3426772
To be or not to be...social: incorporating simple social features in mobile game customer lifetime value predictions
Mobile games make up the largest segment of the games industry, in terms of revenue as well as players. Hundreds of thousands of games are available with most being free to download and play. In freemium games, revenue is predominantly generated by users making in-game purchases. As only a small fraction of users make purchases, predicting these users and their Customer Lifetime Value are key challenges in Game Analytics and currently barely explored in academic research. Furthermore, while social factors have been shown to be essential for retention in games in general, the impact on retention and monetization in mobile games is unexplored. In this paper, the problem of defining social features in freemium casual mobile games is addressed through a case study with over 200,000 players. The study evaluates the influence of specific types of social interactions typical of casual mobile games, on predictions of premium users and Customer Lifetime Value by applying classifiers and regression models respectively. Results indicate that social activity does not correlate with the tendency to become a premium user, but that social activity increases over time in a cohort.
18090444
Game Data Mining
During the years of the Information Age, technological advances in the computers, satellites, data transfer, optics, and digital storage has led to the collection of an immense mass of data on everything from business to astronomy, counting on the power of digital computing to sort through the amalgam of information and generate meaning from the data. Initially, in the 1970s and 1980s of the previous century, data were stored on disparate structures and very rapidly became overwhelming. The initial chaos led to the creation of structured databases and database management systems to assist with the management of large corpuses of data, and notably, the effective and efficient retrieval of information from databases. The rise of the database management system increased the already rapid pace of information gathering.
112786111
Freemium Economics: Leveraging Analytics and User Segmentation to Drive Revenue
Freemium Economics presents a practical, instructive approach to successfully implementing the freemium model into your software products by building analytics into product design from the earliest stages of development. Your freemium product generates vast volumes of data, but using that data to maximize conversion, boost retention, and deliver revenue can be challenging if you don't fully understand the impact that small changes can have on revenue. In this book, author Eric Seufert provides clear guidelines for using data and analytics through all stages of development to optimize your implementation of the freemium model. Freemium Economics de-mystifies the freemium model through an exploration of its core, data-oriented tenets, so that you can apply it methodically rather than hoping that conversion and revenue will naturally follow product launch. By reading Freemium Economics, you will: Learn how to apply data science and big data principles in freemium product design and development to maximize conversion, boost retention, and deliver revenue Gain a broad introduction to the conceptual economic pillars of freemium and a complete understanding of the unique approaches needed to acquire users and convert them from free to paying customers Get practical tips and analytical guidance to successfully implement the freemium model Understand the metrics and infrastructure required to measure the success of a freemium product and improve it post-launch Includes a detailed explanation of the lifetime customer value (LCV) calculation and step-by-step instructions for implementing key performance indicators in a simple, universally-accessible tool like Excel
3929162
Predicting player churn in the wild
Free-to-Play or “freemium” games represent a fundamental shift in the business models of the game industry, facilitated by the increasing use of online distribution platforms and the introduction of increasingly powerful mobile platforms. The ability of a game development company to analyze and derive insights from behavioral telemetry is crucial to the success of these games which rely on in-game purchases and in-game advertising to generate revenue, and for the company to remain competitive in a global marketplace. The ability to model, understand and predict future player behavior has a crucial value, allowing developers to obtain data-driven insights to inform design, development and marketing strategies. One of the key challenges is modeling and predicting player churn. This paper presents the first cross-game study of churn prediction in Free-to-Play games. Churn in games is discussed and thoroughly defined as a formal problem, aligning with industry standards. Furthermore, a range of features which are generic to games are defined and evaluated for their usefulness in predicting player churn, e.g. playtime, session length and session intervals. Using these behavioral features, combined with the individual retention model for each game in the dataset used, we develop a broadly applicable churn prediction model, which does not rely on game-design specific features. The presented classifiers are applied on a dataset covering five free-to-play games resulting in high accuracy churn prediction.
18895504
Hidden Markov models for churn prediction
Most companies favour the creation and nurturing of long-term relationships with customers because retaining customers is more profitable than acquiring new ones. Churn prediction is a predictive analytics technique to identify churning customers ahead of their departure and enable customer relationship managers to take action to keep them. This work evaluates the development of an expert system for churn prediction and prevention using a Hidden Markov model (HMM). A HMM is implemented on unique data from a mobile application and its predictive performance is compared to other algorithms that are commonly used for churn prediction: Logistic Regression, Neural Network and Support Vector Machine. Predictive performance of the HMM is not outperformed by the other algorithms. HMM has substantial advantages for use in expert systems though due to low storage and computational requirements and output of highly relevant customer motivational states. Generic session data of the mobile app is used to train and test the models which makes the system very easy to deploy and the findings applicable to the whole ecosystem of mobile apps distributed in Apple's App and Google's Play Store.
