B2C E-Commerce Customer Churn Management: Churn Detection using Support Vector Machine and Personalized Retention using Hybrid Recommendations

E-Commerce industry, especially the players in Business-to-Consumer (B2C) sector is witnessing immense competition for survival - by means of trying to penetrate to the customer base of their peers and at the same time not letting their existing customers to churn. Avoiding customer attrition is critical for these firms as the cost of acquiring new customers are going high with more and more players entering into the market with huge capital investments and new penetration strategies. Identifying potential parting away customers and preventing the churn with quick retention actions is the best solution in this scenario. It is also important to understand that what the customer is trying to achieve by opting for a move out so that personalized win back strategies can be applied. E-Commerce industry always possess huge amount of customer data which include information on searches performed, transactions carried out, periodicity of purchases, reviews contributed, feedback shared, etc. for every customers they possess. Data mining and machine learning can help in analyzing this huge volume of data, understanding the customer behavior and detecting possible attrition candidates. This paper proposes a framework based on support vector machine to predict E-Commerce customer churn and a hybrid recommendation strategy to suggest personalized retention actions

Sports Participation and Value of Elite Sports in Predicting Well-Being

This work contributes to emerging literature focused on the role of physical activity on the subjective well-being of populations. Unlike the existing literature, it proposes an approach that uses algorithms to predict subjective well-being. The aims of this study were to determine the relative importance of sports participation and the perceived value of elite sports on the subjective well-being of individuals. A total of 511 participants completed an online questionnaire. The statistical analysis used several machine learning techniques, including three algorithms, Decision Tree Classifier (DTC), Random Forest Classifier (RFC), and Gradient Boosting Classifier (GBC). In the three algorithms tested, sports participation, expressed as the weekly frequency and the time spent engaging in vigorous physical activity, showed a greater importance (between 47% and 53%) in determining subjective well-being. It also highlights the effect of perceived value of elite sport on the prediction of subjective well-being. This study provides evidence for public sport policy makers/authorities and for managers of physical activity and sport development programs. The surprising effect of the perceived value of elite sport on the prediction of subjective well-being.info:eu-repo/semantics/publishedVersio

Predicting the Way and the Degree of Users’ Content Contribution in the Social Question and Answer Community

Most predictions of user behavior occur after a user has participated in the community for a while, and those who have just registered are easily overlooked because their community characteristics have not yet been revealed. However, users are easy to be lost in the early stage. Based on the theory of social capital, this paper proposes a new approach to predict the willingness, mode, and degree of content contribution of the newly registered user based on users\u27 information disclosure behavior aiming at reducing the churn rate of newly registered users. We crawled the data of 4 million users in the Zhihu community and deeply studied the relationship between the disclosure behavior of different types of information and the content contribution degree of users through statistical analysis methods and machine learning algorithms. The result shows that if a user discloses personal information, the probability of his in-depth response contribution and in-depth questioning contribution will increase correspondingly, and different types of information disclosure will lead to a different probability of an increase. Furthermore, In addition, users\u27 disclosure of different types of information will lead to differences in their preference for the way they contribute content

Os Grupos de Gamers: Segmentação de Mercado dos Jogadores de Jogos Eletrônicos

The electronic games industry is a new, dynamic, and fast-growing economic sector. However, organizations in this industry do not know the profile of their consumers. In view of this knowledge gap, the objective of this research paper is to analyze groups of electronic games consumers in the Brazilian market, in terms of their socio-demographic, behavioral, and expenditure characteristics. Using market segmentation literature and motivational variables found in games literature, this paper uses self-organizing maps and analysis of variance to segment 601 Brazilian gamers. The results demonstrate the existence of five different groups of games players and that, in order to reach each group, different strategies need to be used. The first group consists of t players who play all the time. The second has the same features as the first, but they do not have the same amount of time available to play. The third group consists of pro players. The fourth group and fifth group are the new challenge for games companies.A indústria de jogos eletrônicos é um setor econômico novo, dinâmico e de rápido crescimento. No entanto, esta não conhece o perfil de seus consumidores. O objetivo desta pesquisa é analisar grupos de consumidores de jogos eletrônicos no mercado brasileiro a partir de características sociodemográficas, comportamentais e de intenções de gastos com esses produtos. Utilizando a literatura de segmentação de mercado e as variáveismotivacionais encontradas na literatura de jogos eletrônicos, este artigo utilizou-se de self-organizing maps e ANOVA para traçar uma análise de segmentação com base em uma pesquisa com 601 jogadores de jogos eletrônicos. Os resultados mostram a existência de cinco grupos diferentes de jogadores, que precisam ser atendidos por estratégias diferentes. O primeiro grupo foi formado pelos jogadores que jogam o tempo todo. O segundotem características parecidas com o primeiro, mas com menos tempo para jogar. O terceiro grupo foi formado por jogadores profissionais. O quartoe o quinto grupo são os novos desafios para as empresas de jogos

