Mobile Gaming Trends and Revenue Models

Khaled Mohammad Alomari1(&), Tariq Rahim Soomro2,and Khaled Shaalan3

1 Faculty of Arts and Sciences, Abu Dhabi University, Abu Dhabi, UAEkhaled.alomari@adu.ac.ae

2 Faculty of Computing and Engineering Sciences,SZABIST Dubai Campus, Dubai, UAE

tariq@szabist.ac.ae3 Faculty of Engineering and IT, British University in Dubai, Dubai, UAE

khaled.shaalan@buid.ac.ae

Abstract. The study tries to find out the most important features in buildinggames based on the grossing. The study is limited to fifty iPhone games thathave achieved top grossing in the USA. The game features were extracted froma previous study [1] and classified through ARM funnel into five groups (“A”,“R”, “M”, “AR”, and “RM”). The paper follows CRISP-DM approach underSPSS Modeler through business and data understanding, Data preparation,model building and evaluation. The researcher uses Decision Tree model sincethe features have closed value i.e. (Yes/No) on the grossing weight. The studyreached to the most important 10 features out of 31. These features are importantto build successful mobile games. The study emphasizes on the availability of(Acquisition, Retention and Monetization) elements on every successful gameand if any is missed, will lead to the failure of the game.

Keywords: Mobile games � Mobile game trends � Mobile games revenuemodels

1 Introduction

According to the UN International Telecommunications Union, the of end 2014 therewere 7 billion subscriptions in the world for mobile phone - compared to 2.2 billion in2005 and 719 million in 2000 [2]. Mobile phones are used extensively for entertain-ments, communicationsand it became a part of personal accessories. The new trendsand applications developments in the mobile phone open the door for developers tooffer their expertise in the development of better to mobile phone applications [3].Currently, mobile phones support a wide range of connectivity features like Bluetooth,NFC, Wi-Fi and Data Mobile. Today, mobile data become more prevalent. Accordingto the UNInternational Telecommunications Union there 2.3 billion mobile-broadbandsubscriptions globally [2] and 352 operators globally. The 4G mobile technology waslaunched in December 2009 and until the end of January 2015, more than a third of theglobal users were using 4G mobile data [4]. The technology is not going to stop hereand 5G was predicted at 360-europe event held in Brussels last year [5].

© Springer International Publishing Switzerland 2016H. Fujita et al. (Eds.): IEA/AIE 2016, LNAI 9799, pp. 671–683, 2016.DOI: 10.1007/978-3-319-42007-3_58

As far as games are concerned on mobile devices, “Snake” was the first gameinstalled on Nokia mobile phone in 1997 – the now-ubiquitous Snake – in one of itsmodels [6]. As compared to game console and PC games using mobile devices to playgames are quite easier [7, 8]. Today mobile game becomes more important issue intechnical development of Smartphone and/or tablets device [9] and mobile gamesbecome the prime component of the video game industry [6]. Mobile games part is oneof the fastest growing fields in the mobile market [9] and increasing by around 20 %annually [10].

Mobile Games is not limited to entertainment only, but entered in the fields oflearning and treatment. In the field of Learning and Education, it is observed thatlearning potentials for players Games higher than non-players [9]. It is noted in theliterature that serious games can make a positive contribution [11]. Mobile game-basedlearning (mGBL) use gameplay to strengthen the motivation to learn, engage in theacquisition of knowledge and to improve the effectiveness of learning content transferand should mix between education and purpose entertainment to make mGBL suc-cessful [3]. The games promote skills of thinking and planning more than assigned tocontent knowledge [12] also promote problem-solving and collaboration [13].According to [3] it was found that the students preferred mobile phone for learningrather than other devices. There are several examples in the field of treatment too.“Lumosity” is a mobile game to brain training based on the latest discoveries inneuroscience and the goal of this game is to make Human Cognition more rapidly andefficiently [14]. Another example is, e-Health game “Re-Mission” to improve behav-ioral outcomes for young patients with cancer in a multisite randomized controlled trial[15]. There is also SPARX program that uses a platform of a fantasy game to teachCBT skills for adolescents with depression [16]. According to the Apple app store andGoogle play, the popular mobile game markets, the mobile games can be classified into18 classes [17, 18], as shown in Table 1 below.

Few years ago, most video games industry moves their development towardsmobile games and today reap billions of dollars. This paper will discuss importantfeatures to build mobile games and will also cover how to build successful gamedevelopment environment and will study top grossing games and analyze data throughCRISP-DM approach where this approach plays a big role in building models, utilizingthe logical sequence of construction steps which applied in this study, such as businessand data understanding, Data preparation, model building and evaluation.

