2023 27th International Conference Information Visualisation (IV)Visualization System to Analyze Browsing Trendsof Internet Video Advertisements
Rika Miura

Hayato Ohya

Takayuki Itoh

Ochanomizu University
Tokyo, Japan
g1820536@is.ocha.ac.jp

Septeni Japan, Inc.
Tokyo, Japan
hayato.oya@septeni.co.jp

Ochanomizu University
Tokyo, Japan
itot@is.ocha.ac.jp

Besides, the target setting of Internet advertising is notalways an alternative choice such as ”female or male” but
can be logical combinations of multiple attributes such as”female and 30s”. It is often a bothering task to ﬁnd out thetrends of Internet advertising with combinations of attributes.
In other words, it is still an open problem to visualize andanalyze advertising data with combinations of advertising
attributes. In this paper, we comprehensively evaluated video
advertising with abandonment rate, click through rate, andconversion rate and developed the visualization system whichtakes into account the combination of advertisement attributes.
This paper introduces our observation of the advertisement
data by using this visualization system. The main contributionsof this study are as follows.
1) Analysis of the advertisement data by evaluating comprehensively video advertisements with the three indicators below.
• Abandonment rate (Percentage of video playback
stopped in the middle)• Click through rate (Percentage of ad clicks)• Conversion rate (Percentage of app installations,product purchases, contract signings, and so on)2) Development of a visualization system which takes into
account the combination of advertising attributes foradvertising data.
The remainder of this paper is organized as follows: Section
2 describes related research and Section 3 describes the proposed method. Section 4 discusses the results of the analysis.
Section 5 summarizes this paper and discusses future issues.

Abstract—The market size of Internet advertising continues togrow. The creation and delivery of effective Internet advertising
will become increasingly important in business. On the other
hand, it is difﬁcult to achieve an advertising effect if thetarget setting is inappropriate because Internet advertising istargeted and delivered to a speciﬁc group. Therefore, this study
aims to discover effective Internet advertising target settings by
evaluating Internet video advertising data with abandonment
rate, click through rate and conversion rates and by developing
a visualization system that takes into account combinations ofadvertising attributes. As a concrete example, we introduce theresults of visualizing 224,355 anonymized records of advertisements distributed by Yahoo! JAPAN Ads and 89,400 anonymized
records of advertisements distributed by LINE Ads.
Index Terms—Visualization method, video advertising, abandonment rate, click through rate, conversion rate

I. I NTRODUCTION
Advertisements on the Internet continue to grow their market
size year after year. According to the report by Dentsu Inc.
[1], in 2021, Internet advertising expenditures in Japan reached
2.7 trillion Japanese yen and exceeded 2.4 trillion Japanese
yen of the total advertising expenditures of the four mass
media, i.e., newspapers, magazines, radio and television. In
particular, video advertising grew signiﬁcantly to 512.8 billion
Japanese yen, 132.8% up from last year, and surpassed 500
billion Japanese yen for the ﬁrst time. Due to this background,the creation and delivery of effective Internet advertising will
become increasingly important in business.
Matsumoto et al. [9] analyzed marketing data by using clustering and principal component analysis to identify consumer
characteristics. They clariﬁed what kind of personality andconsumption value characteristics consumers are inﬂuenced byadvertisements. The results suggested that effective advertising
methods differ from advertisement to advertisement. Zenget al. [12] discovered the characteristics of advertisements
disliked by users and preferred by users with cluster analysis. The above studies proved the necessity of selection of
the appropriate delivery methods for each advertisement andcreating advertisements that are preferred by consumers inorder to promote effective advertising activities. In addition,
Internet advertisements are delivered to speciﬁc groups unlike
television and other media, and therefore, the expected effectcannot be achieved unless the appropriate target is set for eachadvertisement before delivery.

