Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models

Nowadays, video game developers record every virtual action performed by their players. As each player can remain in the game for years, this results in an exceptionally rich dataset that can be used to understand and predict player behavior. In particular, this information may serve to identify the most valuable players and foresee the amount of money they will spend in in-app purchases during their lifetime. This is crucial in free-to-play games, where up to 50% of the revenue is generated by just around 2% of the players, the so-called whales. To address this challenge, we explore how deep neural networks can be used to predict customer lifetime value in video games, and compare their performance to parametric models such as Pareto/NBD. Our results suggest that convolutional neural network structures are the most efficient in predicting the economic value of individual players. They not only perform better in terms of accuracy, but also scale to big data and significantly reduce computational time, as they can work directly with raw sequential data and thus do not require any feature engineering process. This becomes important when datasets are very large, as is often the case with video game logs. Moreover, convolutional neural networks are particularly well suited to identify potential whales. Such an early identification is of paramount importance for business purposes, as it would allow developers to implement in-game actions aimed at retaining big spenders and maximizing their lifetime, which would ultimately translate into increased revenue

From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning

Retaining premium players is key to the success of free-to-play games, but most of them do not start purchasing right after joining the game. By exploiting the exceptionally rich datasets recorded by modern video games--which provide information on the individual behavior of each and every player--survival analysis techniques can be used to predict what players are more likely to become paying (or even premium) users and when, both in terms of time and game level, the conversion will take place. Here we show that a traditional semi-parametric model (Cox regression), a random survival forest (RSF) technique and a method based on conditional inference survival ensembles all yield very promising results. However, the last approach has the advantage of being able to correct the inherent bias in RSF models by dividing the procedure into two steps: first selecting the best predictor to perform the splitting and then the best split point for that covariate. The proposed conditional inference survival ensembles method could be readily used in operational environments for early identification of premium players and the parts of the game that may prompt them to become paying users. Such knowledge would allow developers to induce their conversion and, more generally, to better understand the needs of their players and provide them with a personalized experience, thereby increasing their engagement and paving the way to higher monetization.Comment: social games, conversion prediction, ensemble methods, survival analysis, online games, user behavio

Progame:event-based machine learning approach for in-game marketing

Abstract. There’s been a significant growth in the gaming industry, which has lead to an increased number of collected player and usage data, including game events, player interactions, the connections between players and individual preferences. Such big data has many use cases such as the identification of gaming bottlenecks, detection and prediction of anomalies and suspicious usage patterns for security, and real time offer specification via fine-grained user profiling based on their interest profiles. Offering personalized offer timing could reduce product cannibalization, and ethical methods increase the trust of customers. The goal of this thesis is to predict the value and time of the next in-game purchase in a mobile game. Using data aggregation, event-based purchase data, daily in-game behaviour metrics and session data are combined into a single data table, from which samples of 50 000 data points are taken. The features are analyzed for linear correlation with the labels, and their combinations are used as input for three machine learning algorithms: Random Forest, Support Vector Machine and Multi-Layer Perceptron. Both purchase value and purchase time are correlated with features related to previous purchase behaviour. Multi-Layer Perceptron showed the lowest error in predicting both labels, showing an improvement of 22,0% for value in USD and 20,7% for days until purchase compared to a trivial baseline predictor. For ethical customer behaviour prediction, sharing of research knowledge and customer involvement in the data analysis process is suggested to build awareness.Progame : tapahtumapohjainen koneoppimisjärjestelmä pelinsisäiseen markkinointiin. Tiivistelmä. Peliteollisuuden kasvu on johtanut kerättävän pelaaja- ja käyttödatan määrään nousuun, koostuen mm. pelitapahtumista, interaktiodatasta, pelaajien välisistä yhteyksistä ja henkilökohtaisista mieltymyksistä. Tällaisella massadatalla on monia käyttötarkoituksia kuten tietoliikenteen teknisten rajoitusten tunnistaminen pelikäytössä, käyttäjien tavallisuudesta poikkeavan käytöksen tunnistaminen ja ennustaminen tietoturvatarkoituksiin, sekä reaaliaikainen tarjousten määrittäminen hienovaraisella käyttäjien mieltymysten profiloinnilla. Ostotarjousten henkilökohtaistaminen voi vähentää uusien tuotteiden aiheuttamaa vanhojen tuotteiden myynnin laskua, ja eettiset menetelmät parantavat asiakkaiden luottamusta. Tässä työssä ennustetaan asiakkaan seuraavan pelinsisäisen oston arvoa ja aikaa mobiilipelissä. Tapahtumapohjainen ostodata, päivittäiset pelin sisäiset metriikat ja sessiodata yhdistetään yhdeksi datataulukoksi, josta otetaan kerrallaan 50 000:n datarivin näytteitä. Jokaisen selittävän muuttujan lineaarinen korrelaatio ennustettavan muuttujan kanssa analysoidaan, ja niiden yhdistelmiä käytetään syötteenä kolmelle eri koneoppimismallille: satunnainen metsä (Random Forest), tukivektorikone (Support Vector Machine) ja monikerroksinen perseptroniverkko (Multi-Layer Perceptron). Tutkimuksessa havaittiin, että sekä tulevan oston arvo että ajankohta korreloivat aiemman ostokäyttäytymisen kanssa. Monikerroksisella perseptroniverkolla oli pienin virhe molemmille ennustettaville muuttujille, ja verrattuna triviaaliin vertailuennustimeen, se vähensi virhettä 22,0% arvon ennustamisessa ja 20,7% seuraavaan ostoon jäljellä olevien päivien ennustamisessa. Eettisen asiakkaiden käyttäytymisen ennustamisen varmistamiseksi ja tietoisuuden lisäämiseksi ehdotetaan tutkimustiedon jakamista ja asiakkaan ottamista mukaan analyysin tekemiseen

