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Location-based Data Reveals Train Station Info
Dentsu’s OOH advertising evolves with GroundTruth’s mobile data

Within the digital economy there is an expanding use of such data-related terms as data marketing, data driven, big data, and data management. It is no surprise that these data-related terms are increasingly being used today by the advertising industry as well, particularly in such areas as out-of-home (OOH) advertising, which includes outdoor and transit ads. Below, Kei Hamada from Dentsu’s Out of Home Media Services Division discusses the evolution and possibilities of outdoor and transit advertising, particularly with respect to the use of audience data.

Pertinent data

Relevant questions to consider when discussing data are what kind of data people want, what they want to find out with quantitative data, and what they want to change for the better by using such data.

Answers to these questions initially may not be clear, but that changes once we understand what data can show.

To give a concrete example, were I asked what kind of data I should appreciate having, I would not hesitate to say that I want data that reveals how my husband and our son manage their time of a morning.

The reason is this. Every morning, my husband drops off our son at nursery school. Often, even though my husband is often running late, he has to keep reminding the child to hurry up if he does not want to be late for school.

One example of how my son wastes time of a morning is when he will sit at the table daydreaming, rather than immediately start eating the omelet that is in front of him. My husband is also in the habit of wasting time, in that he spends far too much time taking a long hot shower, even though he barely has any hair on his head to wash.

If both son and husband were to cut their activities by five minutes, they would not have to rush in order to leave the house on time. I really believe I could help them manage their time better had I the right data. But that’s enough of a digression from me.

Indeed, in today’s world, it’s all about data, and OOH advertising is no exception. Below I discuss what can be learned by using mobile data, which has yet to be applied to OOH advertising.

Passenger traffic-based comparison of two Tokyo train stations

Passengers Who Use Otemachi Station

The graph above shows the flow of train passengers who use Otemachi Station in central Tokyo each hour of the day from Monday to Sunday in a particular week. The Y axis represents the number of people, and the X axis shows the times between 5:00 am and 11:00 pm. The weekdays are represented by five shades of turquoise, while Saturdays and Sundays are shown in two shades of gray.

From the graph one can see that the flow of people is almost the same each day from Monday to Friday, while there are far fewer people on weekends. That is because the station is situated in an office district that is home to the head offices of many financial and trading companies. Just like clockwork, the area’s employees go to and from work at the same times, Monday to Friday.

Passengers Who Use Roppongi Station—A

The second graph (above) shows data for Roppongi Station in Tokyo. Again, the Y axis represents the number of people, and the X axis shows the hours, from 5:00 am to 11:00 pm. Unlike at Otemachi Station, the flow of people is somewhat irregular from Monday to Friday, and there are almost as many people using the station on the weekend as on weekdays.

Passengers Who Use Roppongi Station—B

The two sections in the red boxes in the above graph are interesting. Box 1 shows slightly higher numbers of users in the early hours of Saturday and Sunday. This is because people caught an early train home after a night on the town on the Friday and Saturday, since the station is located in an area of the city known for its vibrant nightlife.

Box 2, meanwhile, shows an increase in the number of people using the station in the late hours of Friday evening, as the time for the last trains approaches.

The data for the above three graphs was provided by GroundTruth, Inc., a global provider of location-based data from mobile devices. The company’s proprietary Blueprints technology allowed information about people’s locations to be tracked within precisely specified areas around the train stations used above. For information about Dentsu’s collaboration with this company see (Japanese only): http://www.dentsu.co.jp/news/release/2018/0314-009486.html.

Data that confirms and illuminates

People tend to think that data provides only previously unknown information. But it also verifies—often unexpectedly—hunches and speculation.

Since data can provide quantitative proof of things suspected of being true, it allows us to create ad campaigns with confidence based on such evidence. That is one of the really valuable aspects of quantitative data.

Until recently, OOH advertising in Japan did not have the means to acquire data about people’s movements on a daily and hourly basis, such as the information presented in the above graphs. That is because in panel surveys, it has been difficult to get large enough samples of people for the targeted train stations and lines. But the difficulty can be overcome by using mobile data.

Advances in technologies for acquiring location information via GPS and Wi-Fi, and the popularity of smartphones—which has vastly increased the number of people using location information—have helped make it possible to acquire such data.

Here I have presented data about daily and hourly flows of people at two train stations, but what would the same data look like were we to break it down by gender, age, and other demographic factors?

As the next graph shows, using the same source of mobile data, we can see the proportion of men and women using Otemachi Station and Roppongi Station. Both stations are used by men more than women, and the proportion of men and women for each station does not differ greatly.

Looking at the next set of graphs, however, we can see the Monday-to-Friday flow of Roppongi Station users, broken down by gender and age. For each weekday, those who used the station most during the day are women, between the ages of 25 and 34. I had imagined that the greatest number of users would be young men.

This result is even more pronounced in the following graphs for Saturdays and Sundays, when women between the ages of 25 and 34 far outnumber other categories of users.

The results are not what I had expected, but are these categories of women really the most numerous? By showing the data in the form of the bar chart below, it can be seen that women between the ages of 25 and 34 accounted for 19.9%—the highest ratio—of those who use Roppongi Station.

That was not apparent from the previous graph, which just gave a breakdown of users by gender. We get a different perspective again if we examine the graphs, above and below, that display the more detailed hourly data.

Previously, perhaps, local OOH planners may have assumed that transit advertising should target men, since they believed the majority of Roppongi Station users to be men between the ages of 25 and 34 and that, by extension, advertising should focus on products aimed at this cohort.

Yet, based on mobile data, we see that there are more women using the station than men in the 25-to-34 age group.

This shows us that, rather than make decisions on the strength of gender categories, we would understand more about targeted consumers and, thus, be in a position to plan much more effectively were we to examine data taking into account other demographic factors such as age.

Data helps confirm shared facts

Although we did not immediately discover that young women are the most numerous users of Roppongi Station, some people probably would have assumed that from the outset. Indeed, such observations depend on the person. Among people familiar with the area, some are under the impression that most of the station’s users are women, while others believe they are men.

To settle the issue and establish it as a shared fact, we can use Big Data, including mobile data, which has so greatly benefited marketing.

We have yet to determine all of the reasons for which women travel to Roppongi Station. Were we to know more about the facilities and businesses near the station, the amount of time women spend there, as well as other details, we would have a fuller understanding of what draws women to the station. Do they, for example, work there, stop by to meet friends, or visit the area to shop?

Although I have assumed in my discussion that the data I have used is accurate, it is impossible to fully comprehend exhaustive data on a train station without overlooking something. Therefore, when planning on the basis of Big Data—particularly mobile data trends—instead of surveys such as those used in the past, perhaps we should look at OOH advertising from different perspectives.

Some data confirms what was suspected of being true, other data illuminates what was not known, and still other data helps establish shared facts.

Thus OOH advertising teams that look to such empirical data will develop slightly different approaches compared with those that base their work on general assumptions and hunches.

I believe that all of you currently involved in conducting ad campaigns recognize that the place of OOH advertising within the realm of integrated marketing is changing through the use of data.

The genuine value of mobile data is that it can be integrated with online- and television-related data. Furthermore, mobile data is not exclusively limited to OOH advertising—it is also relevant for the planning of full-funnel marketing campaigns and advertising distribution.


Kei Hamada / Project Manager / Out Of Home Media Services Division

Kei Hamada

Project Manager
Out Of Home Media Services Division

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