Consumer Engagement Characteristics in Mobile Advertising
Lonneke Brakenhoff and Marco Spruit
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
Mobile Advertising, Persuasive Advertising, Consumer Engagement, MAEF, CRISP-DM.
Advertising on mobile devices is becoming increasingly more important as the possibilities regarding their
design and context become increasingly more extensive. This research focuses on the characteristics of design
and context regarding mobile advertisements, structured according to the CRISP-DM process model. First,
we describe their key concepts and relevant theoretical background. Then, we design the Mobile Advertising
Effectiveness Framework for Consumer Engagement (MAEF4CE), which relates medium types, creative at-
tributes, ad formats, device specific ads, and brand visibility as mobile advertisement characteristics. Finally,
we uncover the combination of characteristics that elicits optimal consumer engagement in mobile advertise-
ments in a real-time bidding dataset.
The number of mobile device owners increased to
68% in 2015 for all ages (Deloitte, 2015). The biggest
growth in mobile device owners occurred in the age
category 55+. This indicates an attractive target group
available for the purpose of mobile advertising. The
advertising world is using mobile devices to reach
their target group by a specific medium: the smart-
phone. Since mobile devices are personalized in a
high degree advertisers are able to reach this target
group more efficiently. Another difference between
mobile advertising and traditional advertising is the
possibility to use the capabilities of the device to spec-
ify a target, such as location-based data.
Less is known about the factors that trigger a
user’s behavior when they get served an advertise-
ment (ad). In the case of mobile advertising we first
distinguish two types of behavior. First, the user can
or does not interact with the ad. This means that the
user performs no action related to the banner and is
therefore not engaged with the ad. The second be-
havior does involve the user performing an action, for
example clicking on the ad. This interaction is called
an engagement. This research focuses on the second
In a recent research Grewal, Bart, Spann, & Zubc-
sek (2016) looked at the broader picture of mobile ad-
vertising. Components related to mobile advertising
were mapped into the Mobile Advertising Effective-
ness Framework (MAEF) as separated components
influencing the mobile advertising process. In this
work we customize and extend MEAF to model the
influence of ad design characteristics on ad engage-
ment in mobile advertising: the MAEF4CE frame-
work. The goal of this project is to increase the num-
ber of consumer engagements. Which will be mea-
sured based on different interaction types. The fol-
lowing research question is formulated:
Which combination of mobile advertisement char-
acteristics can optimally improve consumer engage-
To answer this question propositions are defined
based on the literature and the MAEF. This research is
structured according to the CRoss Industry Standard
Process for Data Mining (CRISP-DM). CRISP-DM is
a very commonly used method for knowledge discov-
ery processes (Spruit et al., 2014). The related work
is described in the following section, Section 2. This
section also contains the explanation of some defini-
tions that are important for the remainder of the re-
port. Next, a theoretical background is set in Section 3
including the explanation of the MAEF and the in-
troduction of the MAEF4CE. The research approach
(Section 4) is structured according to the CRISP-DM
phases: Domain Understanding, Data Understanding,
Data Preparation, Modeling, Evaluation, and at last
the Deployment phase. The results are discussed in
Section 5 followed by the conclusion and suggestion
of further research in Section 6.
Brakenhoff L. and Spruit M.
Consumer Engagement Characteristics in Mobile Advertising.
DOI: 10.5220/0006499602060214
In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2017), pages 206-214
ISBN: 978-989-758-271-4
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Consumer Behavior. Generally, consumer behavior
is based on the consumer‘s buying behavior. Since
the purpose of mobile advertising is not persuading
a user to directly buy a product, the approach of this
research is focused on user awareness.
Mobile Campaign. An ad is part of a campaign.
A campaign is a gathering of one of more ads that
are used to persuade a target group to engage with a
brand, product or service. An advertisement is de-
fined by Flores, Chen & Ross (2014) as “an online
advertising space that typically consists of a combi-
nation of graphic and textual content and contains an
internal link to target ad pages or an external link to
the advertiser’s website via a click through URL“. A
mobile application can be added as a medium to dis-
play ads for mobile advertising besides websites. An
ad that is made viewable in a website or application is
called an impression. The ads within a campaign can
be different. This means that the format, design and
layout can differ between the ads of a campaign.
