14. Analyzing the Demand and Survival of Mobile Applications - Dongwon Lee

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Analyzing the Demand and Survival of

Mobile Applications:The Case of Apple’s App Store

Dongwon Lee

Associate Professor of MIS

Korea University Business [email protected]

August 8, 2011



Research Background

Characteristics of Smartphone Market

0

100

200

300

400

500

600

Mar-10 Jun-10 Sep-10 Dec-10 Mar-11

SKT

KT

LGU+

The Environment of Smartphone

Number of Smartphone Subscribers in Korea 1)

Wi-Fi Coverage (%) in Korea 2)

0

1

2

3

45

6

7

2009 2010 2011

SKT

KT

LGU+

Source: 1), 2) Announcement of each company, 2011

Unit : 1) thousand, 2) ten thousand

Relationship

Market Segment

Low

High

HighLow

Service Availability

N u m b e

r o f S m a r t D e v i c e S u b s c r i b e r s

Source: Aggregated data (e.g., newspaper, each company)



Research Background

Characteristics of Mobile Application Market

Mobile Application Market in the U.S.

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

i-Phone Android i-Pad Ovi Blackberry

Jan-11

Jun-11

Mobile Application Market Forecast1)

Source: 1) Distimo Report , April 2011

Characteristics of Market (App Store)

Market

Product

•Multi-sided market

•Uniqueness : Ubiquitous transaction

•Spontaneity : Platform (or service)providers play a minor role compared tothose of other markets.

•Consumer centric product•

Uniqueness : Affected rarely by externalfactors such as mass media, but by userexperience or WOM

•Spontaneity : Application developers andconsumers play a key role.

Price •Important but a minor issue

•Uniqueness : Co-existence of free andpaid market

•Spontaneity : Application developers andconsumers play a major role rather thanservice providers (almost fixed price).



Research Background

Free vs. Paid Download Ratio & Average Selling PriceAmong 300 Most Popular Applications

Source: Distimo Report , June 2011



Research Background

Content needs to be localized to be successful in Asia

Source: Distimo Report , June 2011

34%

Among 300 most popular applications, 87% of free apps and 78% of paid apps are regionally aimedin South Korea.



Research Questions

Different in Free vs. Paid Market? WOM (Word of Mouth)?

Level of Exposure (Slot Effect)?

Rationality vs. Herding?

Free Apps Paid Apps

Apple iTunes

PodGate App

Free Apps

Paid Apps



Research Context

Apple iTunes

http://podgate.com

iTunes SW and PodGate website provide identical top 300 popular mobile applicatio



Number of reviewsName of application

Current price

Duration after first release

Category of app.

Developer

Last update date

Rating

Number of hits

Number of downloads

Number of recommendations

Number of raters



Research Objectives

Key Factors Objectives

ConsumerDemand Evaluation of the factors influencing the consumer demand(i.e., application downloads)

Understanding of the factors influencing the survival of theapplications (i.e., the number of days downloaded)

Measuring the role of exposure of applications insmartphones using sales rank (i.e., the number of dayslisted in the charts (e.g., top 300 list))

Role ofExposure

ProductSurvival



Literature Review

Topic Literature

Uncertainty

• Information made by others acts as a source of one's decision.Individuals follow the behavior of the preceding individuals withoutregarding to their own information. ( Banerjee 1992, Bikhchandaniet al. 1992,1998, Kauffman et al. 2000, Walden & Browne 2009)

• Expectations are informed predictions. Rational means what an agent

has to do with the expectations. Individuals may not know about thefull information, but they learn enough about it through efficient useof available information over time to make their expectations.(Muth 1961, Grossman 1981, Zimmerman 1986, Herstein 1990,Au & Kauffman 2003, 2004)

Herding

RationalChoice

• Derived from the information asymmetry: Akerlof (1970), Stigliz

(1989)• WOM: Individuals use information about the experience of others

to make their purchasing decisions (Clemons et al. 2006,Li & Hitt 2008, Zhu & Zhang 2010)

• Sales Rank: Current popularity is an important determinant of futuredemand ( Salganik et al. 2006, Duan et al. 2009)



Data Collection & Derived Variables

Data for Analysis

Way of data collection Variables

•Characteristics: New variables that have not been used

in the previous studies.