This paper is organized as follows; Sect. 2 will discover trends and revenue modelsin mobile gaming; Sect. 3 will discuss Materials and methods used in this study;Sect. 4 will represent analysis and findings; Sect. 5 will conclude this study.

Table 1. Mobile game categories [17, 18]

Action Adventure Arcade Board Music

Casino Dice Educational Family SportsPuzzle Racing Role playing Simulation StrategyTrivia Word Card

672 K.M. Alomari et al.

2 Trends and Revenue in Mobile Gaming

In the year 2014, there were more than 850,000 apps in Apple App Store and 700,000apps in Google Play store [1]. The statistic shows in Feb 2014 that the game appli-cations get on 41.2 % of all applications category in Google Play store [19] and itshows in Sep 2014 that the game applications were the 1st popular category, with ashare of 21.14 % of all applications in Apple app store [20]. The year 2014 has been ajammy year for the mobile games industry. United States get on the highest earningsoccurred, where games accounted for nearly all revenue of the platform (Leonov 2014).That mobile games remained the largest segment in terms of revenue, accounting foraround a quarter of the total market [10].

Most games are based on “Free to play” model it is the fastest growing during thepast 15 years [21]. Following are few important models described:

• Free to Play (F2P): These games are free to download and play, but contain microtransactions [22].

• Pay to play (P2P) or Premium: In these types of models the users required to paybefore playing [1].

• Freemium: These are part of F2P [23], and are based on the combination between“free” and “Premium”. Freemium become the popular business model for smart-phone app developers today [24]. In the year 2014, there are 69 % of gross revenuefrom IOS and 75 % from Android devices coming from the freemium model.Freemium is achieving monetization from two ways direct and indirect [1].– Direct monetization: In-App Purchases (IAP) for example, it sales Virtual

Goods inside games, best example, Hay Day and clash of clan. In the year 2014,they were able to generate more than 2 million US$ a day from these two titles[25].

– Indirect monetization: Coming from advertising and the best example is FruitsNinja of Half Brick, this game monthly generates 400000 US$ from Ads only.

• Paynium: This model is like a freemium, but the difference is that it requires apayment on an initial purchase, then playing is completely free [26].

This study focuses on the latest trends of the mobile game market and explores itsrevenue model in general and freemium revenue model in particular. Also, what is theimportance of acquisition, retention and monetization for F2P games? Mobile Gamesthat depend on freemium revenue models are analyzed along with the relationshipbetween revenue and games features are explained. Other important relationships arealso analyzed, for example, the relationship between revenue, category, developer,users daily play, users daily install and others, for the future researchers and developers.Features in this study are extracted from Askelöf’s [27] and Moreira et al. [1] they usedARM (Acquisition, Retention and Monetization) funnel, as shown in Fig. 1, to chosena group of features related to F2P Model. Total 31 features were used in this study.Figure 5 later in the paper shows the features and classification.

Mobile Gaming Trends and Revenue Models 673

3 Materials and Methods

Applying the experimental method to extract, transform, and analyze data, theresearchers followed CRISP-DM (Cross Industry Standard Process for Data Mining)approach to handlingthe problem. Researcher this approach adopted for its success inproviding scientific contributions in a previous study [1] in the same field. Wherepresented, what the features are needs to build a successful game.

3.1 Data

Step-1: Researcher extracted data from “thinkgaming” [28]. Apple app store’s Top50 Grossing iPhone Games were investigated. Out of 50, two paid games wereremoved, as study focus on a freemium model; all data inserted in an MS Excelspreadsheet, with attributes, for example, Game Name, Revenue, New Installs,Daily Active User, Category and Developer Name for each game to facilitate dataanalysis.Step-2: Researchers create another MS Excel spreadsheet with attributes GameName in Columns and game features in rows. Out of 48 further 2 games wereremoved because of missing data.Step-3: After understanding the phases of business and data, data preparation wasinitiated to use it in the modeling tool.

Fig. 1. ARM funnel

674 K.M. Alomari et al.

3.2 Transformation

Total 1824 entries collected through two ways. First install top grossing games iniPhone and extracted 31 features for each game; second extracted other data fromthinkgaming for further analysis. The data was processed and converted in such a wayto the values to fit with statistical operations in new MS Excel spreadsheet. Thisprepared data now can use in software, such as Excel, SpSS statistical & SpSSModeler, to get the best result.

