Predicting Player Disengagement in Online Games

Xie, Hanting; Kudenko, Daniel; Devlin, Sam; Cowling, Peter I.

doi:10.1007/978-3-319-14923-3_10

Cited by 6 publications

(8 citation statements)

References 7 publications

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“…In this context, the player activities on both sides of the soft/dynamic splitting date will be compared and used as the criteria to distribute play-ers into either disengagement or non-disengagement class. Our results show this labelling method outperform previous ones (Runge et al 2014;Chawla 2005) across three commercial games with two different data representation approaches (Runge et al 2014;Xie et al 2014).…”

Section: Introductionmentioning

confidence: 62%

“…Disengagement is a similar concept for describing the churning trend of players (Xie et al 2014;2015). It also relies on specific conditions to split players into binary groups.…”

Section: Disengagement Predictionmentioning

confidence: 99%

“…The definition of event frequency based data representations was first mentioned in our previous research (Xie et al 2014) for predicting disengagement. It uses only counts of occurrences of events (e.g., win/lose a match, shoot at goal) regardless of their meanings to form the input feature space.…”

Section: Prediction With Event Frequency Based Data Representationsmentioning

confidence: 99%

“…Player engagement is a basic behaviour metric that companies would like to address. Several research publications have been done on predicting players' churn/disengagement by modelling their in-game behaviours (Mahlmann et al 2010;Runge et al 2014;Weber et al 2011;Xie et al 2014;2015). Although they have provided effective approaches…”

Section: Introductionmentioning

confidence: 99%

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Predicting Disengagement in Free-to-Play Games with Highly Biased Data

Xie

Devlin

Kudenko

2021

AIIDE

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A vital application of game data mining is to predict player behaviour trends such as disengagement, purchase, etc.. Several works have been done by quantitative methods in the last decade. Generally, predicting player behaviour trends is a classification problem where class labels of instances are decided by predefined definitions. However, as the majority of current definitions distribute players into classes only by satisfying specific conditions, a highly biased class distribution may be led to if few (or most) players can satisfy these conditions. In this work, a new definition named trend over varying dates that can create balanced class distribution will be introduced and, as an example, disengagement prediction will be used to show how the definition works. Experiments on three commercial mobile games will show how this definition can be applied to games of various genres. Finally, the performance of this definition towards predicting disengagement will be compared with another disengagement concept called churn’. Both game-specific and event frequency based data representation (introduced in previous work) will be applied to represent the datasets for predictions. Results indicate that the definition of ‘trend over varying dates’ can improve the predictive performance by balancing the class distributions in most cases.

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Section: Introductionmentioning

confidence: 62%

“…Disengagement is a similar concept for describing the churning trend of players (Xie et al 2014;2015). It also relies on specific conditions to split players into binary groups.…”

Section: Disengagement Predictionmentioning

confidence: 99%

Section: Prediction With Event Frequency Based Data Representationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Disengagement in Free-to-Play Games with Highly Biased Data

Xie

Devlin

Kudenko

2021

AIIDE

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“…The lack of interest in qualitative data reported by our informants further highlights that when the resources allocated to the analysis processes are very limited, they must be very time-efficient and the data must be easy to interpret (cf. Xie et al 2014).…”

Section: Key Findingsmentioning

confidence: 99%

How Do Small and Medium-Sized Game Companies Use Analytics? An Attention-Based View of Game Analytics

2019

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The widespread adoption of the freemium business model together with the introduction of cost-efficient analytics tools have made the use of analytics pervasive in the game industry. While big data and analytics have drawn extensive scholarly attention, the research focusing particularly on game analytics is scant and largely descriptive. Thus, there is a need for research focusing on how game companies employ analytics. In this study, we analyze data collected through a set of in-depth interviews of small and medium-sized freemium game developers. We identify four main roles of game analytics: 1) sense-making device, 2) decisionsupport system, 3) communication tool, and 4) hygiene factor. We employ the attention-based view of the firm to discuss how these roles diverge and converge in terms of organizational attention. The study advances the research on the roles and business value of analytics in the game and software industry.

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Why Do Small and Medium-Size Freemium Game Developers Use Game Analytics?

Koskenvoima

Mäntymäki

2015

Open and Big Data Management and Innovation

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Predicting Player Disengagement in Online Games

Cited by 6 publications

References 7 publications

Predicting Disengagement in Free-to-Play Games with Highly Biased Data

Predicting Disengagement in Free-to-Play Games with Highly Biased Data

How Do Small and Medium-Sized Game Companies Use Analytics? An Attention-Based View of Game Analytics

Why Do Small and Medium-Size Freemium Game Developers Use Game Analytics?

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