How to reduce termination on freemium platforms—literature review and empirical analysis

Brüggemann, Philipp; Lehmann-Zschunke, Nina Article — Published Version How to reduce termination on freemium platforms— literature review and empirical analysis Journal of Marketing Analytics Provided in Cooperation with: Springer Nature Suggested Citation: Brüggemann, Philipp; Lehmann-Zschunke, Nina (2023) : How to reduce termination on freemium platforms—literature review and empirical analysis, Journal of Marketing Analytics, ISSN 2050-3326, Palgrave Macmillan, London, Vol. 11, Iss. 4, pp. 707-721, https://doi.org/10.1057/s41270-023-00212-y This Version is available at: https://hdl.handle.net/10419/311046 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Journal of Marketing Analytics (2023) 11:707–721 https://doi.org/10.1057/s41270-023-00212-y ORIGINAL ARTICLE How toreduce termination onfreemium platforms—literature review andempirical analysis PhilippBrüggemann1 · NinaLehmann‑Zschunke1 Revised: 24 August 2022 / Accepted: 2 February 2023 / Published online: 27 February 2023 © The Author(s) 2023 Abstract In light of increasing digitization and monetization, the freemium pricing model is being used more and more frequently. Against a backdrop of increasing competition and rising costs to attract new users, freemium platform providers need to know how to reduce termination rates. We analyze several influencing factors of termination rates using extensive data over a 5-year period from a freemium platform on sport and breeding horses. We find that shorter contract terms, increasing involvement, and increasing platform-specific content reduces termination rates. Furthermore, freemium providers should pay special attention to the interaction of video views and number of last logins. While both variables individually have no significant influence, the interaction effect positively affects termination rates. The higher both the number of video views and the number of last logins, the higher the termination rate. For freemium providers this finding leads to a dilemma. In short term (within a week), they do not know if a premium user has logged in for the last time and is willing to terminate. Still, this result is highly relevant for freemium providers. They should use our results to analyze last logins and video views ex post. In this way, they can learn about their customers’ behavior and find out how to reduce the termination rate in the future. Keywords Freemium· Online marketing· Online platform· Premium· Retention· Termination Introduction andobjectives An increasing number of companies with digital business models, such as Deezer, Dropbox, or Spotify, are built on the freemium pricing concept (Wagner etal. 2014; Mäntymäki etal. 2020). In this pricing concept, customers can choose between a free basic and a paid premium service. The latter provides users with exclusive benefits (Liu etal. 2014). Here, revenues are generated mainly through the subscriptions of premium users and advertising (Osipov etal. 2015). However, according to Gu etal. (2018), continuous and plannable revenues are in particular generated by premium users. Accordingly, for freemium platform providers, it is key to retain their premium users. Otherwise, revenue can decrease dramatically when customers terminate their premium memberships. From freemium providers’ perspective, it is crucial to analyze termination rates closely to ensure stable revenues and thus survival in the market. This is also supported by current research, as Ross (2018) found a positive correlation between customer loyalty and monetization of freemium platforms. Despite this increasing relevance of freemium pricing models in several sectors (e.g., education, data storage, data security, dating, gaming, newspaper, media streaming, messenger services, social networks) the reasons for terminating a premium membership are still unclear (Ahn etal. 2006; Wagner etal. 2014;Ascarza etal. 2018; Mäntymäki etal. 2020). On the one hand, the benefit of the subscription can omit, e.g., due to changing preferences or the end of a corresponding activity. In this case the platform providers have hardly any possibilities to influence the customers’ decision. On the other hand, dissatisfaction with the service or with the platform itself can also be a reason for termination. In the latter scenario, providers can use customer retention strategies to observe and reduce customers’ termination activities (Lee etal. 2011). The purpose of this study is twofold. On the one hand, we aim to contribute to academic research on freemium platforms. We do this by looking at different theories to explain how premium users may behave in terms of termination. In addition, we conduct a comprehensive and systematic * Philipp Brüggemann [email protected] 1 FernUniversität inHagen, Universitätsstraße 11, 58097Hagen, Germany 708 P.Brüggemann, N.Lehmann-Zschunke literature review to compile the status quo of freemium research on customer retention and prediction of churn. On the other hand, we aim to help providers of freemium platforms to analyze and increase customer loyalty. In particular, we investigate several factors that influence the termination rate. We provide meaningful new insights to freemium platform providers on how to identify and retain customers who are likely to terminate, e.g., through individual deals. As platform providers need to know how to analyze and reduce their termination rates and previous research is still limited, we formulate the following research question: What factors can be leveraged to predict and prevent terminations on freemium platforms? In the next section we provide the theoretical background for this study. We start with a theoretical background and provide an extensiveliterature review. We then dive into hypotheses development