When a user interacts with an ad, such as by click-
ing or swiping, the user is engaged with the ad. The
specific engagement is measured and therewith avail-
able for further analysis. The engagement measure-
ments say something about the performance of an ad.
The Click-Through-Rate (CTR) is the measurement
that is most used in mobile advertising and indicates
the performance by providing a ratio of the number of
times the ad is served and the number of times the ad
is actually clicked.
Mobile User. A mobile user is referred to as a person
that owns and uses a mobile device. This research
will be mainly focused on smartphone users. These
mobile users are able to receive advertisements (ads)
during the use of an application and/or mobile Web
Brand Awareness. In this research brand aware-
ness is defined according to the definition of Hoyer &
Brown (1990) and is as follows: brand awareness is
as a rudimentary level of brand knowledge involving,
at least recognition of the brand name.
3.1 Persuasive Technology
New technologies are more and more focused on in-
fluencing the behavior of consumers, which is gen-
erally referred to as persuasive technology (Fogg,
2002). According to Fogg (2002) , the Internet had
a great influence on the growth of persuasive tech-
nologies. As an example he mentions the example
of persuading Webshop visitors by making sugges-
tions to buy more products based on their prefer-
ences (i.e. up-selling). The Internet also enables an-
other medium of approaching consumers: advertis-
ing. Fogg (2002) states that persuasive technology
is more effective when it is interactive. This charac-
teristic distinguishes computer technologies from tra-
ditional technologies, such as television commercial
and paper advertisements. Computer technology al-
lows its users to interact, and therefore is able to adapt
its content to the user. Additionally, computer tech-
nology is ubiquitous and versatile, and is able to gen-
erate and store lots of data (Fogg, 2002). Lane (2010)
states that mobile devices with its sensors can create
user profiles for advertising purposes.
Persuasive Advertising. Persuasive Advertising is
defined by Armstrong (2010) as advertising that in-
tends to influence other through all types of media.
This media includes both traditional as ‘new‘, mod-
ern media, like the Internet.
Mobile Advertising. The effectiveness of mobile
advertising is measured by Goh, Chu & Wu (2015)
by looking at the search behavior and advertising re-
sponse of people who got served with a mobile ad.
Goh et al. (2015) describe the benefits of mobile ad-
vertising, compared to the Internet market, as a tar-
geted marketing strategy. Additionally, nowadays a
lot of people own a smartphone (Lane et al., 2010).
Location targeting and being personal are the main
benefits of mobile devices due to the extensive num-
ber of sensors in mobile devices (Grewal et al., 2016;
Lane et al., 2010). Goh et al. (2016), Wong, Tan,
Tan and Ooi (2015) , and Lane (2010) conclude that
mobile devices therefore are the most relevant media
regarding advertising since it allows the advertiser to
serve more tailor made advertisements to their con-
3.2 MAEF
As mentioned earlier Grewal et al. (2016) de-
signed the Mobile Advertising Effectiveness Frame-
work (MAEF). The MAEF is a framework that maps
the components involved in the setup and design of
an advertisement and its purpose is to create a re-
search agenda. The components defined by Grewal
et al. (2016) are: context, consumer, ad goal, market
factors, ad elements, and outcome metrics.
The following Section (3.3) will first simplify the
framework to better fit the more narrow scope of this
research. Then, the MAEF is expanded with some
additional ad elements and metrics.
3.3 MAEF for Consumer Engagement:
The MAEF model shown is too extensive and unfo-
cused for our research application. Therefore we first
customize it by removing various components that are
not applicable to the domain of this research. For ex-
ample, the ‘Market Factors‘ are being disregarded due
to the low impact on the ad goal and the ad elements
to limit the scope due to page restrictions.
3.3.1 Context and Consumer
The MAEF includes two components that influence
the ad goal directly. First, they split the ‘context’ of
an ad in two types: environmental context and tech-
nology context. The environmental context contains
factors, such as location, time, weather, and events,
that described the environment in which the mobile
device is located at the moment an ad is, or can be
displayed (Grewal et al., 2016). The second type of
context is technology context and is based on the spec-
ifications of the mobile device, such as the size of the
screen and the location (website or application) they
come from. They found that the context in which an
ad is served is very important regarding the behavior
of the consumer towards the ad.
The second component ‘consumer‘ contains infor-
mation about the consumer. Besides the information
of the current state of the consumer in the ‘customer
journey‘, it also contains information about the his-
tory of the consumer and possibly demographic in-
formation (Grewal et al., 2016).