•Aggregated Dataset: To consider fixed effect

Daily downloads •Daily downloads of a specific application

Variables

# of daysdownloaded

•Number of days downloaded during

the period

# of days ranked •Number of days listed on the top 300

Review •Number of reviews

Rating • Consumer ratings scaling from 1 to 8

Recommendation •Number of recommendations

Price •Price of an application

Released period •Period of product release on the market

Description

Period II

Period III

•From July 25 to October 6, 2010

•Sampling the applications based on the

popularity listed on top 300 i-Tunes chart.

•From October 29, 2010 to February 3, 2011

•The application listed more than 5 times on

top 300 during period II.

Collecting Time

Period I

Collecting Method

•From June 20 to July 10 , 2010

•Developing a software agent to extract neededinformation from an aggregate site, PodGate.



An example of free apps



An example of paid apps



Data (10/29/2010 – 2/3/2011)

Descriptive Statistics (Free + Paid = 601 Apps)

Variable Obs. Mean St.Dev. Min Max

Daily downloads 53,747 121.79 412.16 0 24,058

# of review 54,423 37.25 113.33 0 2,585

# of recommendation 54,423 80.42 133.30 0 1468

Released period 54,423 143.01 130.44 0 937

Rating 47,301 5.34 2.56 1 8

# of days ranked 54,423 47.02 35.81 1 97

Price ($) 54,423 1.31 3.48 0.00 49.99

Descriptive Statistics (Free vs. Paid)

Free (335 Apps) Paid (266 Apps)

Variable Obs. Mean St.Dev. Min Max Obs. Mean St.Dev. Min Max

Daily downloads 30,170 194.95 499.83 0 24,508 40,017 28.17 227.91 0 10,412

# of review 30,549 48.70 137.88 0 2,585 40,559 22.59 67.61 0 1,483# of recommendation 30,549 111.02 162.37 0 1,468 40,559 41.26 63.55 0 562

Released period 30,549 107.07 100.00 0 889 40,559 188.99 149.08 0 937

Rating 27,059 5.07 2.61 1 8 36,115 5.71 2.44 1 8

# of days ranked 30,549 50.08 35.82 1 97 40,559 43.10 35.41 1 97

Price ($) 30,549 0.00 0.00 0.00 0.00 40,559 2.98 4.76 0.00 49.99



Identifying Analysis Model

Model Dependent Var. Objective for Use

Demand Daily downloadsWe can understand what factors have significant effects on consumerdemand.

We aim to know what factors are significant for the survival of theapplications .

Number of daysdownloaded

Survival

Using Different Models for Different Aspects



Aggregated Dataset

Descriptive Statistics (Pooled Dataset)Variable Obs. Mean St. Dev. Min Max

Daily purchase 601 116.69 223.50 0.00 3.22

# of days downloaded 601 54.14 36.93 1.00 97.00

# of review 601 36.17 107.96 0.00 1.96

# of recommendation 601 77.52 131.07 0.00 1,467.02

Released period 601 144.77 123.25 9.80 843.27

Rating 535 5.18 2.34 1.00 8.00

# of days ranked 601 46.30 35.62 1.00 97.00

Price ($) 601 1.30 3.44 0.00 49.99

Correlation Analysis (Pooled Dataset)

Variable (A) (B) (C) (D) (F) (E) (G) (H)

Daily purchase(A) 1

# of days sold(B) 0.497 1

# of review(C) 0.456 0.220 1

# of recommendation(D) 0.484 0.385 0.546 1

Released period(E) -0.277 -0.421 -0.126 -0.148 1

Rating(F) -0.093 -0.135 -0.014 -0.075 0.050 1

# of days ranked(G) 0.180 0.296 0.129 0.188 -0.095 0.031 1

Price(H) -0.175 -0.293 -0.043 -0.126 0.160 0.118 0.042 1



Descriptive Statistics (Free vs. Paid)

Descriptive Statistics (Free Dataset)Variable Obs. Mean St. Dev. Min Max

Daily purchase 335 187.96 265.59 0.00 3,216.19


# of review 335 47.48 134.15 0.00 1,958.77

# of recommendation 335 107.06 160.98 0.00 1,467.02

Released period 335 109.56 99.17 9.80 843.27

Rating 299 4.96 2.29 1.00 8.00

# of days ranked 335 49.37 35.71 1.00 97.00

Price ($) 335 0.00 0.00 0.00 0.00

Descriptive Statistics (Paid Dataset)