to theoretically derive influencing factors on termination rates. The empirical analysis section includes information on data and operationalization, descriptive statistics and regression results. Subsequently, we outline implications from our empirical findings. In conclusion, we point out a summary, some limitations and directions for future research. Theoretical background andliterature review Theoretical background To explain users’ behavior on freemium platforms, we use the theory of confirmation/disconfirmation paradigm. This theory states that the evaluation of satisfaction, e.g., with a service, is the result of a comparison process. Thus, the expectation is compared with the actual product performance (Oliver 1980). This theory has been supported by empirical research (Ho etal. 1998; Wirtz and Bateson 1999; Yoon and Kim 2000; Bloemer and Dekker 2007). According to Oliver (1980), positive disconfirmation increases consumer satisfaction, while negative disconfirmation reduces satisfaction. Following the paradigm of Oliver (1980), the consumer will terminate a service, e.g., a premium membership, if the disconfirmation becomes negative. Furthermore, thanks to the extensive research on vendor lock-in theory (Arthur 1989), it is known that certain circumstances, such as cognitive switching costs on websites (Shih 2012) or switching costs between online platforms (Gao etal. 2014) can increase user retention. Beyond that, so-called network effects (Katz and Shapiro 1985; Haruvy and Prasad 1998; Farrell and Klemperer 2007) can also drive lock-in effects. In addition to these theories, the prospect theory by Kahneman and Tversky (1979) can be relevant to termination. The prospect theory states that individuals are more willing to take risks when losses are imminent than when gains are possible. This theory has been used for research on freemium pricing models in different publications (e.g., Rietveld 2018; Niemand etal. 2019). Transferred to platforms using the freemium pricing model this means that users will use the free version of the platform (or not use the platform at all) as long as the expectation of the paid version is higher than the performance. In other words, they will not pay for the premium version as long as their disconfirmation is negative. To evaluate the performance of the paid version, individuals can look at the benefits of a premium subscription. If the user’s expectation is fulfilled by the premium offer (i.e. there is a positive disconfirmation), the user of the free version (or the interested individual who is not yet a user) maybecome a premium user. Considering the research on prospect theory and vendor lock-in theory, we believe that a premium user will only terminate when there is a clear negative disconfirmation. Interestingly, this may open up the possibility for freemium providers to forecast impending cancelations and take timely action. These remarks show the theoretical relevance of analyzing terminations for providers of business models based on the freemium pricing model. In addition to this theoretical background, we provide a literature review in the next section before we derive influencing factors regarding the termination in freemium business models. Literature review Over the past years, business models based on freemium pricing concepts have become more and more widespread (Harvard Business Review 2014). The term ‘freemium’ is a combination of ‘free’ and ‘premium’ and describes two different versions of a product: a free version with a limited scope of use and a paid full version (Osipov etal. 2015). According to Gu etal. (2018), a freemium business model can only become successfully established if revenue is generated from the paid premium version in particular. As a result, increasing termination rates of premium users can jeopardize the entire business model. The churn of customers can be measured by the termination of a contract or, in some cases, by the decrease in demand for a company’s products (Buckinx and Poel 2005). In the following text passages, we present and discuss the literature relevant to our work. We then delineate our research from prior research. Based on the literature on freemium business models, we found three main types of publications in terms of research objectives: (1) Implementation of free and premium strategies; (2) Conversion of basic users into premium users; (3) Customer retention and prediction of churn. In the following sections, we will explain these three research types in more detail. Since we assign our work to the third research type 709How toreduce termination onfreemium platforms—literature review andempirical analysis (customer retention and prediction of churn), we conduct an extensive literature review here. In doing so, we provide a profound differentiation of our research from previous literature. (1) Implementation of free and premium strategies There is a research body on how to implement a successful freemium pricing strategy. Here, differences in product quality between free and premium versions as well as pricing for premium versions are investigated in particular (e.g., Bourreau and Lethiais 2007; Hamari etal. 2017; Holm and Günzel-Jensen 2017; Hüttel etal. 2018; Niemand etal. 2019; Runge etal. 2022). With regard to pricing, for example, Runge etal. (2022) found that both conversions and revenues increase due to promotions. Moreover, the authors found no evidence of negative effects on quality due to price variation. An important term in the context of the pricing of freemium models is the zero-price effect (e.g., Niemand etal. 2015, 2019; Hüttel etal. 2018). This implies that the relationship between quality and price is inversed, so that the free offer has a higher perceived value. Other studies, such as Rietveld (2018), examine how freemium models compete with