OTOinsights "Emotion, Engagement, Internet Video"

Emotion, EngagEmEnt

and intErnEt VidEo

je f f rey bardzel l , ph .d . • shaowen bardzel l , ph .d . • ty ler pace

Internet video is one of the fastest growing entertainment media and among the most

popular of all Internet activities. According to recent reports (July 2007, January 2008)

from the Pew Internet & American Life Project, 57% of Internet users visit video sharing

sites and 20% visit video sharing sites daily. Growth in Internet video consumption is

highest among the key demographics of college-bound and educated 18-29-year-olds,

76% of which visit video sites daily. Additional high-growth demographics include

women (11% view daily, up from 5% in 2007) and 30-49-year-olds (14% view daily, up

from 7% in 2007) [1,2].

In this report, we present an analysis of viewer engagement with Internet video. Viewer

engagement was measured using OTOinsight’s Quantemo™ system. Quantemo™ utilizes

a multimodal approach that combines self-report and physiological data to holistically

and reliably measure user engagement with digital media like Internet video.

Analyzing the results from the various Quantemo™ data sources, we present a series of

three insights concerning how users locate, respond to, and engage with Internet video.

insights

1. Viewer Responses to Internet Videos are Emotionally Complex.

2. Engagement Scores Substantially Enhance Interpretability of User Ratings.

3. Viewer Engagement and Video Success are Positively Linked.

ExEcut iVE summary : :

Copyright © 2008, One to One Interactive www.onetooneinteractive.com 1


introduct ion : :

intErnEt VidEo

Internet video is one of the fastest growing entertainment media and among the most

popular of all Internet activities. According to recent reports (July 2007, January 2008)

from the Pew Internet & American Life Project, 57% of Internet users visit video sharing

sites and 20% visit video sharing sites daily. Growth in Internet video consumption is

highest among college-bound and educated 18-29-year-olds, 76% of which visit video

sites daily. Additional high-growth demographics include women (11% view daily, up

from 5% in 2007) and 30-49-yearolds (14% view daily, up from 7% in 2007) [1,2].

Percent Watch/DoWnloaD Internet VIDeo

men 63%

Women 51%

ages 18-29 76%

ages 30-49 57%

ages 50-64 46%

ages 65+ 39%

hs grad or less 46%

some college 62%

college grad 64%

Less than $30k 52%

$30k-$50k 63%

$50k-75k 63%

$75k+ 62%

FigurE 1: demographic Breakdown of internet Video Viewers.

source: Pew internet & american Life Project (2007).

39 Copyright © 2008, One to One Interactive www.onetooneinteractive.com 3

Analysts expect the growth of Internet video to continue for the foreseeable future.

Forrester predicts that the use of Internet video will triple by 2013 [3]. Additionally, videos

consumed on mobile devices will double, and the creation/submission of user generated

video is expected to increase five-fold in the next five years [9]. Cisco, one of the primary

providers of Internet backbone equipment, predicts that Internet bandwidth will continue

to grow at a 46% annual compound growth rate, which is chiefly led by the ever increasing

popularity of Internet video. According to Cisco, Internet video accounts for 90% of all

consumer Internet bandwidth [4].

The explosive success of Internet video sites, including both those which focus on

consumer uploaded content (e.g., YouTube) and commercially released content

(e.g., Hulu), have put additional pressure on traditional television viewership. Similar

to digital video recorders (DVR), Internet video is seen as another means for time

displacing television viewing, which often results in skipped or deleted advertisements

and, in some cases, an overall decrease in time spent viewing television content [5].

In part as a response to the success of Internet video, Internet advertising spending is

expected to top that of television advertisements within the next year. Furthermore,

Internet video advertisements continue to command premium prices (often higher

CPM than television ads) compared to other forms of Internet advertising (banners) [6].

The success of Internet video cannot be discussed without touching on the phenomenon

of viral videos. Internet video is uniquely positioned to be easily shared with friends and

colleagues. According to Pew, 57% of Internet video viewers share videos with friends,

and 75% receive and watch videos sent from friends/colleagues. The ease and speed with

which Internet video can be shared can result in massive viewership in a short period of

time. Compete, an Internet analytics firm, recently tracked the success of a viral video

released in August 2006. Miss Teen USA competitor Caitlin Upton from South Carolina

embarrassingly answered a question during the televised pageant competition. A video

excerpt of her answer was posted to YouTube on August 25th and obtained over 200,000

views in less than 24 hours. Views grew exponentially each day, peaking at 1.6 million

unique views on August 28th [7]. As of July 2008, the video has over 29 million views

and is the 51st most watched video of all time on YouTube.com.


FigurE 2: Visits to miss teen south carolina Videos. (Each dot equals 1000 video plays.)

