Beyond the Pandemic: The Current State of OOH—A DX-Based Perspective [2]A New Metric for Value! Visualizing "OOH Buzz"
Have you ever spotted an interesting ad in town, snapped a photo with your phone, and sent it to a friend? Or posted on social media saying, "Look what I found!"? Or perhaps you've seen posts about OOH (Out Of Home: outdoor and transit advertising) on X (Twitter).
Dentsu Inc. Out-of-Home Media Division has an "OOH Buzz Research Team," which includes members from the Data & Technology Center and group company CARTA COMMUNICATIONS. This team uses social listening tools to measure the effectiveness of numerous OOH campaigns and compiles reports.
In recent years, as OOH media is increasingly chosen to generate buzz around products and services, we began wondering if certain conditions are necessary for buzz to occur. Understanding these conditions could potentially increase the success rate of creating buzz, even for low-budget campaigns or creative work without celebrities. This article explores the relationship between OOH and buzz, seeking hints for creating new value in OOH.
Many OOH ads make you want to take a photo and share it with someone
OOH is often used for impactful advertising, featuring eye-catching, surprising elements. Examples include train interiors completely covered in one company's ads, entire station spaces in Shinjuku or Shibuya being taken over, or special ads that change depending on the viewing angle... There are many OOH pieces that make viewers instinctively want to take a photo and share it with someone.
In a large-scale survey conducted by Dentsu Inc. in 2022, we asked: "When do you feel like taking photos or videos of transit or outdoor ads? (Select all that apply)." As shown in the graph below, the results ranked in order of score: "When the content is interesting," "When it features people/things/events I like," "When the content is memorable/impactful," and "When I like the content." This confirms that people are most likely to spontaneously take photos when the content is interesting or makes a strong impact.
Buzz on X (Twitter) also adds value to OOH
Checking actual X (Twitter) posts reveals OOH frequently becomes a topic of discussion. For example, people take photos of ads they find interesting and post them, or visit locations featuring ads with their favorite celebrities as if going to a photo exhibition, taking pictures and sharing them. We also see users with similar interests discussing OOH campaigns on X (Twitter).
Furthermore, even if someone couldn't see the OOH locally, they can view it on their personal smartphone or PC via X (Twitter). This led us to conclude that buzz generated on X (Twitter) can also be considered part of OOH's value.
However, OOH encompasses diverse media such as digital signage, billboards, train interior hanging ads, and station posters. It's common for campaigns not to feature the same talent every time. While some media, like LIVE BOARD (*), are based on impression measurement, many OOH media are not. Therefore, even if an OOH campaign becomes a topic on X (Twitter), it's difficult to clearly define how many posts constitute success. This situation has been a factor making it difficult for advertisers to place OOH campaigns, especially as the visualization of advertising effectiveness has advanced across various media in recent years.
*LIVE BOARD: A new company established by NTT DOCOMO and Dentsu Inc. to operate a digital OOH advertising distribution platform and sell advertising space. It enables OOH ad delivery based on impressions (ad viewers) and user attributes, making it possible to verify OOH advertising effectiveness and ROI. It owns a networked digital signage system called "LIVE BOARD" across Japan.
What defines buzz, and what elements create it?
We have been researching to understand the essence of OOH buzz and make it quantifiable. Our first step was defining what constitutes "going viral" for OOH. We first investigated the number of tweets required to be considered viral. Next, we examined the factors that trigger OOH buzz. Furthermore, based on these metrics and factors, we developed a "buzz prediction model." Below, we explain the research findings on OOH and buzz we have derived.
Definition of Buzz
Our initial focus was on tweet volume. Analyzing past OOH campaigns using standard statistical methods revealed that buzz can be categorized as small, medium, or large. Based on this analysis, we set the threshold for "trending" as "medium buzz level" or higher.
According to this analysis, which also considers media and creative elements in OOH campaigns, nearly 70% of the surveyed campaigns fall into the Small Buzz Level category. Campaigns reaching the Medium Buzz Level or higher, which can be considered "trending," not only have higher tweet counts and reach than the baseline but are also frequently reposted by news media and television, aligning closely with our intuitive understanding.
Factors Influencing Buzz
Regarding factors influencing tweet volume, we explored what impacts buzz primarily through two axes: the "ad content (ad scale and media type)" and the "creative content" used in the ads.
For example, we know that the "advertising period" affects buzz. Long-term campaigns create an environment where people are more likely to visit the location, take photos, and tweet about it, leading to greater buzz.
Regarding content (creative), we found that "unique campaign elements" significantly impact buzz. Campaigns like "peel-off" posters—where product samples are attached to the poster for passersby to take—or installing capsule toy machines at stations for people to try, create novelty that makes people want to take photos. The desire to share the joy of obtaining a sample or capsule toy drives significant buzz.
The buzz also increases through the combination of ad content and creative elements. For example, displaying OOH at multiple stations in Tokyo generates greater buzz than displaying it at just one station. However, even with a single station placement, there are cases where tweet volume increases. This happens when official accounts post content encouraging campaign participation for fans unable to visit the location, linking it with social media. As such, numerous elements contribute to buzz. Advertisers can now consider what actions will maximize buzz when placing OOH for the purpose of generating discussion.
Utilizing a Buzz Prediction Model to Increase Campaign Success Rates
By defining buzz and exploring its influencing factors, we developed a buzz prediction model. This model simulates the expected buzz level based on inputs like the advertiser's planned budget, area, media, and creative elements. Using this prediction model allows us to evaluate plans with a high probability of going viral, tailored to the advertiser's budget and requirements.
For example, it can predict how buzz changes when the same ad spend is allocated across multiple display areas, or when compared to a nationwide rollout versus a concentrated, takeover-style campaign focused on a single location within Tokyo. This empowers advertisers to select the most effective approach when aiming to generate buzz.
Furthermore, after OOH placements, in addition to social listening reports, advertisers can now use the predictive model to evaluate plans for future campaigns.
In one case, the predictive model indicated that significant buzz was unlikely. Based on this, the advertiser proceeded with planning by considering several options, such as increasing the number of OOH media or expanding the areas where the campaign would run.
Through buzz prediction models, OOH has entered a phase where it can forecast buzz on X (formerly Twitter) and also verify effectiveness.
Furthermore, having studied numerous past cases, we can now provide data and various consulting services to advertisers seeking to implement OOH specifically for buzz generation.
While OOH has often been implemented to supplement TV reach, we continue researching whether observing buzz on X (Twitter) can uncover new value and possibilities for OOH. If you are interested, please contact us at the address below.
Contact us here:
OOH Social Listening Project Team
Email: ooh_sociallistening@group.dentsu.co.jp
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Author
Mitsui Sayaka
CARTA COMMUNICATIONS, Inc.
After joining Cyber Communications (now CARTA COMMUNICATIONS), I was responsible for web analytics using Google Analytics and Adobe Analytics, supporting the implementation of analytics tools, and digital marketing operations utilizing various tag managers. I also engaged in social media analysis using social listening tools. Since 2019, I have been working at Dentsu Inc.'s Out-of-Home Media Division.