premium models. In terms of online games, Rietveld (2018) has shown that freemium games are played less and generate less revenue than premium games. (2) Conversion of basic users into premium users Another research strand of freemium models focusses on how basic users become premium users. Here, the authors of several publications investigate factors that influence the conversion of basic users into premium users (e.g., Pauwels and Weiss 2008; Li and Cheng 2014; Wagner etal. 2014; Mäntymäki and Islam 2015; Koch and Benlian 2017; Lee etal. 2017; Sifa etal. 2018; Hamari etal. 2020). Looking at free trial premium versions (“premium-first”), the conversion rate is significantly higher compared to starting with a free version (“free-first”). Moreover, this effect is stronger when the premium and free versions are similar (Koch and Benlian 2017). In addition, Hamari etal. (2020) found a negative relationship between enjoyment of the freemium service and the intention to purchase premium content. In contrast, the social value positively influences premium purchases. In addition, the authors find that the quality of the freemium service positively influences freemium usage. Interestingly, this does not seem to result in more premium purchases. (3) Customer retention and prediction of churn Another important area of research, to which we categorize our study, relates to the increase of customer retention and the prediction of churn. Table1 provides a comprehensive literature review of publications relevant to this type of research. In addition, we have included the present study in Table1 to classify and distinguish it from previous research. The table contains information on subject, data and method, key findings, as well as type of observed platform(s). Our literature review reveals that there are numerous publications on freemium business models using data from gaming sector (e.g., Hadiji etal. 2014; Ross 2018; Ascarza etal. 2020; Hagen etal. 2021; Karmakar etal. 2022). These are usually free-to-play games, i.e. non-contractual, freemium models. Here, revenue is generated through in-game purchases or advertising. However, subscriptions usually play a minor role in these freemium games (Karmakar etal. 2022). Unfortunately, in these studies focusing on gaming, the investigations of churn or customer loyalty refer to free to play applications. For example, Rahmansyah and Hijrah Hati (2020) differentiate between continuance intention with respect to free use and purchase intention. As some of the factors they use are quite specific to online games, they cannot be readily applied to other areas. Another example is Karmakar etal. (2022). The authors consider player cooperation and player performance achieved at each level. A contractual freemium model was studied by Mäntymäki etal. (2020). They have shown that the values of users that are decisive for the upgrade intention are completely different from those that are decisive for the retention of a premium subscription. These differences clearly reveal the importance of examining in more detail factors that influence termination. Hagen etal. (2021) also consider a freemium model with subscriptions, the premium version is purchased as an annual subscription. However, they take a different perspective than our study, looking at the impact of the premium version on engagement and retention, rather on factors that may lead to premium membership termination. Based on the literature review presented above, we conclude that there is a gap in research looking at factors that influence the termination rate of premium users on online freemium platforms. Furthermore, there is a lack of empirical analyses over a long period of time with current and comprehensive data. Moreover, there are only a few publications that deal with other areas than gaming, e.g. streaming services or sports (see Table1). We fill this research gap by identifying and empirically analyzing several factors influencing the termination of premium users of a freemium platform that provides information on (equestrian) sports. Hypotheses development andresearch model In the following sections, we derive the relevant hypotheses for this study based on the literature. To measure termination rates on freemium platforms, we consider different factors that have not yet been considered jointly in previous research. Furthermore, we differentiate from previous literature by looking at the relationship between influencing factors and termination rates per week. We deliberately do not aim at an individual level per user, but at a comprehensive measurement of the effect of 710 P.Brüggemann, N.Lehmann-Zschunke Table 1 Literature review on customer retention and prediction of churn Author(s) and year Subject Data and method Key findings Type of platform(s) Hadiji etal. (2014) Predicting player chum in freemium games 20 million play sessions from five different free-to-play games over a period of 5 months in 2013 Method: Experiment for the development of a machine learning approach to predict player churn (different classifiers, including Neural Networks, Logistic Regression, Naive Bayes and Decision Trees are compared) Behavioral features like playtime, session time and intersession time can be used when predicting churn in games The learned decision trees show that the number of sessions, the number of days, the average time between sessions and the current absence time are very important features to predict player churn Gaming Voigt and Hinz (2016) Predicting Customers’ Lifetime Value in freemium business models with initial purchase information Payment data collected from three noncontractual freemium business models (two gaming companies and a dating platform). Combined, they have purchases from about 57,000 paying customers Method: Negative binomial regression,ordinary least