3.3 Normalization

In order to get the easiest value in the columns (Revenue, New Installs and DailyActive User) data was normalization to get value between (0,1) by using followingformula:1

value ¼ xi�Min range xið Þð ÞMax range xið Þ �Min range xið Þð Þ

To measure performance evaluation used ROC Curve test, where this test just acceptsvalues between (0–1) i.e. Candy Crush Saga revenue is 1005806$ after normalizationCandy Crush Saga revenue the result becomes (0.590196166).

3.4 Model

In this study, three step processes have been used to generate a model. In the first step,a “Decision Tree” is used to extract knowledge about the problem domain. In thesecond step, a “Regression Analysis” used to support the most significance betweenvariables. Finally, the third step measures “Performance Evaluation” to ensure thevalidity of results that get from two previous steps.

Decision Tree. In the finding of this study, a decision tree is the best way for modelingdata, as data contents features have close values i.e. (yes/no) were used to find thefeatures are available in some particular game or not. The data representation bydecision tree allows advantage compared with other approaches and easy to interpret.The goal of a data model is to create classification model to predict target attributesusing SpSS modeler software. The value was used in this model was: (label: Game-Name, values: All Features, weight: Revenue). Figure 2 below depicts the strongrelationship between revenue and the features through decisions tree model was gen-erated and it was found that there are total 10 features, which are most important amongall 31 features. These 10 features model was generated and find that there is a realisticchoice of features to achieve higher profits, as shown below in Fig. 3.

1 “Xi” is value for each game under columns (Revenue, New Installs and Daily Active User) and“range Xi” mean all games values in the same columns.

Mobile Gaming Trends and Revenue Models 675

Regression Analysis. To extract the relationship between attributes in the database,linear regression analysis has been used through SpSS statistical software. Beforeanalysis, two variable dependent and independent variable were chosen. All features

Fig. 2. Features through decision tree

Fig. 3. Decision tree rules for 10 Features

676 K.M. Alomari et al.

that got from Decision Tree after applying analysis through revenue attributes wereused as dependent variable and feature factors were used as an independent variable asshown in Table 2. From Table 2, it is depicted that the statistical value T-Test of(2.176) at the 0.05 level. This value is statistically significant, which indicates theexistence of a relationship between features factor and revenue.

Performance Evaluation. ROC Curve is the fundamental tool for diagnostic testevaluation. The ROC Curve test was used for this step. ROC Curve is binary classifiersystem for illustrates performance. ROC Curve was acceptable if the result is between1.0 and 0.5 and if it is less than 0.5 is not acceptable. The SpSS statistical software wasused to apply ROC Curve through variable “Revenue” and as a test variable all featureswere extracted from Decision Tree, as shown in Fig. 4 below and result of test variableas shown in Table 3.

Table 2. Linear regression between features factor and revenue

Coefficientsa

Model Un-standardizedcoefficients

Standardizedcoefficients

t Sig.

B Std. error Beta

1 (Constant) −104368.711 130133.878 −.802 0.427Features.fac 45103.448 20727.667 .305 2.176 0.035

aDependent Variable: Revenue

Fig. 4. ROC Curve (Color figure online)

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4 Analysis and Findings

This part will discuss the approach to game features and its relationship of revenue withthe category, developers, other features, for example, the number of users’ daily play, anumber of users’ daily install.

4.1 Category

Study finds 14 categories out of 18 categories of 48 top grossing games (as in 2 gamesresearchers were not able to find all related data), as shown in Table 4:

Table 3. Result of test ROC curve variable

Test result variable (s) Area

Invite friends feature .628Skill tree .883Facebook .585Leaderboard .574Time skips .872Request friend help .670Event offer .745Customizable .862Soft currency .606Unlock content .660

Table 4. Category-wise

Category

Trivia

Card

Arcade

Strategy

Casual

Casino

Fam

ily

Action

Role P

laying

Sports

Simulation

Adventure

Puzzle

racing

Total

Frequency 1 1 1 6 7 13 2 2 2 2 3 2 4 2 48

Percent

2.1

2.1

2.1

12.5

14.6

27.1

4.2

4.2

4.2

4.2

6.3

4.2

8.3

4.2

100.0

Gross $

83007

37537

32345

3281339

1813050

1165649

224565

172057

165401

175827

233260

131956

218554

65134

7799681

678 K.M. Alomari et al.

As shown in Table 4, the casino games listed the highest 13 games out of 48 topgrossing games in the USA on iPhone devices. This is due to culture and lifestyle. Thecasual and strategy games came next at 7 and 6 respectively.