2375-0138/23/$31.00 ©2023 IEEE
DOI 10.1109/IV60283.2023.00011

II. R ELATED W ORKThis section introduces existing studies for advertising datavisualization and utilizing data for the purpose of improving
advertising effectiveness.
A. Methods for visualizing advertising data
The video advertising data used in this study is multidimensional data. There have been various discussions on effective
multidimensional data visualization methods. There have been
several studies of visualization analysis speciﬁc to advertisingdata. Peng et al. [11] proposed an interactive analysis tool
called TargetingVis. This tool enables advertising analysts to1

III. P ROCESSING FLOW OF OBSERVATION AND

discover ideas to make advertising more effective by visualizing targeted advertisement delivery data and examining
the delivery patterns of advertisers. TargetingVis uses themethod for visualizing target combination patterns in the same
way as in our study and suggests that this method is usefulfor discovering effective advertisement settings. Liu et al.
[8] proposed MulUBA, a multi-view interactive system. Thisvisualization system helps the analysis of online shopping
behavior and attributes of customers by advertising analysts
by combining multiple visualizations. These studies use clickthrough rate and conversion rate as indicators of advertising
effectiveness but do not use advertisement abandonment rate.
On the contrary, our study aims to discover effective advertisement settings by comprehensively visualizing the browsing
trends of video advertisements based on the three indicatorsof abandonment rate, click through rate, and conversion rate.

VISUALIZATION OF ANNOTATION TASKS

A. Dataset
In this study, we used anonymized datasets in the same format
as the video advertisement data actually delivered on Yahoo!
JAPAN Ads and LINE Ads.
B. Data Preprocessing
We created feature values for abandonment rate, clickthrough rate, and conversion rate. This section describes eachindicator in detail.
1) Video ad withdrawal rate: We evaluate advertisements
by using the abandonment rate of video advertisements in thisstudy.We calculated the six variables R1 - R6 listed below byusing the four variables in Table 1 to generate clusters withsimilar trends in video ad abandonment and analyze the data.
The six variables R1 - R6 represent the video ad abandonment
rates between each time point.

B. Utilization of Data for the Purpose of Improving Advertising Effectiveness
There have been many studies aimed at improving advertising effectiveness, in addition to the studies on visualization
analysis methods as described in section 2.1. One of therepresentative researches which approach the improvementof advertising effectiveness is the study of click throughrates prediction(CTR). Chen et al. [3] proposed an end-to-end
integrated deep network to predict the CTR of advertisements.
Zhou et al. [13] proposed the new model called DIEN for CTR
prediction which takes into account the changing trend of theuser interests. There exists also a lot of research not only CTR
prediction but also analyzing advertisements effectiveness byusing advertising data. Azimi et al. [2] analyzed the effectof advertisements appearance on users to assist creating highquality creative. Kitada et al. [6] proposed a framework forcorrectly evaluating the effectiveness of advertising creatives
by learning on unbalanced data using multi-task learningand a conditional attention mechanism. Ishikawa et al. [5]discussed the data which provide advertisers with clear quantitative indicators among ten types of basic advertisement
data obtained from individual advertisements submitted toYahoo! promotion advertisements. As a result, they founda combination of indicators that are signiﬁcantly involvedin conversion prediction. Zeng et al. [12] discovered whatkind of advertisements make consumers uncomfortable bycluster analysis. They revealed that the clusters with the goodadvertisements were the ones that were simple, well-designed,
and relevant to the interests of customers. Meanwhile, theyfound that the clusters which attracted the bad advertisements
had list articles and political ones. Minjung et al. [10] analyzed
differences in online behavior by consumer characteristics withclustering methods. Cheung et al. [4] investigated sizes andtypes of advertisements which might cause users to leave
the advertisements. As mentioned above, studies on effective
advertisement production support have been conducted fromvarious perspectives; on the other hand, this study aims to
improve advertisement effectiveness from the approach ofvisualization analysis.