squares regression and logistic regression Customers have higher future lifetime values if they make a purchase early after registration, spend a significant amount on their initial purchase, and use credit cards to purchase credits The average revenue per purchase increases with the number of purchases a customer makes Gaming and Dating Ross (2018) Comparison of four retention measures (daily, timed, full, and rolling retention) and relationship between customer retention and monetization in freemium games 51 freemium mobile video games with 5.7 million individual installs, 300 million user sessions, and 46.8 million unique daily active users (data from 2016/01/17 to 2016/02/06) Method: Correlation analysis Timed retention is the strongest predictor of monetization The study shows a positive relationship between customer retention and future monetization Gaming Banerjee etal. (2020) Predicting user Activity, engagement, and churn in freemium games Player level gaming information from 38,860 gamers over a period of 60days in a mobile app game Method: Development of a constrained extremely zero inflated joint modeling framework for simultaneous analysis of player activity, engagement, and churns; it is also used to segment players based on their churn rates Chances for user activity increase on weekends and through in-app purchases; chances decrease if there was no log-in at the previous day The number of additional battles played, level progression, virtual in-app currency spent and earned affect the probability of a positive engagement at later points in time Gamers who do not visit the game often and who spend more of their virtual currencies have a high probability of dropping out at later times Promotions can help to reduce the odds of dropouts validating their usage as retention schemes Gaming 711How toreduce termination onfreemium platforms—literature review andempirical analysis Table 1 (continued) Author(s) and year Subject Data and method Key findings Type of platform(s) Ascarza etal. (2020) Customer retention and monetization in freemium games Field Experiment with a free-to-play game from 2014/06/11 until 2014/08/03; 329,999 users and 79 million gaming sessions are considered Method: Ordinary least squares regression Reducing game difficulty among customers at risk of churning increases engagement, retention and customer spending for premium services The study shows heterogeneity in the effect of reducing game difficulty, where customers who are more prone to make progress in the game exhibit stronger effects Customers who have already spent money on the game show the strongest effect on in-app purchases Gaming Mäntymäki etal. (2020) Differences between basic and premium users in terms of different values that influence basic users’ decision to upgrade and premium users’ decision to retain their subscriptions Survey data from 436 users of a leading online music service (Spotify) Method: Structural equation model The user values that drive upgrade intent are completely different from those that drive premium subscription retention The study shows that enjoyment and price value are the only predictors of the intention to upgrade The intention to retain the premium subscription is determined by the discovery of new content and ubiquity, enjoyment and price value have no effect and social connectivity has a negative effect Media Streaming Rahmansyah and Hijrah Hati (2020) Influence of customer engagement experiences between satisfaction and loyalty relationships on freemium business model; loyalty intention involves the two dimensions continuance intention and purchase intention Survey data of 274 freemium app users Method: Structural equation model Satisfaction has a positive effect on the continuance intention and a negative influence on purchase intention The greatest influence in moderating satisfaction with purchase intention has utilitarian value Social facilitation is the only dimension of customer engagement experiences that moderates continuance intention and purchase intention Gaming Hagen etal (2021) Impact of premium version adoption on user engagement and retention in a freemium business model Daily activity of 12,000 users of a freemium mobile fitness application over several months Method: Gaussian copula approach Lagged tracking behavior increases user engagement and retention Premium membership improves user engagement and retention Premium version adoption is a pivotal factor for the customer relationship management Sports 712 P.Brüggemann, N.Lehmann-Zschunke Table 1 (continued) Author(s) and year Subject Data and method Key findings Type of platform(s) Karmakar etal. 2022 Development of a joint modeling framework to predict retention in freemium games by jointly modeling players’ motivation, progression and churn Individual level-wise characteristics, social collaboration and game activities of 15,000 players in an online action roleplaying game over a 3 months timespan Method: Generalized linear mixed model framework; the parameters of the joint model are estimated using a Hamiltonian Monte Carlo algorithm Players with a male character have a greater probability of dropout than female characters Users with higher engagement values are more likely to have higher achievement and more collaboration Collaboration is negatively associated with dropout; players with high achievement have a lower chance of dropout When different levels of promotion are offered to improve collaboration, freemium models can increase customer retention and revenue Gaming Brüggemann and LehmannZschunke (2023) (this study) Empirical analysis of factors influencing the termination rate on freemium platforms Aggregated analysis per month Unique data set from 2016 to 2021 with rich data on userand platform-specific information of a freemium platform on sport and breeding