4.2 Developers

Study finds 8 developers out of 31 Developers of 48 top grossing games (as in 2 gamesresearchers were not able to find all related data), as shown in Table 5:

As shown in Table 5, King.com had 5 games out of 48 games. The net grossexceeded $ 1 m and 700 thousand per day. Whereas Supercell had 3 games out of 48but it reached nearly 2 m and 200 thousand per day. These results were measured oniPhone devices in the USA.

There are many developers in the field of games. However, a successful gamedeveloper might attract the majority of users if one of their successful games was themost popular and used among others.

4.3 Features Analysis

Study used 31 features and divided it into five groups, such as “A”, “R”, “M”, “AR”,and “RM” of ARM Funnel approach (Fig. 1 above) and the results are shown in Fig. 5.Through review the 10 features, as shown in Table 6 below, which are important to

Table 5. Developers-wise

Developers

KIN

G.C

OM

EL

EC

TR

ON

IC

AR

TS

ZY

NG

A IN

C

SUP

ER

CE

LL

PL

AY

TIK

A L

TD

BIG

FISH

GA

ME

S INC

SGN

FU

NZ

IO IN

C

Frequency

5 4 4 3 3 2 2 2

Percent10.4

8.3

8.3

6.3

6.3

4.2

4.2

4.2

Gross $

1716487

341659

140003

219999

2

322896

318159

124277

72314

Percent

22.01%

4.38%

1.79%

28.2

1%

4.14%

4.08%

1.59%

0.93%

Mobile Gaming Trends and Revenue Models 679

build the successful game with the ARM Funnel Column finding the three elements(Acquisition, Retention and Monetization) in the F2P games are very important for thesuccess of the game and a failure to one side leads to the failure of the game, where twofeatures have classified under acquisition and four features have classified under eachretention and monetization.

4.4 Other Relationships

Study finds other relationships belonging to problem and findings were summaries inTable 7 using linear regression.

Fig. 5. Feature frequency of 48 top grossing mobile game and groups (Color figure online)

Table 6. Ten important features with ARM funnel classification

Nodes Importance ARM funnel

Invite friends feature 0.4356 A01Skill tree 0.1708 R03Leaderboard 0.0869 R11Unlock content 0.0341 R04Soft currency 0.0341 M10Facebook 0.0341 R01Customizable 0.0341 M09Event offer 0.0341 M06Request friend help 0.0341 AR01Time skips 0.0341 M07

680 K.M. Alomari et al.

Table 7 depicted that the statistical value T-Test at the 0.05 level. This value isstatistically significant, which indicates the existence of a relationship betweenDependent and Independent variables. As far as there are daily active users of thegames, there will surely be revenue and new installations of games, where a regressionlinear analysis (Table 7) shows significant equal 0.0 between the elements above. Thisproves a strong relationship between them.

Table 7. Linear regression between other relationships belonging to problem and findings

Dependent Independent Sig

Unique offer Daily Active User .014Daily offer Revenue .002

Daily Active User .027Event offer Revenue .010Time skips Revenue .011Timed boost Revenue .018Soft currency Revenue .019

Daily Active User .001Chat Daily New Installs .027

Revenue .008Daily Active User .009

Competitive Revenue .001Daily Active User .006

Single Revenue .024Daily Active User .046

Cumulative Revenue .011Daily Active User .043

Item upgrade Revenue .010Daily Active User .031

Status upgrade Revenue .020Daily Active User .135

M.Factor Revenue .001Daily Active User .008

R.Factor Revenue .004RM.Factor Revenue .037SUM.ALL.factor Revenue .000

Daily Active User .014Developer Daily Active User .049Daily Active User Revenue .000

Daily New Installs .000Daily New Installs Revenue .029

Daily Active User .000

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5 Conclusions

This paper presents the development of previous research regard building a successfulgame. The 31 features were extracted from the decision tree. The study found that thereare 10 most important features to build a successful game. The result is useful for futureresearchers and developers who want to develop and implement a successful game.Other features were also analyzed in Table 7 and their significant values were pre-sented with the relationship among revenue and other features the strongest relationaffecting the revenue was the Daily Active User. So the developer should strengthenthe retention element in building games. It is also believed that the other features willalso play important roles in successful games, such as culture, lifestyle and loyalty tobrand names.

Acknowledgement. Special thanks to ÁtilaValgueiro Malta Moreira who provides some data,which help this research to be completed (Moreira et al. 2014).

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