Revisão e aplicação de métodos de aprendizado de máquina para a predição de Churn

Em um mundo onde os produtos, processos e relações estão gradativamente mais digitalizados, os algoritmos de aprendizado de máquina têmse tornado ferramentas importantes em todas as áreas das organizações. Esses algoritmos ganharam relevância dentro da elaboração da estratégia de marketing, pois a partir de dados disponíveis eles conseguem descrever e predizer comportamentos relacionados ao churn, que pode ser definido pela desistência do relacionamento com a empresa por parte do cliente. Portanto, há necessidade de o profissional de administração entender essas técnicas preditivas, bem como suas vantagens e desvantagens. Este trabalho busca, através de pesquisa bibliográfica de trinta artigos científicos em inglês e português da última década: (i) identificar os principais algoritmos de aprendizado de máquina utilizados na literatura para a predição do cancelamento de serviço por parte dos clientes; (ii) apresentar as principais métricas utilizadas para avaliar o desempenho dos modelos; (iii) ilustrar os usos destes métodos através da linguagem de programação Python. Observou-se que, para o problema de churn, é preferível o uso de métodos de fácil interpretação, sendo a regressão logística e árvores de decisão são os métodos mais utilizados. Em pesquisas onde o objetivo principal era encontrar métodos com melhor desempenho, foi observado o uso de métodos mais complexos como florestas aleatórias e redes neurais, que prometem maior desempenho ao custo de menor capacidade de interpretação e geração de “insights”. A vantagem observada em usar mais de um método para resolver problemas de classificação, é que há maneiras simples de comparar o desempenho entre modelos a partir de métricas calculadas através da matriz de confusão, como acurácia, precisão e recall. Por fim, este trabalho elucida que atualmente há inúmeros algoritmos desenvolvidos e pré-configurados na linguagem Python, logo é possível começar a aplicar com rapidez modelos complexos com conhecimento intermediário de programação. Em muitos casos, as maiores dificuldades para o pesquisador podem ser a interpretação, seleção de variáveis apropriadas para abordar o problema e escolha do melhor método preditivo

Viestinnän vaikutus asiakaskokemukseen SaaS-yrityksessä : Asiakaspoistuman ja asiakaskokemuksen hallinta proaktiivisen viestinnän keinoin