horses Method: Linear regression with interaction effect Shorter contract terms, increasing involvement, and increasing platform-specific content reduce the termination rate significantly There is an interaction effect between video views and number of last logins. While both variables individually do not have the expected impact, the interaction effect positively affect the termination rate. The higher both the number of video views and the number of last logins, the higher the termination rate Sports 713How toreduce termination onfreemium platforms—literature review andempirical analysis individual influencing factors that can be used to monitor and determine termination rates of the freemium platform. For this reason, we derive hypotheses in the following text passages with a view to the effect of influencing factors on the termination rate per week. For the customer’s decision to continue a premium subscription or to subscribe to a premium version for the first time, the expected risk is important, similar to purchase decisions for physical goods (see on the relationship between expected risk and purchase decisions, e.g., Kaplan etal. 1974; Ariffin etal. 2018). With respect to the paid premium version of a freemium platform, we expect that users’ expected risk increases the longer the length of the subscription, since the total amount to be paid is generally higher even with, for example, lower monthly contributions. Various studies on subscriptions have already proven that the expected risk has a strong negative influence on the attitude toward an offer and that users expect flexibility (e.g., Bischof etal. 2020; Descloux and Rumo 2020). Against this backdrop, we expect that a short-term subscription, compared to a long-term subscription, implies a higher flexibility concerning the termination as well as a lower risk. Thus: H1 The higher the share of short-term subscriptions in a week, the lower the termination rate in this week. Another possible influencing factor in the context of freemium business models is users’ involvement (Gainsbury etal. 2016). The involvement of a user can serve as an indicator of personal interest and the perceived relevance of the content of the premium version (Niemand etal. 2015). In previous research, a positive influence of users’ involvement has been identified, for example, regarding satisfaction with the premium offering (McDonald 2010). According to Oestreicher-Singer and Lior Zalmanson (2013), user’s willingness to pay for premium services is strongly positive related to the level of participation in the community. Consequently, we expect, the higher user’s participation, the higher her or his involvement, the lower the probability of termination. Moreover, Karmakar etal. (2022) emphasize the importance of user engagement for customer loyalty in freemium models. The authors conclude that, in line with user involvement, user engagement is also an indicator of personal interest in the content of the premium version. In online games, Karmakar etal. (2022) found that changes in players’ engagement are highly significant in predicting impending terminations. Thus, we expecta positive correlation between the share of premium users with high involvement and the termination rate. Thus: H2 The higher the share of premium users with high involvement in a week, the lower the termination rate in this week. An important incentive for the paid use of a premium version is the content available in contrast to the free version (Kim and Kim 2020; Mäntymäki etal 2020). Wagner etal. (2014) found that a strong difference between the freely available content and the premium content increases the probability of conversions to premium memberships. Consequently, we expect the more new content is provided by the online platform provider the lower the termination rates. Thus: H3 The more new content is provided on the platform in a week, the lower the termination rate in this week. To benefit from a premium membership, users must actively use the content of the online platform. Previous literature analyzed the relevance of users’ activity, for example, in the context of online freemium games and music platforms (Yu etal. 2017; Bapna etal. 2018;Banerjee etal. 2020). For instance, Bapna etal. (2018) found that users who converted to a premium membership of a music platform were significantly more active. In addition, Rothmeier etal. (2021) point out the importance of activity in online games in predicting user termination. They conclude that the more active and motivated users are, the less likely they are to terminate. With this in mind, we include both the number of profile visits and the number of videos views as two relevant indicators of premium user activity. Thus: H4a The higher the number of visited profiles per premium user in a week, the lower the termination rate in this week. H4b The higher the number of video views per premium user in a week, the lower the termination rate in this week. Complementing the hypotheses about user activity, we expect that when the premium membership benefit has expired, customers will no longer log in, which increases the likelihood of termination. In line with this, Castro and Tsuzuki (2015) pointed out the high relevance of login behavior in predicting churn. In a similar framework, Rothmeier etal. (2021) also use the number of users’ logins for churn prediction. Consequently, we expect a positive relationship between the termination rate and the number of last logins per premium users in this week. We therefore expect the termination rate to increase in a week in which more households have logged in for the last time. It should be noted that when a household logs in for the last time, this does not mean that it also terminates directly. Furthermore, 714 P.Brüggemann, N.Lehmann-Zschunke it should be pointed