2868418
Churn prediction for high-value players in casual social games
Predicting when players will leave a game creates a unique opportunity to increase players' lifetime and revenue contribution. Players can be incentivized to stay, strategically cross-linked to other games in the company's portfolio or, as a last resort, be passed on to other companies through in-game advertisement. This paper focuses on predicting churn for highvalue players of casual social games and attempts to assess the business impact that can be derived from a predictive churn model. We compare the prediction performance of four common classification algorithms over two casual social games, each with millions of players. Furthermore, we implement a hidden Markov model to explicitly address temporal dynamics. We find that a neural network achieves the best prediction performance in terms of area under curve (AUC). In addition, to assess the business value of churn prediction, we design and implement an A/B test on one of the games, using free in-game currency as an incentive to retain players. Test results indicate that contacting players shortly before the predicted churn event substantially improves the effectiveness of communication with players. They further show that giving out free in-game currency does not significantly impact the churn rate or monetization of players. This suggests that players can only be retained by remarkably changing their gameplay experience ahead of the churn event and that cross-linking may be the more effective measure to deal with churning players.
3909784
Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE
In non-contractual freemium and sharing economy settings, a small share of users often drives the largest part of revenue for firms and co-finances the free provision of the product or service to a large number of users. Successfully retaining and upselling such high-value users can be crucial to firms’ survival. Predictions of customers’ Lifetime Value (LTV) are a much used tool to identify high-value users and inform marketing initiatives. This paper frames the related prediction problem and applies a number of common machine learning methods for the prediction of individual-level LTV. As only a small subset of users ever makes a purchase, data are highly imbalanced. The study therefore combines said methods with synthetic minority oversampling (SMOTE) in an attempt to achieve better prediction performance. Results indicate that data augmentation with SMOTE improves prediction performance for premium and high-value users, especially when used in combination with deep neural networks.
14736258
Predicting Purchase Decisions in Mobile Free-to-Play Games
Mobile digital games are dominantly released under the freemium business model, but only a small fraction of the players makes any purchases. The ability to predict who will make a purchase enables optimization of marketing efforts, and tailoring customer relationship management to the specific user's profile. Here this challenge is addressed via two models for predicting purchasing players, using a 100,000 player dataset: 1) A classification model focused on predicting whether a purchase will occur or not. 2) a regression model focused on predicting the number of purchases a user will make. Both models are presented within a decision and regression tree framework for building rules that are actionable by companies. To the best of our knowledge, this is the first study investigating purchase decisions in freemium mobile products from a user behavior perspective and adopting behavior-driven learning approaches to this problem.
18768968
Predicting player disengagement and first purchase with event-frequency based data representation
In the game industry, especially for free to play games, player retention and purchases are important issues. There have been several approaches investigated towards predicting them by players' behaviours during game sessions. However, most current methods are only available for specific games because the data representations utilised are usually game specific. This work intends to use frequency of game events as data representations to predict both players' disengagement from game and the decisions of their first purchases. This method is able to provide better generality because events exist in every game and no knowledge of any event but their frequency is needed. In addition, this event frequency based method will also be compared with a recent work by Runge et al. [1] in terms of disengagement prediction.
154799558
Modeling CLV: A test of competing models in the insurance industry
Customer Lifetime Value (CLV) is one of the key metrics in marketing and is considered an important segmentation base. This paper studies the capabilities of a range of models to predict CLV in the insurance industry. The simplest models can be constructed at the customer relationship level, i.e. aggregated across all services. The more complex models focus on the individual services, paying explicit attention to cross buying, but also retention. The models build on a plethora of approaches used in the existing literature and include a status quo model, a Tobit II model, univariate and multivariate choice models, and duration models. For all models, CLV for each customer is computed for a four-year time horizon. We find that the simple models perform well. The more complex models are expected to better capture the richness of relationship development. Surprisingly, this does not lead to substantially better CLV predictions.
46916448
A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy
The authors evaluate the usefulness of customer lifetime value (CLV) as a metric for customer selection and marketing resource allocation by developing a dynamic framework that enables managers to maintain or improve customer relationships proactively through marketing contacts across various channels and to maximize CLV simultaneously. The authors show that marketing contacts across various channels influence CLV nonlinearly. Customers who are selected on the basis of their lifetime value provide higher profits in future periods than do customers selected on the basis of several other customer-based metrics. The analyses suggest that there is potential for improved profits when managers design resource allocation rules that maximize CLV. Managers can use the authors’ framework to allocate marketing resources efficiently across customers and channels of communication.