SaaS (Software as a Service) on yleistynyt ohjelmistomalli, jossa asiakkaille tarjotaan käyttöön Internet-pohjainen ohjelmisto kuukausimaksullisella hinnoittelulla. Tämän tyyppinen palvelumalli perustuu pitkälti asiakkaan itsepalveluperiaatteeseen, jolloin tukitoimintojen, kuten asiakaspalvelun ja IT-tuen tarve pitäisi olla verrattain pieni. SaaS-yritysten yksi suurimmista haasteista on asiakaspoistuman hallinta, joten onnistuneeseen asiakaskokemukseen on syytä kiinnittää huomiota asiakkuuden elinkaaren pidentämiseksi. Tutkimuksen tavoitteena oli selvittää, miten proaktiivisella viestinnällä voidaan vaikuttaa asiakaskokemukseen ja asiakkuuden elinkaareen SaaS-yrityksessä. Tutkimus on tehty yhteistyössä ohjelmistoyritys Pulse247:n kanssa. Yrityksen päätuote on heidän kehittämänsä SaaS-pohjainen verkkokauppapalvelu MyCashflow, jota yli 2000 yritystä käyttää oman verkkoliiketoimintansa pyörittämiseen. Tutkimusaineistona hyödynnettiin toimeksiantajan asiakasviestinnän tilastodataa sekä toimeksiantajan tekemän tuoreen asiakaskyselyn tuloksia. Aineistoja tutkittiin yhdistellen kvantitatiivista ja kvalitatiivista tutkimustapaa. Asiakasviestinnän tilastoista tutkittiin useimmin toistuvia asiakasyhteydenottojen aiheita sekä tarkasteltiin toimeksiantajan onboarding-vaiheessa käyttämää viestintämateriaalia. Asiakaskyselyn tuloksista analysoitiin asiakkaiden palautetta asiakaspalveluun, asiakasviestintään ja koulutuksiin liittyen. Tutkimuksen alussa hypoteesina oli, että SaaS-yrityksen runsas asiakaspalveluresurssien tarve voisi johtua ohjelmiston puutteellisesta käyttöohjeistuksesta ja asiakasviestinnästä. Oletuksena oli, että näiden osa-alueiden parantaminen sekä automatisoiminen mahdollisilta osin johtaisi parempaan asiakaskokemukseen ja sitä kautta pidempään asiakassuhteeseen. Tutkimuksessa kuitenkin havaittiin toimeksiantajan aineistojen analyysin avulla, että vaikka viestintään ja ohjeistukseen oli panostettu huolellisesti, eivät kaikki asiakkaat silti halunneet toimia itseohjautuvasti SaaS-ohjelmiston kanssa, vaan nimenomaan kaipasivat perinteistä asiakaspalvelua. Lisäksi tutkimustuloksissa oli havaittavissa myös ihmisten kuormittuminen verkossa olevasta runsaasta tiedon määrästä. Informaatiotulva on nykypäivänä niin suuri, että oikean tiedon löytäminen koetaan työlääksi, josta johtuen tietoa ei ehkä jakseta etsiä ollenkaan itse, vaan halutaan mieluummin esimerkiksi asiakaspalvelijan apua ongelmatilanteissa. Kun tietoa koetaan olevan jo ennestään liikaa, ei viestinnän lisääminen entisestään välttämättä korjaa tilannetta. Vaikka asiakkaat odottavat myös jatkuvasti nopeampaa ja yksilöidympää palvelua, tutkimustuloksista käy kuitenkin ilmi, että palveluiden automatisoiminen ja digitalisaatio eivät kuitenkaan välttämättä johda parempaan asiakaskokemukseen

Developing strategies to retain organizational insurers using a clustering technique: evidence from the insurance industry

Formulating strategies to maintain policyholders is one of the main challenges for most insurance companies in Iran. The purpose of this article is to help marketing strategists of insurance companies predict insurees' churn and develop insurees retention strategies. Since the cost of maintaining an insurance policyholder is approximately one-eighth of the cost of attracting new ones, predicting their churn can help insurance companies adopt proper strategies in advance, which will definitely lead to saving marketing costs and maintaining the insurer's portfolio. Accordingly, the main question of this research is how to classify organizational insurees with the help of the clustering technique. This research is conducted in both qualitative and quantitative phases. In the qualitative phase, by conducting a semi-structured interview (interview protocol) with 15 experts in the insurance industry, the influential factors on policyholders' churn are identified. Then, based on the factors identified in the research literature and comparing them with the interview results, eight main factors are finalized. In the quantitative phase, in order to cluster the organizational insurees, 120 samples from the Iran Insurance Company are selected, and k-means is applied for clustering. Organizational insurees are divided into two groups according to the desired indicators. Using the results of clustering, insurees are divided into four groups, and effective marketing strategies are developed for each group. According to the results, the variable “health care insurance price” has the most effective role in separating the clusters at an error level of <0.01, and on the contrary, the variable “liability insurance amount” has the least important role at an error level of <0.978