Variable Obs. Mean St. Dev. Min Max

Daily purchase 266 26.94 98.44 0.00 807.41


# of review 266 21.93 57.83 0.00 870.75

# of recommendation 266 40.32 61.19 0.00 448.43

Released period 266 189.12 135.86 15.08 781.11

Rating 236 5.45 2.38 1.00 8.00

# of days ranked 266 42.43 35.19 1.00 97.00

Price ($) 266 2.94 4.68 0.00 49.99



Correlation Analysis (Free vs. Paid)

Correlation Analysis (Paid Dataset)

Variable (A) (B) (C) (D) (F) (E) (G) (H)

Daily downloads(A) 1

# of days downloaded(B) 0.529 1

# of review(C) 0.510 0.270 1


Released period(E) -0.165 -0.225 0.009 0.041 1

Rating(F) -0.143 -0.058 0.030 -0.062 0.033 1

# of days ranked(G) -0.058 0.243 0.120 -0.016 0.017 0.165 1

Price(H) -0.087 -0.004 0.020 -0.064 0.035 0.114 0.140 1

Correlation Analysis (Free Dataset)Variable (A) (B) (C) (D) (F) (E) (G) (H)

Daily downloads(A) 1

# of days downloaded(B) 0.361 1

# of review(C) 0.439 0.182 1


Released period(E) -0.238 -0.370 -0.163 -0.138 1

Rating(F) -0.039 -0.117 -0.013 -0.051 -0.006 1

# of days ranked(G) 0.235 0.370 0.129 0.243 -0.160 -0.053 1



Results in Pooled Dataset

Standard errors in bracket

***p<0.01, **p<0.05, *p<0.1

Demand Survival

Variable Daily downloads # of days downloaded

# of review 0.551*** -0.001

[0.087] [0.013]

# of recommendation

0.444*** 0.058***

[0.074] [0.011]

Released period -0.319*** -0.096***

[0.067] [0.010]

Rating -4.836 -1.137**

[3.570] [0.521]Price -9.777*** -3.683***

[2.906] [0.424]

# of days ranked 0.495** 0.217***

[0.234] [0.034]

Constant 130.113*** 67.543***

[25.277] [3.688]

Observations 601 601

R-squared 0.336 0.419



Results in Paid & Free Datasets

Standard errors in bracket

***p<0.01, **p<0.05, *p<0.1

Demand Survival

Paid Free Paid Free

VariableDaily

downloads

Daily

downloads

# of days

downloaded

# of days

downloaded

# of review 0.826*** 0.506*** 0.078*** -0.004[0.092] [0.120] [0.025] [0.009]

# of recommendation 0.318*** 0.384*** 0.132*** 0.021***

[0.088] [0.102] [0.024] [0.008]

Released period -0.130*** -0.356** -0.049*** -0.063***

[0.039] [0.142] [0.011] [0.011]

Rating -5.127 -2.205 -0.770 -0.862*[2.352] [5.999] [0.637] [0.452]

Price -2.056 -0.303

[1.443] [0.391]

# of days ranked -0.253 0.816** 0.174*** 0.168***

[0.159] [0.392] [0.043] [0.030]

Constant 66.471** 143.047*** 26.612*** 82.948**[16.927] [43.642] [4.588] [3.292]

Observations 266 335 266 335

R-squared 0.367 0.262 0.284 0.270



Discussion

Research Implications

• WOM plays a critical role on the demand of mobile applications

• Consumers show different patterns on the free and paid application market

• The exposure of popularity has no influence on consumer demand in paid app market

while a strong influence in free app market

Research Contributions

•Using the number of downloads as consumer demand

: We did not use the sales rank of applications as a proxy of consumer demand

•Using sales rank as an independent variable

: We evaluate the relationship between sales rank and consumer demand to measure the effect of

exposure

• Explaining different patterns in free and paid markets using rational choice and herding behavior

: We found heterogeneous demand patterns in mobile application market



Research Limitations

• Data

I. Hard to track the sales rank of the applications when they are dropped from the top 300 lists

II. Results were confined to the aggregated data set

Limitations & Future Research

Future Research

• Data

I. Supplementing the different period of data

II. Comparing the results of panel data set

14. Analyzing the Demand and Survival of Mobile Applications - Dongwon Lee

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