out that the last login can, of course, only be measured ex-post. Nevertheless, we are interested in this relationship because such ex-post data can also help to reduce future termination rates. Thus: H5 The higher the share of last logins in relation to all premium users in a week, the higher the termination rate in this week. While we expect the number of video views per premium user to have a negative effect on the termination rate and the share of last logins in a week to have a positive effect, we additionally consider the interaction between these two influencing factors. From the user's perspective, it is reasonable to expect that premium users who want to terminate will make more use of their premium benefits (watching videos), log in for the last time, and then terminate. The relevance of interaction effects in customer loyalty for premium offers has been proven by Hagen etal. (2021). Against this background, we complement this insight and postulate an interaction effect between video views and last logins, specifically for termination rates on freemium platforms. We expect that the advantages of the premium membership will be used more intensively before a termination occurs. Some videos may be watched again during the last login, since this is no longer possible after termination. Thus, we expect an interaction effect between the number of video views per premium user and the share of last logins. The higher both the number of video views and the share of last logins in a week, the higher the termination rate in this week (see Fig.1). Thus: H6 The higher the number of video views per premium user and the share of last logins in a week, the higher the termination rate in this week. Based on these literature-based hypotheses we came up with the following research model (see Fig.1). To test the hypotheses, we decided to use an empirical approach for several reasons. First, the use of a regression analysis allows us to quantitatively measure relationships between the selected influencing factors and the termination rate. Second, this empirical method allows us to analyze and compare the results of influencing factors simultaneously. Third, our analysis is based on very actual data from the field. Thus, we can provide highly actual and relevant results based on comprehensive data. Fourth, the empirical analysis using a regression allows us to consider interaction effects between different influencing factors. Empirical analysis Data andoperationalization The object of this study is an online platform providing users with various information on horses for sport and breeding. This online platform belongs to a publishing house that operates throughout Europe. Information includes pedigrees of horses, results of horse shows and videos of sport and breeding events. The platform is based on a freemium concept. While there is a free basic version, only premium users get full access to videos and tournament results. The underlying data of this online platform was collected from January 2016 to March 2021. In this period, we excluded data from two weeks because in those weeks the termination rate was driven by a price increase in the previous week. For all variables, we aggregate the data to calendar weeks. This allows us to fundamentally regress the impact of the independent variables (e.g., new content, profile visits, and last logins in a week) on the termination rate. We also calculate some of the variables on a per-user basis to avoid bias from trends in our data, because over the entire observation period, the number of premium users is steadily increasing. Table2 provides an overview of the operationalization of both the dependent variable (d) and the independent variables (i). Fig. 1 Research model share of premium users with high involvement termination rate share of 6-month subscriptions H1 (-) newcontent videoviews per premium user last logins H2 (-) H3 (-) H4a (-) H6 (+) profile visits per premium user H5 (+) videoviews per premium user H4b (-) 721How toreduce termination onfreemium platforms—literature review andempirical analysis loyalty relationships on Freemium Business Model. Advances in Economics, Business and Management Research 160: 79–84. Rietveld, J. 2018. Creating and capturing value from freemium business models: A demand-side perspective. Strategic Entrepreneurship Journal 12 (2): 171–193. Ross, N. 2018. Customer retention in freemium applications. Journal of Marketing Analytics 6 (4): 127–137. Rothmeier, K., N. Pflanzl, J.A. Hüllmann, and M. Preuss. 2021. Prediction of player churn and disengagement based on user activity data of a freemium online strategy game. IEEE Transactions on Games 13 (1): 78–88. Runge, J., J. Levav, and H.S. 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Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Philipp Brüggemann is post-doctoral researcher at the chair of marketing at FernUniversität in Hagen, Germany. His research interest is particular on retailing, digital marketing, and e-commerce. He is working on research projects related to online and offline grocery shopping, changes in purchasing behavior induced by the pandemic, the adoption of digital voice assistants and augmented reality for grocery shopping, privacy concerns and technology anxiety, as well as marketing channel strategy. Nina Lehmann‑Zschunke is post-doctoral researcher at the chair of marketing at FernUniversität in Hagen, Germany. She is working in various research projects on digital marketing. Online platforms in particular are in focus. In addition to success factors for sales via intermediary platforms, she also deals with the credibility of product testers with regard to online reviews. Moreover, she conducts research in the area of freemium platforms. In this context, she looks at customer loyalty and termination behavior.