The context and consumer components contribute
to the determination of the goal. The components al-
low an advertiser to specify its target based on data
that is generated by mobile devices. These compo-
nents are not further specified in this project, but are
mentioned in the model since their influence, pro-
vided that they are chosen accurately, can be of great
influence on ad performance (see column A of Fig-
ure 1).
3.3.2 Ad Goal
The ad goal is what the initiator of a mobile campaign
aims for. It determines the target of the campaign.
The MAEF mentions multiple ad goals, however sev-
eral elements are not included in this project. The fo-
cus is on the ‘engagement goal’ and therefore the rest
of the outcome metrics are removed from the frame-
work (see column B of Figure 1).
3.3.3 Ad Elements
The Ad Elements in the MAEF are elements that char-
acterize the look and feel of an ad and from now on
referred to as design characteristics. The characteris-
tics given by Grewal et al. (2016) are the ad medium,
media type, pull versus push, interactive versus static,
and promotional elements.
Ad Medium. Grewal et al. (2016) mentions that a
mobile device can function as different ad medium.
First, the mobile device can be the only screen the
consumer is working with, but can also be the sec-
ond screen besides, for example, a television. In this
project we assume that the mobile device serves the
purpose of the first and only screen and therefore ad
medium is removed from the MAEF.
Pull/Push. In the MAEF distinction is made between
pull and push methods for advertising. With the pull-
based method the consumers pull the advertisement
by opening a specific mobile website or application
(Grewal et al., 2016). The second method is the push-
based method, and relies on the delivery methods
such as SMS. This project is only focused on the pull-
based methods and therefore the pull/push element is
removed from the MAEF.
Promotional Element. An ad might contain a pro-
motional element. This means that an ad can contain
discount offers for the consumer (Grewal et al., 2016).
This element is not taken into account in this project
since the purpose of the ads are more often to create
traffic to the website of the client.
The ad elements described below are included in
the framework, as shown in column C of Figure 1.
Medium Type. The medium type is the channel
through which the ad is served to a consumer. The
types can be a mobile Webpage (site) or a mobile ap-
plication (app). Both are opened by the consumer it-
self. The content of the Webpage or application can
influence the perception of an ad (Grewal et al., 2016).
Since the content of an app is more personalized, the
user installed the app itself (Grewal et al., 2016), we
expect that the ad is more personalized and therefore
more likely to be interacted with.
Proposition 1. An ad that is served in an app elicits
better consumer engagement than an ad served on a
Creative Attributes. Grewal et al. (2016) distin-
guishes four types of creative attributes (IAB Ned-
erland, 2014) in mobile ad: static, dynamic, interac-
tive, and ads containing a video. In the MAEF the
creative attributes are indicated as interactive / static.
The type of creative in an ad can be important regard-
ing how inviting the ad is for interaction (IAB Ned-
erland, 2014). According to de Sa, Navalpakkam, &
Figure 1: MAEF4CE: Mobile Advertising Effectiveness Framework for Customer Engagement.
Churchill (2013) an in-ad animation leads to a nega-
tive impact on user experience than ads that are not
animated. Based on Sa et al. (2013) we expect that a
static ad will elicit the highest consumer engagement
compared to a interactive, dynamic, or video ad due
to the possible negative impact of animated ads.
Proposition 2. A static ad will elicit better consumer
engagement compared to a dynamic ad.
Ad Format. According to a study performed by de Sa
et al. (2013) is an smaller ad preferred over a larger
ad. We expect that large ad sizes elicit better con-
sumer engagement since a large banner size is com-
pletely filling up the device screen.
Proposition 3. A ‘large’ medium type will elicit
better consumer engagement compared to the other
medium types.
Device Specific Ads. The size of the mobile device
limits down the space for mobile ads (Grewal et al.,
2016). Since there are major differences in screen size
of mobile devices it can be useful to create ads that are
specific for certain screen sizes.
Proposition 4. An ad that is screen specific will result
in a higher consumer engagement than an ad that is
not screen specific.
Brand Visibility. Ghose & Todri (2015) concluded
that advertising contributes to an increased interest in
a brand. This ad element researches if the presence of
a brand logo contributes to the recognition of a brand.
According to Flores et al. (2014) does an ad contain-
ing images of the brand of the product positively in-
fluenced attitude towards the brand.
