises 1174 of data. Likewise, the correlations and the significant impact on the agency are validated to be able to keep 217 defending clients, who belong to a premium category. Likewise, it is worth mentioning that the defection and leakage of credit card users affects more frequently, a comparison of other products in all financial entities of Peru since, banking entities offer customers better rates and benefits every month. / Trabajo de investigación

The current technology shift has contributed to increased consumption of media and entertainment through various mobile devices, and especially through subscription based services. Storytel is a company offering a subscription based streaming service for audio and e-books, and has grown rapidly in the last couple of years. However, when operating in a competitive market, it is of great importance to understand the behavior and demands of the customer base. It has been shown that it is more profitable to retain existing customers than to acquire new ones, which is why a large focus should be directed towards preventing customers from leaving the service, that is preventing customer churn. One way to cope with this problem is by applying statistical analysis and machine learning in order to identify patterns and customer behavior in data. In this thesis, the models logistic regression and random forest are used with an aim to both predict and explain churn in early stages of a customer's subscription. The models are tested together with the feature selection methods Elastic Net, RFE and PCA, as well as with the oversampling method SMOTE. One main finding is that the best predictive model is obtained by using random forest together with RFE, producing a prediction score of 0.2427 and a recall score of 0.7699. The other main finding is that the explanatory model is given by logistic regression together with Elastic Net, where significant regression coefficient estimates can be used to explain patterns associated with churn and give useful findings from a business perspective. / Det pågående teknologiskiftet har bidragit till en ökad konsumtion av digital media och underhållning via olika typer av mobila enheter, t.ex. smarttelefoner. Storytel är ett företag som erbjuder en prenumerationstjänst för ljud- och e-böcker och har haft en kraftig tillväxt de senaste åren. När företag befinner sig i en konkurrensutsatt marknad är det av stor vikt att förstå sig på kunders beteende samt vilka krav och önskemål kunder har på tjänsten. Det har nämligen visat sig vara mer lönsamt att behålla existerande kunder i tjänsten än hela tiden värva nya, och det är därför viktigt att se till att en befintlig kund inte avslutar sin prenumeration. Ett sätt att hantera detta är genom att använda statistisk analys och maskininlärningsmetoder för att identifiera mönster och beteenden i data. I denna uppsats används både logistisk regression och random forest med syfte att både prediktera och förklara uppsägning av tjänsten i ett tidigt stadie av en kunds prenumeration. Modellerna testas tillsammans med variabelselektionsmetoderna Elastic Net, RFE och PCA, samt tillsammans med översamplingsmetoden SMOTE. Resultatet blev att random forest tillsammans med RFE bäst predikterade uppsägning av tjänsten med 0.2427 i måttet precision och 0.7699 i måttet recall. Ett annat viktigt resultat är att den förklarande modellen ges av logistisk regression tillsammans med Elastic Net, där signifikanta estimat av regressionskoefficienterna ökar förklaringsgraden för beteenden och mönster relaterade till kunders uppsägning av tjänsten. Därmed ges användbara insikter ur ett företagsperspektiv.

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.