10794588
Play With Me? Understanding and Measuring the Social Aspect of Casual Gaming
Social gaming is today a pervasive phenomenon. Driven by the advent of social networks and the digitization of game distribution. In this paper the impact of digitization and so-cial networks such as Facebook on digital games is de-scribed and evaluated. This impact follows several vectors, including the introduction of new game formats and extend-ing the traditional audiences for games, which in turn has increased industrial revenue. The industry is in turn shaped by new business model such as Free-to-Play, digital distri-bution and the use of viral social features. These changes do not only appear irreversible, but more importantly, play a part in shaping the future of digital game design, notably for mobile devices. The paper presents new knowledge from controlled live experiments from a casual social game across Facebook and mobile platforms, finding positive re-turns by adding social gameplay features. This suggests that not only social network games, but also casual mobile games can benefit from deeper social gameplay mechanics. Given the impact of social features on gameplay, Game An-alytics will need to evolve to be able to handle requirements that arise from the introduction of social features, e.g. how to measure engagement against social features and shaping organic and viral spreading of a game.
207224445
Socializing by Gaming
Multiplayer Online Games (MOGs) like Defense of the Ancients and StarCraft II have attracted hundreds of millions of users who communicate, interact, and socialize with each other through gaming. In MOGs, rich social relationships emerge and can be used to improve gaming services such as match recommendation and game population retention, which are important for the user experience and the commercial value of the companies who run these MOGs. In this work, we focus on understanding social relationships in MOGs. We propose a graph model that is able to capture social relationships of a variety of types and strengths. We apply our model to real-world data collected from three MOGs that contain in total over ten years of behavioral history for millions of players and matches. We compare social relationships in MOGs across different game genres and with regular online social networks like Facebook. Taking match recommendation as an example application of our model, we propose SAMRA, a Socially Aware Match Recommendation Algorithm that takes social relationships into account. We show that our model not only improves the precision of traditional link prediction approaches, but also potentially helps players enjoy games to a higher extent.
28231547
Cash Trade in Free-to-Play Online Games
The rapidly expanding ‘‘free-to-play’’ online game payment model represents a huge shift in digital game commercialization, with cash payments for virtual items increasingly recognized as central to ‘‘free game’’ participation. In this article, the authors look at implications of this trend for gameplay experiences (especially in terms of immersion, fairness, and fun) and describe a fundamental shift in player self-perceptions as consumers rather than members of a gaming community. This change is occurring at a time when the line separating game and physical worlds is becoming less distinct. The new business model entails a subtle but significant reduction in consumer rights awareness, which explains why some members of the greater gaming community are negotiating a new sense of fairness and arriving at a new consensus regarding legitimate gameplay.
30238327
Evaluating customer lifetime value for customer recommendation
It is crucial for a company to identify which are its most valuable and least profitable customers so that appropriate customer-specific marketing strategies can be developed and put in place. This paper develops a comprehensive customer lifetime value (CLV) measure model. The model not only combines the up-selling, cross-selling, and network influence opportunity, but also considers the churn risk. By regarding the market as a social network, the social influence role of a customer is explicitly modeled into the CLV evaluation. The result from the CLV model is used as a metric for customer selection. The model is empirically validated by the application to a company in the health care industry.
17021557
Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning
Major commercial (AAA) games increasingly transit to a semi-persistent or persistent format in order to extend the value of the game to the player, and to add new sources of revenue beyond basic retail sales. Given this shift in the design of AAA titles, game analytics needs to address new types of problems, notably the problem of forecasting future player behavior. This is because player retention is a key factor in driving revenue in semi-persistent titles, for example via downloadable content. This paper introduces a model for predicting retention of players in AAA games and provides a tensor-based spatio-temporal model for analyzing player trajectories in 3D games. We show how knowledge as to trajectories can help with predicting player retention. Furthermore, we describe two new algorithms for three way DEDICOM including a fast gradient method and a seminonnegative constrained method. These approaches are validated against a detailed behavioral data set from the AAA open-world game Just Cause 2.
14193683
Rapid Prediction of Player Retention in Free-to-Play Mobile Games
Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity.
16897485
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles
Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.
89141
Random Forests
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
4650265
XGBoost: A Scalable Tree Boosting System
Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.
6644398
A decision-theoretic generalization of on-line learning and an application to boosting
In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weightupdate Littlestone Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games, and prediction of points in R. In the second part of the paper we apply the multiplicative weight-update technique to derive a new boosting algorithm. This boosting algorithm does not require any prior knowledge about the performance of the weak learning algorithm. We also study generalizations of the new boosting algorithm to the problem of learning functions whose range, rather than being binary, is an arbitrary finite set or a bounded segment of the real line. ] 1997 Academic Press
5262555
C4.5: Programs for Machine Learning
From the Publisher: 
Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation. 