Proposition 5. A visible logo within an ad results in
a higher consumer engagement than an ad without a
3.3.4 Outcome Metrics
The outcome metrics are used to determine the result
of serving a viewable ad to consumers. The metrics
are explained below and shown in column D of Fig-
ure 1.
Viewability. This outcome metric is not described
in the MAEF. Viewability is whether the ad is di-
rectly viewable, without performing an action by the
consumer, or is viewable as a result of the consumer
scrolling down. In this project all the ads used for
analysis are viewable. This means that the ad is view-
able the moment the consumer opens an application
or site. As a result of the guaranteed viewability we
can assume that the ad is seen by the consumer, be-
cause the ad was visible on a consumer’s screen area
(Ghose and Todri, 2015). An ad is considered to be
viewable when at least 50% of the ad is visible for at
least 1 second, according to the Media Ratings Coun-
cil (MRC) (Ghose and Todri, 2015; IAB Nederland,
Interaction. Here, an interaction is considered as a
measurable type of engagement. When an ad is actu-
ally visible to users.
The ratio is a composition of multiple interaction
types. The interactions measured in this research are:
i) installs, ii) clicks, and iii) events.
Installs. An install is an engagement in the form of
an application installation as a result of an interaction
with an ad. When the application is actually installed
an ‘install’ event is registered.
Clicks. Clicks are the simplest form of interaction
of the user and a served ad. When a user clicks on
the ad a click event is registered. This ‘click through’
event often results in the navigation to another (web)
page of the advertiser. In this project a click is cate-
gorized as an engagement.
Events. Besides install and click events other in-
teractions can be elicited, such as dragging and swip-
Quantitative research design is used to determine
the relationship between advertisement characteris-
tics and consumer engagement. Multiple statistical
tests are performed to determine this relationships.
The structure of the methodology, as described in the
Introduction, is according to CRISP-DM. The follow-
ing order is used for the research approach descrip-
tion: Domain Understanding, Data Understanding,
Data Preparation, Modeling, and Results (evaluation).
The last, deployment, phase of the CRISP-DM will be
absent as this paper is an exploitative research without
an implementation.
4.1 Domain Understanding
Two parties can be distinguished in the process of
serving an ad in a mobile device context (see Fig-
ure 2). First the one which provides the ad, the ad-
vertiser, and second, the party that offers an ad space
in an application or site, the publisher. However, be-
tween the advertiser and the publisher are two service
parties that make sure the ad is served in the right
space, the bidding service and the auction service.
When an advertiser is going to use mobile adver-
tising, the ad needs to be served in mobile contexts.
Before a mobile ad, or impression, is served in a mo-
bile application or mobile website, a Real-Time Bid-
ding (RTB) process is performed. The Interactive Ad-
vertising Bureau (IAB) creates and maintains stan-
dards and specifications in order to improve the ad
network landscape and describes RTB as bidding for
individual impressions in real-time (IAB Nederland,
The advertiser is not directly involved in the auc-
tion, but is outsourcing the bidding process to a ser-
vice on the Demand-Side Platform (DSP). Companies
on the DSP compete in auctions to obtain an unique
impression. This means that the advertiser is waiting
until the auction for one unique banner is finished in
order to serve an ad (IAB Nederland, 2014). The auc-
tion is finished when the highest bidder is determined.
The decisions made regarding the participation in the
auction and its result is based on computer algorithms,
so there is no direct human input required (Yuan et al.,
On the other hand, opposite to the advertiser and
the DSP, are the publisher that offers the impressions,
and the Supply-Side Platform (SSP) that conducts an
auction among bidders on the DSP per impression.
4.2 Data Understanding
The data used in the research is made available by a
company operating in the Dutch mobile advertising
landscape, Mobile Professionals (MobPro). MobPro
has build its own bidding server in order to participate
in the RTB process. MobPro is mostly operating on a
DSP. The data that is needed for this research is a re-
sult of three types of information: i) bid request infor-
mation, ii) design characteristics, and iii) engagement
First, each impression has information that was
generated during the RTB process consisting of infor-
mation about the context of the prospective impres-
sion, such as the location of the user and device spec-
ifications. This information is stored in the (1) ‘auc-
tion’ table in Google BigQuery. BigQuery enables us
to perform SQL-queries for data analyses. Second,
some visual information is available of the ad that
is shown during the impression, (2) the ‘view’ table.