The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition

Machine learning competitions such as those organized by Kaggle or KDD represent a useful benchmark for data science research. In this work, we present our winning solution to the Game Data Mining competition hosted at the 2017 IEEE Conference on Computational Intelligence and Games (CIG 2017). The contest consisted of two tracks, and participants (more than 250, belonging to both industry and academia) were to predict which players would stop playing the game, as well as their remaining lifetime. The data were provided by a major worldwide video game company, NCSoft, and came from their successful massively multiplayer online game Blade and Soul. Here, we describe the long short-term memory approach and conditional inference survival ensemble model that made us win both tracks of the contest, as well as the validation procedure that we followed in order to prevent overfitting. In particular, choosing a survival method able to deal with censored data was crucial to accurately predict the moment in which each player would leave the game, as censoring is inherent in churn. The selected models proved to be robust against evolving conditions---since there was a change in the business model of the game (from subscription-based to free-to-play) between the two sample datasets provided---and efficient in terms of time cost. Thanks to these features and also to their a ability to scale to large datasets, our models could be readily implemented in real business settings

A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction

Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue and this could be achieved only by understanding the customers more. Customer Lifetime Value (CLV) is the total monetary value of transactions/purchases made by a customer with the business over an intended period of time and is used as means to estimate future customer interactions. CLV finds application in a number of distinct business domains such as Banking, Insurance, Online-entertainment, Gaming, and E-Commerce. The existing distribution-based and basic (recency, frequency & monetary) based models face a limitation in terms of handling a wide variety of input features. Moreover, the more advanced Deep learning approaches could be superfluous and add an undesirable element of complexity in certain application areas. We, therefore, propose a system which is able to qualify both as effective, and comprehensive yet simple and interpretable. With that in mind, we develop a meta-learning-based stacked regression model which combines the predictions from bagging and boosting models that each is found to perform well individually. Empirical tests have been carried out on an openly available Online Retail dataset to evaluate various models and show the efficacy of the proposed approach.Comment: 11 pages, 7 figure

Using machine learning to predict customer lifetime value of players in a freemium mobile game: Effect of seasonal features

Freemium business model is currently largely used in the mobile gaming industry. The key idea of the model is that a game can be played for free, and revenue is generated through in-app purchases and advertising. However, the freemium model makes predicting the lifetime value of players, the amount of revenue they will generate, challenging as the revenue distribution is highly skewed and majority of revenue is generated by a relatively small group of spenders. Predicting lifetime value of players (LTV) is one of the hottest topics in the freemium mobile games industry. Knowing how much revenue players brings games companies competitive advantage as it allows for better user acquisition optimization and financial planning, to name a few. Freemium games have several unique characteristics that set them apart from other similar fields such as online retail and traditional games such as high amount of behavioral data and high skewness of the data as only a very small share of players spend money. This thesis has two objectives. First, different state-of-the-art machine learning models are compared to see which performs the best predicting lifetime values on a 360-day window. The models used haven been proven to be the most accurate by recent studies and include deep multilayer perceptron, random forest, gradient boosted trees as well as linear regression. The second goal of the thesis is to empirically test whether including seasonal features to the prediction dataset improves the model performance. Two different ways of using seasonal features is tested. The first approach is one-hot encoding and second applying sine and cosine transformations to make the seasonal features cyclical, representing better real-life situation. To the knowledge of the author, this is the first time these methods is used literature in freemium game setting. Results show that deep multilayer perceptron performs the best, standing apart from the other models. This suggests that there are some complex relationships in the data that simpler models cannot capture. Against expectations, including seasonal features do not improve performance of most of the models

Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study

In this paper a comparative study is presented on dynamic prediction of customer profitability over time. Customer profitability is measured by Re-cency, Frequency, and Monetary (RFM) model. A real transactional data set collected from a UK-based retail is examined for the analysis, and a monthly RFM time series for each customer of the business has been generated accord-ingly. At each time point, the customers can be segmented by using k-means clustering into high, medium, or low groups based on their RFM values. 12 dif-ferent models have been utilized to predict how a customer’s membership in terms of profitability group could evolve over time, including regression, multi-layer perception, and Naïve Bayesian models in open-loop and closed-loop modes. The experimental results have demonstrated a good, consistent and in-terpretable predictability of the RFM time series of interest