TABLE IF OUR VALUES USED TO CALCULATE THE ABANDONMENT RATE .
Vp25
Vp50
Vp75
Vp95

Number of times the video was viewed up to 25% of the video play time
Number of times the video was viewed up to 50% of the video play time
Number of times the video was viewed up to 75% of the video play time
Number of times the video was viewed up to 95% of the video play time

Vp25 −Vp50
V
−V
V
−V
, R2 = p25Vp25 p75 , R3 = p25Vp25 p95 ,
Vp25
V
−V
V
−V
V
−V
R4 = p50Vp25 p75 , R5 = p50Vp25 p95 , R6 = p75Vp25 p95

R1 =

(1)
Click through rate / Conversion rate: The factors whichcause users to leave video advertisements can be divided into
positive and negative factors. Negative abandonment refers to
the fact that the advertisement does not have an advertising
effect and the user simply leaves the video advertisement.
On the other hand, positive abandonment occurs when theadvertisements were clicked. It is difﬁcult to distinguishwhether the abandonment is a positive or negative one if
we evaluate advertisements based only on abandonment rates.
In general, Internet advertisements are often evaluated by
the click through rate (CTR) which is the number of clicks
relative to the number of advertisements displayed, and theconversion rate (CVR) which is the rate at which the purposeof advertisement delivery is achieved after an advertisement
is clicked. Therefore, we considered that it would be possible
to distinguish between positive and negative abandonment
factors by combining the abandonment rate, click throughrate, and conversion rate of video advertisements. This study
comprehensively evaluates advertisements by using the abandonment rate, click through rate, and conversion rate of video
advertisements.
CT R = clicks/impressions × 100
CV R = conversions/clicks × 100

2

(2)
(3)

C. Dimensionality reduction and clusteringsame cluster. A unique color is assigned to each clusterin the scatterplots.
2) One of the clusters plotted in Visualization 1) is selected,
and the click through rate of the advertisement data
belonging to this cluster is visualized in the heatmap.
In the visualization screen shown in Fig. 1, the verticalaxis depicts the attribute of the advertisement genre andthe horizontal axis depicts the attribute of the advertiser
name. As shown in Fig. 3, red color is assigned to
combinations of attributes with high click through rates,and purple color is assigned to combinations of attributeswith low click through rates.

The six variables R1 to R6 related to the abandonment
rate of video advertisements calculated in section 3.2.1 were
mapped from a six-dimensional space to a two-dimensional
space by applying a dimensionality reduction method. In addition, by applying a clustering process to the six variables R1
to R6 , advertisements with similar abandonment tendencies
belong to the same cluster. In this study, we observe whatcharacteristics are observed among the advertisements whichhave similar trends in video ad abandonment rates. Principal
component analysis was used as the dimensionality reduction
method and the k-means method was used as the clustering
method.
D. Visualization
We applied the following two visualizations for multidimensional training data.

Fig. 3. Color map used in the heat map.

3) The results of visualizing the conversion rate for eachadvertiser are displayed as a bar graph. The selected data
items are highlighted in the bar graph by selecting data
items in the heatmap in Visualization 2).
4) The details of the data items displayed in Visualization
2) are displayed in the data table.
IV. R ESULTS
This section describes several results and discoveries obtained from the visualization. As an example, this paper
presents the results of visualization and analysis of 224,355
anonymized records of video adcertisement dataset distributed
by Yahoo! JAPAN Ads and 89,400 anonymized records ofvideo advertisement data distributed by LINE Ads.
Fig. 1. Overall screen shot of the visualization system.