These are the design characteristics. The ad is only
considered an impression when the design character-
istics, specified by the advertiser and the designer,
meet the specifications of the request. It is only pos-
sible that a certain design is shown to a user if the
design fits the available ad space. Finally, data that
is generated during the impression is stored in the (3)
‘click’ or ‘event’ table. This is the actual data about
how the ad is perceived by the user and contains in-
formation about an engagement. The first two cate-
gories are constructing the independent variables, the
last one forms the dependent variables.
4.3 Data Preparation
MobPro uses a system to store information about all
the incoming bid requests. A bid request is the call
from the bidding servers that an ad space is available.
The data used in this research is only containing won
bid requests, since only the bids that are won can be
filled with an ad. This means that an incoming bid
request is accepted, bid on, and the bid is won ac-
cording to the highest bid respectively. This process
takes place in the RTB process explained earlier.
Due to the large amount of data the dataset is that
is used is reduced to limit number of bid request.
First, we are using the auction data table (the incom-
ing bid requests) of one day (2017-01-26). Since we
are only interested in the ads that were actually view-
able to users, the auction table is combined with the
table containing the views. Joining the auction table
and the view table results in a table with 3.16 million
won bids on that day. In this step, for ad format and
medium type, the design characteristics are directly
filtered from the auction table. This means that the
total numbers of views can differ per dataset for each
hypotheses. Overall, the number of viewable ads was
more than one million.
In the next step a distinction was made between
the characteristics. For the creative attributes and
brand visibility a list with campaigns was manually
checked to assign the right value for each ad (N =
151). This means that each ad was checked and cat-
egorized for with the right characteristic. Since each
Figure 2: Summary of the Real-Time Bidding process and the involved parties.
ad in the dataset contained a logo or other brand in-
dication, a distinction was made between whether a
logo was visible more than 90% of the time (‘long’)
or less than 50% of the time (‘short’).
For medium type and ad format the following ap-
proach was used. A new table was created for each
for each possible value of a category while combin-
ing the views with the engagement data based on ad
ID. Staying with the example of the ad format, three
tables were created for each ad size and for each ad
size an extra column was added with a ‘1’ or ‘0’ for
indicating the an engagement or not.
Unfortunately, the data for the device specific ads
could not be gathered from the available resources
due unavailability of data and time limit. As a result
that proposition 4 can not be tested.
4.4 Data Modeling
Both independent variables (clicking) and depen-
dent variables (ad characteristics) are categorical data.
First, to test the propositions defined in Section 3.3
null hypotheses and alternative hypothesis are formu-
lated for each proposition. To analyze the hypotheses
the significance level is 0.05 is used. Using the sam-
ple data, we will conduct a chi-square test for inde-
pendence. The statistics are calculated using Python
packages. The following packages are used: pandas
for data preparation and visualization, numpy for pro-
cessing calculations, scipy as statistics library, and
matplotlib for visualization of the results.
In this section the evaluation phase of the CRISP-DM
is described by examine the stated propositions by ap-
plying the previous explained statistics.
Medium Type
The following hypotheses are formulated to test if
medium type has an influence on consumer engage-
ment (see Proposition 1):
. Consumer engagement and medium type are in-
. Consumer engagement and medium are not inde-
The number of degrees of freedom is 4 which
gives critical value of 9.49 at 95% significance level.
The chi square statistic of 373.8271 (n = 1016988)
exceeds the critical value and the P value (0.0) less
than the significance level (0.05), we reject the first
hypothesis. Thus, we conclude that there is a rela-
tionship between medium type and clicking.
Figure 3 shows the relative frequency of the two
medium types. The ‘site’ medium is clicked more of-
ten compared to the ‘app’ medium (see Figure 3).
Figure 3: The distribution of medium type in app and site.
Creative Attribute
The following hypotheses are formulated to test if
the type of creative attribute has an influence on con-
sumer engagement (see Proposition 2):
. Consumer engagement and type of creative at-
tribute are independent.
. Consumer engagement and type of creative at-
tribute are not independent.
The number of degrees of freedom is 6 which
gives critical value of 12.59 at 95% significance level.
The chi square statistic of 42.9243 (n = 306450) ex-
ceeds the critical value and the P value (0.0) less
than the significance level (0.05), we reject the first
hypothesis. Thus, we conclude that there is a rela-
tionship between creative attribute and clicking.
