Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various duration variables quantifying product use: total playtime or session playtime are popular research targets, and active playtime is well-suited for subscription games. Such research often has the goal of increasing player retention or conversely decreasing player churn. Survival analysis is a framework of powerful tools well suited for retention type data. This paper contributes new methods to game analytics on how playtime can be analyzed using survival analysis without covariates. Survival and hazard estimates provide both a visual and an analytic interpretation of the playtime phenomena as a funnel type nonparametric estimate. Metrics based on the survival curve can be used to aggregate this playtime information into a single statistic. Comparison of survival curves between cohorts provides a scientific AB-test. All these methods work on censored data and enable computation of confidence intervals. This is especially important in time and sample limited data which occurs during game development. Throughout this paper, we illustrate the application of these methods to real world game development problems on the Hipster Sheep mobile game. Playtime in games 1.Why Playtime is ImportantGame analytics is becoming increasingly important in understanding player behavior [1]. Widespread adoption * M. Viljanen, A. Airola, J. Heikkonen, and T. Pahikkala are with the Department of Information Technology, University of Turku, 20014 Turku, emails: majuvi@utu.fi, ajairo@utu.fi, jukhei@utu.fi, aatapa@utu.fi of games, internet connectivity and new business models have resulted in data gathering in an unprecedented scale. With increasing availability of data, researches and industry alike are motivated to gain insight into the data through game analytics.Focal point of analytics is player retention and churn [2]. Retention has been used in connection with many related measures and methods aiming to increase the length of product use [3][4][5][6][7][8][9][10][11][12]. Better retention simply means players are engaged with the game for longer. Player churn, meaning players quitting the game either momentarily or definitely, decreases product use and is therefore a counterpart of retention. It has also been extensively researched [13][14][15][16][17][18][19][20]. Retention metrics are popular because they are thought to reflect player enjoyment, and increased product use provides increased possibilities for monetization in free-to-play and subscription based games. Game success may be attributed to the process of acquiring new users and retaining these users with effective monetization [2].Of actual measures that quantify retention in analytics, total playtime is a highly useful overall retention metric [21,22] and session playtime [23][24][25][26][27][28][29][30] can be utilized to measure in-game retention. Discrete metrics such as session count...

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The growing popularity of P2P lending has attracted more borrowers and lenders to the sector. With the growth in the popularity of P2P lending there have been many studies focusing on analyzing credit risk in P2P lending. However, the credit risk is only a part of the story. The higher interest rates are allocated to the riskier loans, and the higher interest rates may or may not in fact compensate for the defaults expected. Therefore, the profit of a loan depends on both the interest rate and the default probability. Since investors are ultimately concerned with return on investment, models should help investors to predict the profit as accurately as possible. We develop a model that predicts the expected profit of a loan using survival analysis based monthly default probability. Our approach extends previous profit scoring approaches, since it can be applied to any loan data set, including current data sets with many on-going loans.

Game analytics supports game development by providing direct quantitative feedback about player experience. Player retention and monetization in particular have become central business statistics in free-to-play game development. Many metrics have been used for this purpose. However, game developers often want to perform analytics in a timely manner before all users have churned from the game. This causes data censoring which makes many metrics biased. In this work, we introduce how the Mean Cumulative Function (MCF) can be used to generalize many academic metrics to censored data. The MCF allows us to estimate the expected value of a metric over time, which for example may be the number of game sessions, number of purchases, total playtime and lifetime value. Furthermore, the popular retention rate metric is the derivative of this estimate applied to the expected number of distinct days played. Statistical tools based on the MCF allow game developers to determine whether a given change improves a game, or whether a game is yet good enough for public release. The advantages of this approach are demonstrated on a real in-development free-to-play mobile game, the Hipster Sheep.