A. Results of visualization of video advertisements distributed
by Yahoo! JAPAN Ads
We calculated the six variables R1 to R6 related to thevideo abandonment rate by using the method described in
Section 3.2.1 with the anonymized data from the video ad
data distributed by Yahoo! JAPAN Ads. Fig. 4 is the resultof applying principal component analysis and clustering to
the six values and then visualizing them in a scatterplot.
Table II shows the results of calculating the eigenvectors of
the ﬁrst principal component (PC1) and the second principal
component (PC2). PC1 has higher absolute values of R1 to
R3 , while PC2 has relatively higher absolute values of R4
to R6 . Therefore, we found that PC1 is the abandonment
rate in the entire video and PC2 is the abandonment rate in
the second half. In the ﬁrst principal component of Fig. 4,
the further to the left, the advertisement data which have thehigher abandonment rate is plotted. Further to the right, theadvertisement data which has the lower abandonment rate isplotted. Fig. 5 shows the heatmap visualization of the clickthrough rates of advertisements belonging to Cluster 1 which
has the lowest abandonment rate from video advertisements
out of the eight clusters visualized in the scatterplot in Fig. 4.
The vertical axis depicts the genre of the video advertisement

Fig. 2. Data table displayed by switching tabs in Fig. 1(3).

In this study, we developed the visualization system byusing Bokeh, a visualization library for Python. We used
Python 3.9.6 and Bokeh 2.4.3. Fig. 1 shows the overview ofthe visualization system in this study. By switching tabs in thevisualization screen at the bottom of Fig. 1, the data table canbe displayed as shown in Fig. 2. The four visualizations (1)to (4) are described below.
1) The advertising data is visualized in the scatterplots
after applying dimensionality reduction and clustering
methods described in section 3.3. The advertisements
which have similar abandonment behaviors belong to the3while the horizontal axis depicts the advertiser’s name. This
result shows that there exist differences in click through rates
even among advertisements with low video ad abandonment
rates. Speciﬁcally, advertisements with high click through rates
correspond to those with ”genre_8” and ”advertiser_12” andthose with ”genre_4” and ”advertiser_4”.
Next, we selected cluster 3 which has a high abandonment rate of video advertisements. Fig. 6 shows the results
visualized in the heatmap. The ﬁgure shows that there exist
combinations of attributes with high click through rates, suchas ”genre_13” and ”advertiser_48, even in the advertisement
data with high advertisement withdrawal rates. It suggests thatadvertisements with such combinations of attributes have ahigh number of times in which abandonment occurred as aresult of being clicked. Therefore, advertisements with these
combinations of advertising attributes are connected to clicks
and are effective advertising activities.

the abandonment rate, click through rate, and conversion rate,by looking at the visualizations presented in this paper.

Fig. 5. The result of visualizing the click through rate of ad data which
belongs to Cluster 3 in Fig. 4 by using a heatmap. (vertical axis: advertisement
genre. horizontal axis: advertiser name.)

Fig. 4. The results visualized in scatter plots by applying principal component
analysis and clustering by using dummy data in the same format as theadvertising data delivered by Yahoo! JAPAN Ads.

Fig. 6. The result of visualizing the click through rate of ad data which
belongs to Cluster 3 in Fig. 4 by using a heat map. (vertical axis: advertisement
genre, horizontal axis: advertiser name).

TABLE II
E IGENVECTOR VALUES FROM PRINCIPAL COMPONENT ANALYSIS BY
USING DUMMY DATA IN THE SAME FORMAT AS THE AD DATA DELIVEREDBY YAHOO ! JAPAN A DS .

R1
R2
R3
R4
R5
R6

PC1
-0.511-0.554-0.555-0.287-0.1530.134

B. Visualization results for video advertisements distributed
via LINE Ads
Fig. 9 shows the visualization results with anonymized
data in the same format as the video ad data distributed byLINE Ads. Table III shows the eigenvectors of the principal
component analysis. PC1 has high absolute values of R1 to R5
while PC2 has relatively high absolute values of R6 . In otherwords, PC1 corresponds to the abandonment rate in the entire
video while PC2 corresponds to the abandonment rate in thelatter half. Further to the left in Fig. 9, the lower abandonment
rate from video advertisements is plotted in the advertisement
data. Further to the right, more advertisements with highabandonment rates from video advertisements are plotted.
Fig. 10 shows the results of a heatmap visualization of the clickthrough rate for advertisements belonging to Cluster 4 whichhave the lowest abandonment rate. The vertical axis depicts