Figure 4 shows the relative frequency of the three
types creative attributes. The ‘interactive’ advertise-
ment is slightly clicked more often compared to the
video’ and ’dynamic’ ads (see Figure 4).
Figure 4: The distribution of creative attribute in dynamic,
interactive, and video.
Advertising Format
The following hypotheses are formulated to test if
advertising format has an influence on consumer en-
gagement (see Proposition 3):
. Consumer engagement and advertising format
are independent.
. Consumer engagement and advertising format
are not independent.
The critical value is 12.59 at 95% significance
level when number of degrees of freedom is 6.
Calculating the chi-square distribution for P(X
2554, 77) = 0 (n = 1321890). The chi-square exceeds
the critical value and the P value (0.0) less than the
significance level (0.05), we reject the first hypothe-
sis. We conclude that there is a relationship between
advertising format and consumer engagement.
Figure 5 shows the relative frequency of the three
ad formats. The ‘large’ ad format is clicked more of-
ten compared to a ‘small’ and ‘medium’ ad.
Figure 5: The relative frequency of the three banner for-
Brand Visibility
The following hypotheses are formulated to test if
brand visibility has an influence on consumer engage-
ment (see Proposition 5):
. Consumer engagement and brand visibility are
. Consumer engagement and brand visibility are
not independent.
The number of degrees of freedom is 4 which
gives critical value of 9.49 at 95% significance level.
Since the chi square statistic of 77.04 (n = 306.450)
exceeds the critical value we reject the first hypothe-
sis. We conclude that there is a relationship between
brand visibility and consumer engagement.
Figure 6 shows the relative frequency of the ‘long’
or ‘short’ visibility of a brand. This means that the fig-
ure shows the percentages of clicks compared to the
total number of views for each specific brand visibil-
ity option.
The ads where the brand is shown less than 50%
of the time are clicked more often compared to the ads
where the brand was visible almost the whole time the
ad was served (see Figure 6).
Figure 6: The clicks in percentages for on (non-)visibility
of a recognizable brand in an advertisement.
All characteristics are not independent with consumer
engagement, which means that they have a relation-
ship with consumer engagement.
Proposition 1 stated that an ad in an app will elicit
better consumer engagement, however, according to
the results advertisements on a mobile site did gen-
erate more clicks. This proposition turned out to be
false. In Proposition 2 we assumed that a static ad
would be clicked more often. However, no static adds
were found on in the data selection. Interactive ads
did elicit the best consumer engagement. It turned
out that an ad with a short brand visibility generated
more consumer engagement compared to being the
brand always visible (Proposition 5). Proposition 3
turned out the be true. The large ads elicits, com-
pared to small and medium formats, more consumer
engagement. At last, short brand visibility receives
more clicks than long brand visibility. Proposition 5
can not be answered as true or false since all ads did
show a logo.
Two of the four tested propositions (1 and 2) were
stated incorrect. First, an ad in a mobile site is more
clicked than an app. So while an app is considered
to be more personal (e.g. having an app is the con-
sumer its own choice), its use is goal oriented since
it will fit the purpose of the app. Therefore, an ad
opposed to browsing on a site, its content might vary
more and ads are thus only additional varying con-
tent. Secondly, since no ‘static’ ads were included in
the evaluation, another creative attribute elicited more
consumer engagement: ‘interactive’ ads. Since the
purpose of interactive ads is to engage interaction this
output seems valid. Additionally, an interactive ad is
relative newly creative type which can result in a lim-
ited coverage of the type in the literature.
7.1 Limitations
The data is only based on one day due to process lim-
itations. So, different days of the week are not taken
into account.
No information is available of the actions of the
consumer after the click-out. This means, that we
could not state anything on how effective the adver-
tisement was regarding the engagement with the ad-
vertiser outside the advertisement.
Additionally, influences of other advertising me-
dia for the same campaign at the same time is not
taken into account. For example, when a campaign
for a certain brand is served on both mobile and tele-
vision, brand awareness can be influenced by multiple
7.2 Further Research
The ‘context’ and ‘consumer’ components of the
MAEF are not tested in this project, but are highly in-
teresting within this scope. An additional study could
be the combination of the context or consumer char-
acteristics regarding ad performance.
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