 
C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. 
 
This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.
1554582
SMOTE: Synthetic Minority Over-sampling Technique
An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of oversampling the minority (abnormal)cla ss and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space)tha n only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space)t han varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC)and the ROC convex hull strategy.
479953
Why do people play social network games?
Recently, Social Network Games (SNGs) over social network services have become popular and have spawned a whole new subculture. This study examines the perceived factors which contribute to an SNG user's behaviors. It proposes an SNG acceptance model based on integrating cognitive as well as affective attitudes as primary influencing factors. Results from a survey of SNG players validate that the proposed theoretical model explains and predicts user acceptance of SNG very well. The model shows fine measurement properties and establishes the perceived playfulness and security of SNGs as distinct constructs. The findings also reveal that flow plays a moderation role that affects various paths in the model. Based on the results of this study, both the appropriate practical implications for SNG marketing strategies and the theoretical implications are provided.
3025087
Behavior evolution in Tomb Raider Underworld
Behavioral datasets from major commercial game titles of the “AAA” grade generally feature high dimensionality and large sample sizes, from tens of thousands to millions, covering time scales stretching into several years of real-time, and evolving user populations. This makes dimensionality-reduction methods such as clustering and classification useful for discovering and defining patterns in player behavior. The goal from the perspective of game development is the formation of behavioral profiles that provide actionable insights into how a game is being played, and enables the detection of e.g. problems hindering player progression. Due to its unsupervised nature, clustering is notably useful in cases where no prior-defined classes exist. Previous research in this area has successfully applied clustering algorithms to behavioral datasets from different games. In this paper, the focus is on examining the behavior of 62,000 players from the major commercial game Tomb Raider: Underworld, as it unfolds from the beginning of the game and throughout the seven main levels of the game. Where previous research has focused on aggregated behavioral datasets spanning an entire game, or conversely a limited slice or snapshot viewed in isolation, this is to the best knowledge of the authors the first study to examine the application of clustering methods to player behavior as it evolves throughout an entire game.
18907499
Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks - Online E-Companion Appendix
Demonstrating compelling causal evidence of the existence and strength of peer to peer influence has become the holy grail of modern research in online social networks. In these networks, it has been consistently demonstrated that user characteristics and behavior tend to cluster both in space and in time. There are multiple well-known rival mechanisms that compete to be the explanation for this observed clustering. These range from peer influence to homophily to other unobservable external stimuli. These multiple mechanisms lead to similar observational data, yet have vastly different policy implications. In this paper, we present a novel randomized experiment that tests the existence of causal peer influence in the general population -- one that did not involve subject recruitment for experimentation -- of a particular large-scale online social network. We utilize a unique social feature to exogenously induce adoption of a paid service amongst a group of randomly selected users, and in the process develop a clean exogenous randomization of treatment and control groups. A variety of nonparametric, semiparametric and parametric approaches, ranging from resampling-based inference to ego-level random effects to logistic regression to survival models, yield close to identical, statistically and economically significant estimates of peer influence in the general population of a freemium social network. Our estimates show that peer influence causes more than a 60% increase in odds of buying the service due to the influence coming from an adopting friend. In addition, we find that users with a smaller number of friends experience stronger relative increase in the adoption likelihood due to influence from their peers as compared to the users with a larger number of friends. Our nonparametric resampling procedure based estimates are helpful in situations of networked data that violate independence assumptions. We establish that peer influence is a powerful force in getting users from free to premium levels, a known challenge in freemium communities.
40886134
Monetizing Freemium Communities: Does Paying for Premium Increase Social Engagement?
Making sustainable profits from a baseline zero price and motivating free consumers to convert to premium subscribers is a continuing challenge for all freemium communities. Prior research has causally established that social engagement (Oestreicher-Singer and Zalmanson 2013) and peer influence (Bapna and Umyarov 2015) are two important drivers of users converting to premium subscribers in such communities. In this paper, we flip the perspective of prior research and ask whether the decision to pay for a premium subscription causes users to become more socially engaged. In the context of the Last.fm music listening freemium social community, we establish, using a novel 41-month-long panel dataset, a look-ahead propensity score matching (LA-PSM) procedure coupled with a difference-in-difference estimator of the treatment effect, that payment for premium leads to more social engagement. Specifically, we find that paying for premium leads to an increase in both content-related and community-related social engagement. Free users who convert to premium listen to 287.2% more songs, create 1.92% more playlists, exhibit a 2.01% increase in the number of forum posts made, and gain 15.77% more friends. Thus, premium subscribers create value not only for themselves by consuming more content, but also for the community and site by organizing more content and adding more friends, who are subsequently engaged by the social diffusion emerging from the focal user’s activities.