PC2
0.241
0.083
-0.008-0.504-0.613-0.553

Then, we selected the combination of ad attributes withhigh click through rates among the data items visualized inFig. 6, and highlight the corresponding data items on the bar
graph visualizing the conversion rate. From the visualization
result shown in Fig. 7, we found that this combination oftarget attributes has a large click through rate and is also
linked to conversions. This result demonstrates that users cancomprehensively analyze advertising effectiveness in terms of4

Fig. 9. The results visualized in scatter plots by applying principal component
analysis and clustering by using dummy data in the same format as theadvertising data delivered by LINE Ads.

Fig. 7. Select the combination of attributes with a high click through rate
among the data belonging to cluster 3 which has a high abandonment rate.

TABLE IIIE IGENVECTOR VALUES FROM PRINCIPAL COMPONENT ANALYSIS BY
USING DUMMY DATA IN THE SAME FORMAT AS THE AD DATA DELIVEREDBY LINE A DS .

R1
R2
R3
R4
R5
R6

Fig. 8. The result of visualization highlighting the advertiser’s conversion
rate for the selected data in Fig. 7.

the advertisement genre while the horizontal axis depicts theadvertiser name. These visualization results show that the data
distributed by LINE Ads are dominated by advertisements withrelatively high click through rates.
Fig. 11 shows the visualization results after changing theattributes of the vertical and horizontal axes in the heatmap.
The vertical axis depicts the attribute of the advertisement subgenre while the horizontal axis depicts the attribute of thetargeting type in Fig. 11. Table IV shows a description of thetargeting type attributes. The visualization results show thatthe combination of advertisement attributes ”sub_genre_4”
and ”Look Alike” has the highest click through rate. This
result demonstrates that our visualization system enables users
to discover the advertisement settings with the highest clickthrough rate for each combination of the two attributes bychanging the attributes on the vertical and horizontal axes of
the heatmap.

PC1
0.408549
0.475229
0.495529
0.402646
0.409860
0.177725

PC2-0.428766-0.294864-0.114966
0.136094
0.477465
0.685203

rates. Such video advertisements may have abandonment
caused by a click.
• Different combinations of advertisement attributes among
the platform may occur high click through rates.
We have three future issues. The ﬁrst is to select the mostappropriate dimensionality reduction and clustering method inthis study. The visualization results vary greatly dependingon which dimensionality reduction and clustering method areapplied [7]. Therefore, it is necessary to evaluate whether
the currently used method is appropriate. In the future, wewould like to compare more diverse methods and seek theappropriate method for this study. The second is to improve
the interactivity of the visualization tools. The current visualization tool does not allow the interactive selection of data
items. We would like to enhance this tool to allow users tofreely select data items so that users can archive more effective

V. C ONCLUSION AND F UTURE WORKWe developed a visualization system for the evaluation ofadvertisements comprehensively by combining the abandonment rate, click through rate, and conversion rate of video
advertisements. This paper introduced visualization results
considering the combination of advertisement attributes. As
a result, we found the following trends:• There are video advertisements with high click throughrates among video advertisements with high abandonment

TABLE IVE XPLANATION OF TARGETING TYPE .

RT (Re Targeting)
LA (Look Alike)
AT (Audience Targeting)5

Advertisements that display
once-seen products, etc.
Advertisements that display items
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Fig. 10. The result of visualizing the click through rate of the advertisement
data belonging to cluster 4 in Fig. 9 by using a heat map.(vertical axis: ad
genres, horizontal axis: advertiser name)Fig. 11. The result of visualizing the click through rate of the advertisement
data belonging to cluster 4 in Fig. 9 by using a heat map.(vertical axis: ad
subgenres, horizontal axis: targeting types)

visualization analysis. The third is to apply parallel coordinateplots to visualization (3). Parallel coordinate plots will allow
users to interpret the relationships among data items without
losing information on multidimensional data, thus enabling
more detailed data analysis.
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