1Analysis of Relationship Between Non-Identifiable
TV Viewing History Data and Web Search Trends
Hiroki Matsuda†,‡ , Taichi Sakakibara† , Daiki Mayumi‡ , Yuki Matsuda‡ , and Keiichi Yasumoto‡† Yomiuri Telecasting Corporation, Osaka, Japan, {hiroki.matsuda, taichi.sakakibara}@ytv.co.jp‡ Nara Institute of Science and Technology, Nara, Japan, {mayumi.daiki.mb9, yukimat, yasumoto}@is.naist.jp

Abstract—Non-identifiable viewing history data can be collected from internet-connected TVs, and it is possible to grasp
information such as IP addresses and viewing times without
identifying individuals. Yomiuri Telecasting Corporation has
collected data from approximately 3.5 million TV units, but it
has been considered challenging to find new value from thesedata alone. Therefore, in this study, we combined non-identifiable
viewing history data with internet search data, such as Google
Trends, to analyze the impact of TV commercials on viewers’internet search behavior. As a result, we found that as thenumber of commercial broadcasts increased, search behavior also
increased, and we were able to identify the broadcast times ofcommercials that efficiently led to search behavior.
Index Terms—TV, Television, TV viewing data, Big data,Viewer behavior, Visualization, Internet search, IoT

I. I NTRODUCTION
Non-identified viewing history data refers to the data collected by broadcasters in a format that does not identify
individuals, and it is expected to be utilized as big data toenhance advertising value. However, although this data is not
personal information, it can capture individuals’ preferences
and interests, making privacy considerations crucial [1]. In
comparison to internet advertising, TV advertising is said tolag behind in terms of effect visualization and analysis, as wellas the ability to conduct targeted advertising.
In related studies, analysis of viewing data using TVs manufactured by Toshiba was conducted to determine “what typesof viewers select which programs” [2], and clustering basedon viewing patterns were performed [3]. Additionally, studies
have been conducted on the relationship between the frequencyof exposure to TV commercials and their effectiveness indriving website visits [4].
In this study, we focused on the post-viewing internet behavior of TV commercial (CM) viewers and analyzed three typesof data: non-identified viewing history data, CM broadcasting
records, and internet search data. The results demonstrated
that the broadcasting of TV commercials influences viewers’internet search behavior. Furthermore, it was revealed that the
impact of viewership numbers on internet search trends is
relatively minimal, and specific periods that efficiently connect
viewers to internet search activities were identified.
II. DATASETS
Here, we describe three different datasets for data analysisin this paper. The relationship between those three datasets(A), (B) and (C) are illustrated in Fig. 1.

(A) Non-IdentifiableTelevision Viewing
History Data
3.5 million TVs
in the Kinki region

Internet①TV CM viewing(C) Internet Search Data
②SearchCM
Meta

Analysis:
Visualization of CM effects(B) TV Commercial
Broadcast
Records Data

Fig. 1. Analysis Summary Chart

A. Non-Identifiable Television Viewing History Data
In this study, we utilize non-identifiable television viewing
history data collected by Yomiuri Telecasting Corporation(hereafter YTV). This dataset consists of approximately 3.5
million television sets connected to the internet in the Kansai
region of Japan, excluding those that have opted out. It includes information such as IP addresses, TV device IDs, postal
codes, viewing start and end times, and TV manufacturer IDs.
The data spans from April 2020 to March 2021.
B. TV Commercial (CM) Broadcast Records Data
For our analysis, we employ actual CM broadcast records
provided by YTV. These records are outputted in CSV format
from YTV’s sales broadcasting system. The dataset containsinformation such as sponsor names, sponsor industries, CMnames, broadcast start times, and CM durations. The data
covers the period from April 2020 to March 2021.
C. Internet Search Data
One of the main user behaviors that we focus on in thisstudy is internet search activities. We utilize Google Trends,
a service provided by Google, which allows us to analyze thesearch volume for specified regions and search terms within aspecific time period. The search volume represents the relative
amount of searching on the Google platform. In this paper, weset Japan as the search region and specify “{sponsor name},”“{sponsor name} + CM,” and, if applicable, “{product name}”as the search terms. The data collection period spans one week
before the start of the CM broadcast to one week after thebroadcast’s end.

