Using Big Data to Understand Chinese Users' Intentions to Tap Through Mobile Advertisements

Using Big Data to Understand Chinese Users' Intentions to Tap Through Mobile Advertisements

Jing ("Jim") Quan (Salisbury University, USA)
DOI: 10.4018/978-1-7998-2235-6.ch006

Abstract

This study examines influencing factors for users' intentions to tap through mobile advertisements. This chapter uses a data set with 115,899 records of ad tap-through from a mobile advertising company in China to fit a logit model to examine how the probability of advertisement tap-through is related to the identified factors. The results show that the influencing variables are application type, mobile operators, scrolling frequency, and the regional income level as they are positively correlated with the likelihood whether users would tap on certain types of advertising. Moreover, a Bayesian network model is used to estimate the conditional probability for a user to tap on an advertisement in an application after the user already taps on another advertisement in the same application. Based on the findings, strategies for mobile advertisers to engage in effective and targeted mobile advertising are proposed.
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Introduction

The fastest growing form of marketing is digital advertising, within which, mobile is the fastest growing medium as people feel increasingly comfortable viewing mobile advertising and making purchases (Smith, 2019). The rapid development of mobile technology makes it possible for mobile advertisers to use various applications to dynamically push advertisements onto smartphones and tablets (Wong, Tan, Tan, & Ooi, 2015). The traditional advertising formats of short message service (SMS) and multimedia messaging service (MMS) (Park, Shenoya, & Salvendy, 2008; Samanta, Woods, & Ghanbari, 2009) are gradually being replaced by mobile interactive advertising (Laszlo, 2009). More advantageous than the traditional advertising, this new form of mobile advertising possesses properties of real-time, mobility, higher rates of user reachability, and instantaneous interactions. Because of the unique match between a smartphone and its user identification, mobile advertisers can analyze user behavior and preferences to achieve more accurate advertising content delivery. The mobile advertising service industry, resulting from the popularity of mobile advertising, sets its core business as pushing advertisements to mobile users. They not only organize a large number of mobile application developers to provide application services to mobile customers, but also promote the development and practice of personalized advertising service market.

Since 2007 when Apple marketed the first generation of smartphones the mobile Internet industry has flourished. The emergence of Android, Windows, and other smartphone operating systems, along with iPhone, has provided strong support to smartphone handset manufacturers. The model of mobile applications (app), third-party developers and application stores quickly became the most popular and opportunistic business model in the market. This model calls for sharing smartphone operating system interfaces (API) or the source codes, allowing non-platform developers and third-party operators to develop apps, and then uploading the apps to the app store for global users to download either for free or for a fee. The developers and platform operators share the revenue. Apple and Google are well-known examples of this business model. Because this model allows any developer to upload apps, the number of applications on the platform can increase rapidly to meet users’ demand for a variety of applications. Apple’s iOS platform has become a mature fee-based model, and its rigorous audit ensures the quality of apps. Because most of the apps in the Apple store are for fee-based downloads and iOS users have been accustomed to paying, iOS developers can earn a decent income. As a result, this business model has created many entrepreneurs.

In contrast to iOS, the Android platform is an open platform. Because most of the apps are free to download, developers on the Android platform develop and upload apps for free. Such an open platform leads to uneven application quality and results in the current situation where the majority of users do not want to pay for downloading apps. For example, Viennot, Garcia, and Nieh (2014) find that about 80% of apps in the Google Play Store are free. In general, developers on the Android platform cannot make money by relying on basic app downloads, although many commercial apps are paid and an important source of revenue for their developers is the price of the app. To overcome this shortcoming, a new kind of profit model of “free apps + advertising” has gained market popularity in recent years. Taking advantage of this trend, a number of mobile advertising companies emerged. Successful examples include Millennial Media, StrikeAd, and AirPush in the U.S., and Cellphone Ads Serving E-Exchange (CASEE), WOOBO, and Youmi in China. In 2012, Millennial Media saw its initial public offering (IPO) price rise more than 90% on its first day listed on the NASDAQ, showing market confidence and expectation for this emerging industry. Among all forms of advertising, mobile advertising is expected to grow the fastest; it is the new frontier of advertising. Social media companies such as Google and Facebook race to gain shares of this form of advertising. According to the U.S. market research firm eMarketer, U.S. and China mobile advertising revenues in 2015 reached $28 and $13 billion, respectively, and in 2018 are expected to reach $57 and $40 billion, respectively (Dogtiev, 2016).

Key Terms in this Chapter

Mobile Commerce (m-commerce): Buying and selling products on wireless devices such as smartphones.

TAP: When a user clicks on a digital advertisement.

Bayesian Network Model: Statistical model utilizing past user behavior to predict the probability of future behavior.

Mobile advertising: A form of mobile commerce that includes text ads, banner advertisements, and downloadable apps.

Tap-Through/Tap Through Rate (TTR): Measure of conversion from an advertisement that is shown to an advertisement that the user interacts with by tapping.

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