There is little doubt that Internet video is one of the great successes of the Internet and

offers a new and growing medium for advertising materials. However, relatively little is

known about how viewers engage with Internet video on an emotional level. The links

between emotion and behavior are well established in marketing[8], but the ability to

measure affective response to Internet video is still lacking. Developing methods to

measure emotional response and engagement with Internet video is critical to the suc-

cess of future Internet video advertising campaigns.

study dEs ign : :

mEasuring Emotion: ProBLEms and stratEgiEs

Traditional user research approaches, such as focus groups, interviews, and surveys, all

focus on self-report. Assuming that people tell the truth in such situations, there remains

the problem of cognitive bias, which is the notion that while emotion affects the whole

body, including both its physiology and cognitive dimensions, traditional self-report

mechanisms are filtered through cognition. Physiological measurements of emotion allow

researchers to analyze emotional activity without cognitive bias. However, physiological

measures have their own limitations: a strong reliance on physiological data for measuring

emotions leaves room for misinterpretation of physiological noise (natural changes in body

status) and burdens researchers with the difficult task of attributing specific physiological

changes (increase in heart rate) to complex and subjectively experienced emotions

(hate, love, fear, etc.). A combination approach, which approaches emotional measurement

from both physiological and self-report methods, is warranted.

This study is part of a larger research program investigating the role of affect in interactive

system design at Indiana University School of Informatics, conducted in partnership with

OTOinsights. In it, we combine data from traditional, self-report user research methods in

addition to physiological measurements to correlate (i) people’s felt experience of their

emotions when interacting with Internet videos (browsing, selecting, and watching)

with (ii) their behavioral/physiological responses by using OTOinsights’ Quantemo™

neuromarketing research lab. Specifically, we are seeking to understand how people’s

emotions influence their interactions with videos, with the hopes that marketers can design

engaging experiences that better support users’ emotional needs and desires. The

combined methods used in this study set out to explore ways to combine and interpret

both objective measures of emotions with the subjective notion of emotions. Any patterns

or relations between the objective, moment-to-moment measure of emotional impact and

the subjective, post-interpretive understanding of emotions could inform the design of

engaging video presentations to reach the target audience.



mEasuring sELF-rEPort aFFEctiVE rEsPonsE to ViraL VidEos

A collection of 60 videos was selected from three video web sites of amateur social

multimedia content, based on their popularity rankings. The sites were as follows:

www.youtube.com, www.newgrounds.com, and www.albinoblacksheep.com. Videos

were categorized into eight genres: Action, Comedy, Documentary, Drama, Family,

Horror, Mashup and Romance.

Before the study begins, participants were asked to identify their present emotional

state by selecting one to three emotional descriptors from a collection of 36, based on

the Geneva Emotion Wheel (Scherer, 2005). Developed by the researchers at the Swiss

National Research Center in Affective Sciences, the Geneva Emotion Wheel is designed

to obtain self-report information on a wide range of felt emotions elicited by a particular

event (in the case of this study, viewing an Internet video).

Participants were then asked to watch six videos of their choosing from any combination

of the 60 total, spread across the eight available genres. After watching each video,

participants were asked to complete two different tasks with the objective of providing

different means for them to express their emotions:

TAGGING: the participants were asked to select up to three out of 36 emotional

descriptors to describe the emotional dimensions of the video they watched. They

were also asked to state the intensity of their emotional responses. Both the selected

emotions valence (positive or negative) and the intensity are factored into our scoring

of emotional descriptors.

REVIEWING: the participants were asked to write a short review to comment on the

emotional reactions after viewing the video, as well as assigning a rating of 1-5 (with 5

being the highest) of each video viewed.

The same procedures were repeated for each of the six videos. At the end of the study,

we gave the participants an exit survey, which helped us understand more about their

familiarity with Internet video, their video selection criteria, and their evaluation of the

effectiveness of three different methods of emotional expression.


THE QPI AND QEI: MEASURING EMOTIONAL ENGAGEMENT WITH VIRAL VIDEOS

While watching their videos, participants were connected to OTOinsight’s Quantemo™

neuromarketing research system (Figure 3). Quantemo™ simultaneously records multiple

biophysical signals (breath rate, galvanic skin response, heart rate, body temperature) in

addition to eye and click tracking information. After recording the biophysical measures,

Quantemo™ combines the measures into a single representative measure of physiological

engagement. The Quantemo™ Physiological Index or QPI serves as a single point of

reference for the overall level of physical engagement (or disengagement) exhibited by

a research participant. Positive QPI scores represent stronger physiological engagement,

while negative QPI scores represent weaker physiological engagement

The QPI, ratings, and emotional descriptor scores are combined to form the Quantemo™

Engagement Index or QEI. Calculating the QEI produces a single, representative and

holistic measure of user engagement that allows researchers to correlate the objective

physiological data of the QPI with the subjective, self-report data of the ratings and

emotion scores. Additionally, the written reviews offer insight into the reactions and

thoughts of participants after they viewed each media. The insights presented in this

report are based on analysis of the QPI, QEI and written reviews.

To summarize: insights from this study were thus based on the analysis of both the

self-report dimensions of emotions (e.g., participants’ assignment of emotional

descriptors, reviews, and the exit survey), as well as on objective, physiology-based

measures of emotions (e.g., the QPI). The QEI is a single measure that combines the

two data collection strategies for measuring affect.