2Fig. 3. Distribution of CM broadcast time(per hour) and search volume

Fig. 2. Distribution of CM broadcast time(per hour) and number of TV viewers

TABLE IE XAMPLE OF COMMERCIAL BROADCAST TIME AND SEARCH VOLUME
MEASUREMENT PERIODCM Broadcast
Date

Number ofTV viewers

Measurement period

Jan 1st , 12:05:30

100,000180,000
60,000

Jan 1st , 12:00 – 12:59
Jan 1st , 20:00 – 20:59
Jan 2nd , 01:00 – 01:59

No12
3

Jan 1st , 20:59:04
Jan 2nd , 01:03:57

III. A NALYSIS
We analyze datasets: Non-Identified Viewing History Data,
CM Broadcasting Records Data, and Internet Search Data, toinvestigate the effects of CM on internet search activities. The
overall picture of the analysis is shown in Fig. 1.
A. Methodology
Firstly, we investigated the distribution of CM viewing numbers in relation to the CM broadcasting time and the numberof TVs tuned in, focusing on hourly CM broadcasting time
and the corresponding viewing numbers in the Kansai region.
Next, to understand how internet search volumes vary basedon CM viewing numbers, we investigated the distribution of
search volumes in the vicinity of each CM, taking into accountthe viewing numbers for each CM. The viewing numbers
were measured during the CM broadcasting time, while thesearch volumes were measured at the rounded-down minuteof the CM’s broadcasting date and time. Table I provides anexample of the measurement periods. Lastly, to identify thebroadcasting time slots of TV commercials that efficiently leadto internet searches, we categorized each CM’s broadcastingtime into one-hour intervals and examined the distributionof search volumes per 10,000 viewing numbers. Similar tobefore, the viewing numbers were measured during the CM
broadcasting time, while the search volumes were measured at
the rounded-down minute of the CM’s broadcasting date andtime.
B. Results
Fig. 2 shows the distribution of CM broadcasting times
and corresponding viewing numbers for a food manufacturer’s
CM. It confirms that viewing numbers are lower during latenight hours and increase from morning to evening. It isevident that prime time (The period that highly anticipated TV

Fig. 4. Distribution of search volume per 10,000
viewing units and CM broadcast time period

programs will be broadcasted. In Japan, it is between 19:00
and 22:00.) is the most efficient period to reach a large numberof viewers when seeking broad recognition for CM broadcasts.
Fig. 3 shows the distribution of CM broadcasting times andthe volume of internet searches for the same manufacturer’sproducts. Unlike in Fig. 2, it can be observed that prime time
does not necessarily result in higher search volumes. Specifically, even during late-night hours, which are considered tohave lower CM value, the search volumes are comparable to or
higher than those during prime time. Finally, Fig. 4 shows thedistribution of search volumes per 10,000 viewing numbers
by categorizing each CM’s broadcasting time into one-hourintervals. Similar to before, it confirms that late-night hours
and morning/daytime periods with lower viewing numbers are
more efficient in leading to search actions compared to primetime.
IV. D ISCUSSION & C ONCLUSIONAs shown in Fig. 2, it is evident that CMs broadcast during
prime time receive higher viewing numbers, indicating a larger
reach. However, as demonstrated in Figures 3 and 4, the timeslots following TV CM viewing that lead to search actions are
not necessarily during prime time but rather during late-night
hours and morning/daytime periods. This can be attributed to
the fact that late-night hours and morning/daytime are more
conducive to “second-screen viewing” when watching TV.
These findings indicate that even during time slots consideredto have relatively lower advertising value, such as late-night
hours and morning/daytime, viewers’ internet search behavior
is more efficiently connected to CMs than during primetime. In future work, we will incorporate multiple telecasting
corporations’ data for more comprehensive analysis.
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1P203–1P203, 2018. (In Japanese)[4] T. Kato, and K. Tsuda, “Validation of Three-Hit Theory on Television
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