Despite the apparent utility of IAM, CMA and MAEF, they all basically only include factors related to consumers, advertisers, ad networks and built around the goals of advertisers – the demand side of an ad serving process (Brakenhoff & Spruit 2017; Grewal , Bart , Spann & Pal Zubcsek 2016; Rodgers & Thorson 2000). In particular, the identified publisher-controlled factors will be individually evaluated using the A/B testing – the most popular testing methodology on the Internet (Kohavi et al. 2009).
Ad Space Supply Process
It is common practice for large publishers to sell only the remaining inventory through an ad exchange, and negotiate the rest of the inventory directly with advertisers. In both the guaranteed and non-guaranteed ad serving processes, it is the publisher who provides the ad spaces and provides the ad impressions through its ad spaces.
Ad Space Delivery Process
As a result, any mobile in-app ad ad serving process can be broken down into ad space provisioning process and ad space delivery one.
Mobile In-App Advertising Participants
Ultimately, users are consumers of advertisers' brands or products (Lin, TTC et al. 2015). Direct response is also used to measure the relevance of ads to users in the long term (Kohavi et al. 2009) and to measure the best match for ad networks/exchanges (Kumar, S 2016).
Mobile In-App Advertising Outcome Metrics
The cost-per-click (CPC) or click-through rate (CTR) model remains one of the most important media pricing metrics for the Internet. These outcome measures are the joint product or interaction of the consumer and the advertisement.
Mobile In-App Advertising Factors
Advertisers-controlled factors
Another related study by Lim, Tan and Jnr Nwonwu (2013) revealed that mobile users remember image banner ads better than text ads and are more likely to view large image banner ads than app content. Li, Y-W, Yang, and Liang (2015) found that both website interactivity and promotional methods can improve consumer attitudes, but price discounts are only effective when the brand perception is functional, but not effective when the brand image is symbolic.
Consumers-controlled factors
Certain aspects related to the specific format of the advertisement can have an impact on its effectiveness such as the type of advertisement (Grigorovici & Constantin 2004). Personalization is a customer-oriented marketing strategy that aims to deliver the right content to the right person at the right time, in order to maximize business opportunities (Tam & Ho 2006). Their findings suggest that the level of personalization influences consumer-related factors, such as feelings of intrusiveness (Doorn & Hoekstra 2013), feelings of vulnerability, the ad's perceived usefulness, response and privacy concerns (Bleier & Eisenbeiss 2015a).
The level of personalization also influences outcomes, such as click-through rates (Aguirre, E et al. 2015). Today, advertisers have many consumer targeting options to monitor and improve the performance-based effectiveness of their mobile in-app ad campaigns.
Ad networks-controlled factors
Medium context refers to the ad environment provided by the vehicle that carries it, such as a television programme, an issue of a magazine or a website (Pieters & Raaij 1992). Studies on commercial context have mainly focused on the effect of the amount and nature of other commercial messages in an advertisement's environment, referred to as clutter and competitive clutter (Moorman 2003). It has been shown that as the number of other ads in the target ad environment increases, the effectiveness of target augmentation decreases, especially when other ads are directly competitive.
Grewal, Bart, Spann, and Zubcsek (2016) summarized those context factors in the Mobile Advertising Effectiveness Framework as shown in Appendix D. This section presented an overview of the mobile in-app advertising processes, participants, outcome measures, and factors.
4 Publishers-controlled Factors and Interactions
Publishers-controlled Factors
All the academic and practitioner literature mentioned has pointed out that ad slot duration has not been fully studied in the past, but could be a factor that can strongly influence the click-through rate of mobile in-app ads. By providing the advertising space with predefined and relevant characteristics, the publisher was able to significantly increase the click-through rate. However, the empirical results of Li, H, and Bukovac (1999) showed that click-through rates do not increase proportionally with size.
All the cited academic and practical literature have pointed out that the size of the ad space has not been fully studied in the past, but it can be a factor that can strongly influence the click-through rate of ads in mobile applications. Delivery factors related to publishers are shown to be critical in the ad serving process that will then increase the click-through rate of mobile app ads.
Publishers-controlled Interactions
Nakamura and Abe (2005) developed an LP-based algorithm to schedule banner ads, where they introduced three features that each ad was associated with; time of day when ads were preferred to be seen (eg afternoon), page category (eg sports) and number of impressions. The features were then used to determine the optimal ad time and location that maximizes overall revenue, rather than relying solely on an individual ad's CTR. An important factor is considered not only the time of day, but also the day of the week.
Similarly, Tuesday and Friday are the best days when the maximum number of Internet users in India open and click on email communications sent to them ('Annual State of Email Marketing in India' 2015). When banners use animation, they also take on the character of television advertising, and this may suggest that animated banner ads will attract more attention and thus be clicked more (Wegert 2002).
5 An Integrated Effectiveness Framework
One of the first models is Defining Advertising Goals for Measured Advertising Results (DAGMAR). With the Internet, however, control has passed from the advertiser to the consumer. This is a framework that maps the components involved in "creating and targeting an ad".
The components of consumer, advertiser, and ad network controlled factors comprise the theoretical content derived during the literature review phase and are critical to the design of the current study. The click-through rate refers to the relevance and implications of the current study derived from the empirical analysis and results.
6 Experiments
Methodology
Ad space size: The size of ad spaces is selected by publishers (‘Ad size guidelines for display and mobile advertising’ 2015). For this reason, the top and center of the screen are selected as two values for the position of the ad space. The eight independent variables were ad space duration, ad space size, ad space position, ad space scheduling, location, time of day, ad medium, and ad type.
For the current study, text and image ads were selected as two values for the ad type. For the duration of the ad space and the size of the ad space, the click-through rate is adjusted according to the time-based formula of CTR (Truong 2016).
Results
In the current study there is one dependent variable which is the click through rate. Based on the p-values of the z-tests, it showed that all the factors have a strong impact on the click-through rate. This confirms that ad type is a factor that strongly influences click-through rate in the context of mobile in-app advertising (Lim, Tan & Jnr Nwonwu 2013).
Since all eight factors have been confirmed to have major effects on click-through rate. This further demonstrated that the influences of consumer, advertiser, and ad network factors on click-through rates are strongly moderated by factors controlled by publishers.
7 Discussions 7.1 Key Findings
Contributions
The current study is one of the first attempts to explore this promising area. The research contributes to the literature on mobile in-app advertising by examining the role of publishers and the impact of their offering and delivery factors on the click-through rate of mobile in-app advertising. The current research suggests new advertising strategies from publishers to further improve ad click performance of mobile in-app advertising.
In this way, newly integrated advertising strategies can be recommended to be put into practice and could help increase mobile ad revenue in the app significantly higher by balancing the benefits for all participants involved. Furthermore, not only highly applicable to in-app mobile advertising, this study can be extended to other forms of advertising where the role of publishers has not been thoroughly investigated.
Limitations and Future Research
This study also developed a new empirical method whereby multiple factors controlled by multiple participants could be tested interactively. For publishers who have more than one app published, applying the new delivery and delivery strategies can give them more benefits. For agents who publish apps on behalf of publishers, this strategy can provide even more value.
Ad networks can integrate new strategies related to these factors to increase the matching and relevance of the ads to their users. The current study sheds new light on online marketing, with interactive outcome metrics playing a more important role than ever before.
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APPENDIX A: Real-Time Bidding Process
APPENDIX B: Interactive Advertising Model
APPENDIX C: Framework for Online Behavioral Advertising
APPENDIX D: Mobile Advertising Effectiveness Framework
