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Customer Churn Prediction for PC Games: Probability of churn predicted for big-spenders usingsupervised machine learning
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Kundchurn prediktering för PC-spel : Sannolikheten av churn förutsagd för spelaresom spenderar mycket pengar med övervakad maskininlärning (Swedish)
Abstract [en]

Paradox Interactive is a Swedish video game developer and publisher which has players all around the world. Paradox’s largest platform in terms of amount of players and revenue is the PC. The goal of this thesis was to make a churn predic-tion model to predict the probability of players churning in order to know which players to focus on in retention campaigns. Since the purpose of churn prediction is to minimize loss due to customers churning the focus was on big-spenders (whales) in Paradox PC games.

In order to define which players are big-spenders the spending for players over a 12 month rolling period (from 2016-01-01 until 2018-12-31) was investigated. The players spending more than the 95th-percentile of the total spending for each pe-riod were defined as whales. Defining when a whale has churned, i.e. stopped being a big-spender in Paradox PC games, was done by looking at how many days had passed since the players bought something. A whale has churned if he has not bought anything for the past 28 days.

When data had been collected about the whales the data set was prepared for a number of di˙erent supervised machine learning methods. Logistic Regression, L1 Regularized Logistic Regression, Decision Tree and Random Forest were the meth-ods tested. Random Forest performed best in terms of AUC, with AUC = 0.7162. The conclusion is that it seems to be possible to predict the probability of churning for Paradox whales. It might be possible to improve the model further by investi-gating more data and fine tuning the definition of churn.

Abstract [en]

Paradox Interactive är en svensk videospelutvecklare och utgivare som har spelare över hela världen. Paradox största plattform när det gäller antal spelare och intäk-ter är PC:n. Målet med detta exjobb var att göra en churn-predikterings modell för att förutsäga sannolikheten för att spelare har "churnat" för att veta vilka spelare fokusen ska vara på i retentionskampanjer. Eftersom syftet med churn-prediktering är att minimera förlust på grund av kunderna som "churnar", var fokusen på spelare som spenderar mest pengar (valar) i Paradox PC-spel.För att definiera vilka spelare som är valar undersöktes hur mycket spelarna spenderar under en 12 månaders rullande period (från 2016-01-01 till 2018-12-31). Spelarna som spenderade mer än 95:e percentilen av den totala spenderingen för varje period definierades som valar. För att definiera när en val har "churnat", det vill säga slutat vara en kund som spenderar mycket pengar i Paradox PC-spel, tittade man på hur många dagar som gått sedan spelarna köpte någonting. En val har "churnat" om han inte har köpt något under de senaste 28 dagarna.När data hade varit samlad om valarna var datan förberedd för ett antal olika maskininlärningsmetoder. Logistic Regression, L1 Regularized Logistic Regression, Decision Tree och Random Forest var de metoder som testades. Random Forest var den metoden som gav bäst resultat med avseende på AUC, med AUC = 0, 7162. Slutsatsen är att det verkar vara möjligt att förutsäga sannolikheten att Paradox valar "churnar". Det kan vara möjligt att förbättra modellen ytterligare genom att undersöka mer data och finjustera definitionen av churn.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:254
Keywords [en]
Customer churn prediction, whales, data analysis, machine learning, binary classification.
Keywords [sv]
Kund churn prediktering, valar, dataanalys, maskinlärning, binär klas-sificering.
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-254198OAI: oai:DiVA.org:kth-254198DiVA, id: diva2:1328904
External cooperation
Echo State
Subject / course
Optimization and Systems Theory
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-06-24Bibliographically approved

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