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

Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games

In freemium games, the revenue from a player comes from the in-app purchases made and the advertisement to which that player is exposed. The longer a player is playing the game, the higher will be the chances that he or she will generate a revenue within the game. Within this scenario, it is extremely important to be able to detect promptly when a player is about to quit playing (churn) in order to react and attempt to retain the player within the game, thus prolonging his or her game lifetime. In this article we investigate how to improve the current state-of-the-art in churn prediction by combining sequential and aggregate data using different neural network architectures. The results of the comparative analysis show that the combination of the two data types grants an improvement in the prediction accuracy over predictors based on either purely sequential or purely aggregated data

Implementation of Data Mining for Churn Prediction in Music Streaming Company Using 2020 Dataset

Customer is an important asset in a company as it is the lifeline of a company. For a company to get a new customer, it will cost a lot of money for campaigns. On the other hand, maintaining old customer tend to be cheaper than acquiring a new one. Because of that, it is important to be able to prevent the loss of customers from the products we have. Therefore, customer churn prediction is important in retaining customers. This paper discusses data mining techniques using XGBoost, Deep Neural Network, and Logistic Regression to compare the performance generated using data from a company that develops a song streaming application. The company suffers from the churn rate of the customer. Uninstall rate of the customers reaching 90% compared to the customer’s installs. The data will come from Google Analytics, a service from Google that will track the customer’s activity in the music streaming application. After finding out the method that will give the highest accuracy on the churn prediction, the attribute of data that most influence on the churn prediction will be determined

Detecting customer defections: an application of continuous duration models

The considerable increase of business competition in the Portuguese fixed telecommunications industry for the last decades has given rise to a phenomenon of customer defection, which has serious consequences for the business financial performance and, therefore, for the economy. As such, researchers have recognised the importance of an in-depth study of customer defection in different industries and geographic locations. This study aims to understand and predict customer lifetime in a contractual setting in order to improve the practice of customer portfolio management. A duration model is developed to understand and predict the residential customer defection in the fixed telecommunications industry in Portugal. The models are developed by using large-scale data from an internal database of a Portuguese company which presents bundled offers of ADSL, fixed line telephone, pay-TV and home-video. The model is estimated with a large number of covariates, which includes customer’s basic information, demographics, churn flag, customer historical information about usage, billing, subscription, credit, and other. The results of this study are very useful to the computation of the customer lifetime value

Machine learning applications for censored data

The amount of data being gathered has increased tremendously as many aspects of our lives are becoming increasingly digital. Data alone is not useful, because the ultimate goal is to use the data to obtain new insights and create new applications. The largest challenge of computer science has been the largest on the algorithmic front: how can we create machines that help us do useful things with the data? To address this challenge, the field of data science has emerged as the systematic and interdisciplinary study of how knowledge can be extracted from both structed and unstructured data sets. Machine learning is a subfield of data science, where the task of building predictive models from data has been automated by a general learning algorithm and high prediction accuracy is the primary goal. Many practical problems can be formulated as questions and there is often data that describes the problem. The solution therefore seems simple: formulate a data set of inputs and outputs, and then apply machine learning to these examples in order to learn to predict the outputs. However, many practical problems are such that the correct outputs are not available because it takes years to collect them. For example, if one wants to predict the total amount of money spent by different customers, in principle one has to wait until all customers have decided to stop buying to add all of the purchases together to get the answers. We say that the data is ’censored’; the correct answers are only partially available because we cannot wait potentially years to collect a data set of historical inputs and outputs. This thesis presents new applications of machine learning to censored data sets, with the goal of answering the most relevant question in each application. These applications include digital marketing, peer-to-peer lending, unemployment, and game recommendation. Our solution takes into account the censoring in the data set, where previous applications have obtained biased results or used older data sets where censoring is not a problem. The solution is based on a three stage process that combines a mathematical description of the problem with machine learning: 1) deconstruct the problem as pairwise data, 2) apply machine learning to predict the missing pairs, 3) reconstruct the correct answer from these pairs. The abstract solution is similar in all domains, but the specific machine learning model and the pairwise description of the problem depends on the application.Kerätyn datan määrä on kasvanut kun digitalisoituminen on edennyt. Itse data ei kuitenkaan ole arvokasta, vaan tavoitteena on käyttää dataa tiedon hankkimiseen ja uusissa sovelluksissa. Suurin haaste onkin menetelmäkehityksessä: miten voidaan kehittää koneita jotka osaavat käyttää dataa hyödyksi? Monien alojen yhtymäkohtaa onkin kutsuttu Datatieteeksi (Data Science). Sen tavoitteena on ymmärtää, miten tietoa voidaan systemaattisesti saada sekä strukturoiduista että strukturoimattomista datajoukoista. Koneoppiminen voidaan nähdä osana datatiedettä, kun tavoitteena on rakentaa ennustavia malleja automaattisesti datasta ns. yleiseen oppimisalgoritmiin perustuen ja menetelmän fokus on ennustustarkkuudessa. Monet käytännön ongelmat voidaan muotoilla kysymyksinä, jota kuvaamaan on kerätty dataa. Ratkaisu vaikuttaakin koneoppimisen kannalta helpolta: määritellään datajoukko syötteitä ja oikeita vastauksia, ja kun koneoppimista sovelletaan tähän datajoukkoon niin vastaus opitaan ennustamaan. Monissa käytännön ongelmissa oikeaa vastausta ei kuitenkaan ole täysin saatavilla, koska datan kerääminen voi kestää vuosia. Jos esimerkiksi halutaan ennustaa miten paljon rahaa eri asiakkaat kuluttavat elinkaarensa aikana, täytyisi periaatteessa odottaa kunnes yrityksen kaikki asiakkaat lopettavat ostosten tekemisen jotta nämä voidaan laskea yhteen lopullisen vastauksen saamiseksi. Kutsumme tämänkaltaista datajoukkoa ’sensuroiduksi’; oikeat vastaukset on havaittu vain osittain koska esimerkkien kerääminen syötteistä ja oikeista vastauksista voi kestää vuosia. Tämä väitös esittelee koneoppimisen uusia sovelluksia sensuroituihin datajoukkoihin, ja tavoitteena on vastata kaikkein tärkeimpään kysymykseen kussakin sovelluksessa. Sovelluksina ovat mm. digitaalinen markkinointi, vertaislainaus, työttömyys ja pelisuosittelu. Ratkaisu ottaa huomioon sensuroinnin, siinä missä edelliset ratkaisut ovat saaneet vääristyneitä tuloksia tai keskittyneet ratkaisemaan yksinkertaisempaa ongelmaa datajoukoissa, joissa sensurointi ei ole ongelma. Ehdottamamme ratkaisu perustuu kolmeen vaiheeseen jossa yhdistyy ongelman matemaattinen ymmärrys ja koneoppiminen: 1) ongelma dekonstruoidaan parittaisena datana 2) koneoppimista sovelletaan puuttuvien parien ennustamiseen 3) oikea vastaus rekonstruoidaan ennustetuista pareista. Abstraktilla tasolla idea on kaikissa paperissa sama, mutta jokaisessa sovelluksessa hyödynnetään sitä varten suunniteltua koneoppimismenetelmää ja parittaista kuvausta