FigurE 3: Quantemo™ index types and component values

Quantemo™ InDex comPonents

Quantemo™ Physiological index (QPi)

Quantemo™ Engagement index (QEi) QPi, ratings, Emotion scores

Breath rate, heart rate, Body temperature, galvanic skin response


VidEos anaLyzEd

Although a total of 60 videos were available to participants, any given participant

only watched six, and moreover, participants selected which videos they watched.

As a result, videos received an uneven number of viewings, and so only a subset of

videos were included for analysis in this study. A given video was only included for

analysis if it had a sufficient number of viewings (n=5 or higher); 10 videos met the

minimum criteria for analysis (Figure 4).

The People’s Mario 123.53 212.1

The Matrix Has You (Burly Brawl) 128.27 290.1

Completely Uncalled For 125.79 219.1

Piece of Mind - Vancouver Film School 123.73 123.7

The Ultimate Showdown 120.81 294.7

World of Warcraft BigBlueDress 130.77 195.4

Web 2.0 ... The Machine is Us/ing Us 128.05 304.7

The Evil Strawberry 125.97 181.5

Jobs 136.35 266.4

Bagadada - Bagagaga Bop! 122.83 160.2

VIDeo QPI QeI

(QPi: aVg=126.61, sd=4.52, sE=1.43; QEi: aVg=224.79, sd=61.99, sE=16.7)

FigurE 4: List of analyzed Videos with QPi and QEi scores


ins ights : :

gEnEraL Findings

Before introducing our specific insights, which are actionable findings targeted toward

corporate viral video designers, we share several general findings to provide some

important context.

First, data from the study does not suggest any correlation between engagement,

emotion, and the length of a video. Long videos (three minutes or greater) and short

videos (two minutes or less) are equally likely to have high or low engagement scores.

This finding suggests that Internet videos do not need to be limited to sound bite

productions or even standard television commercial length. Internet video viewers are

willing to view longer productions so long as they’re engaging.

Second, the order in which videos were watched in the study had no noticeable effect on

the engagement scores for those videos. Participants found a video engaging regardless

of the sequence in which it was viewed. This finding supports the validity of the study

data with evidence that participants did not tire out during the study thereby artificially

deflating engagement scores for their final videos.

Third, according to our exit survey, participants overwhelmingly agreed (86%) that

Internet video affects their current emotional state. In fact, many participants noted that

they deliberately use Internet video to alter their moods. Participants sought out videos

which projected the emotional state they wished to achieve (e.g., selecting humorous

videos to lighten one’s mood).

In the following sections, we summarize three major findings from this study under the

headings of the primary insights derived from analysis of the QPI, emotional descriptors,

user review, and survey data.


insight 1: VIEWER RESPONSES TO INTERNET VIDEOS ARE EMOTIONALLY COMPLEx

A common perception of Internet videos is that they are both simple and discrete in their

emotional content and advertising message. The relative ease of producing and distributing

an Internet video (and their often highly focused nature) adds to the perception that the

media is somewhat “flimsy,” that is, that Internet video lacks the aesthetic sophistication to

have an emotional impact on viewers. Our data suggests otherwise. To our surprise, we

found across several measures that viewer’s emotional responses were complex, often

even conflicting.

At the time of this writing, we have 80 unique emotional descriptor sets created by study

participants. Each emotional descriptor set ranges between one and three emotional tags,

reflecting a participant’s combined emotional reaction to a single video. These emotional

descriptors are divided on the Geneva Emotion Wheel into positive (e.g., amusement,

interest, touched, etc.) and negative (e.g., disgust, irritation, disappointment, etc.)

groupings. We mapped the 80 emotional descriptor sets onto the groupings and found

that, overall, participants’ emotional descriptor sets were composed of 57% positive

emotions and 43% negative emotions (Figure 5). The surprisingly high number of negative

emotional descriptors used, this in spite of the overall positive reviews of videos, suggests

complex and often contradictory emotional reactions to Internet videos.

Among the most popular positive emotion descriptors, participants most frequently used

the “amusement” emotion to describe their initial emotional state, prior to the video viewing

session. Considering the number of videos watched from the comedy genre (21% of all

videos watched), it is not surprising that amusement is the primary emotion descriptor

used by our participants. However, when participants used multiple emotion descriptors to

describe their emotional reaction to the video, the makeup of their affective state becomes

much more complex (Figure 6). The most common secondary emotional descriptors are

evenly split among negative (e.g., dissatisfaction, boredom, tension/stress, etc.) and

positive (e.g., interest, pleasure, happiness, etc.) emotions. Those participants who used

three emotional descriptors continued the trend of highly varied emotions, with “irritation”

being the most common emotion identified by our participants who used three emotional

descriptors to describe their reactions.

FigurE 5: Breakdown of overall use of Positive and negative Emotion descriptors

% used overall

Positive Emotion tags 56.86%

negative Emotion tags 43.14%


Our data demonstrates deeper and, unexpectedly, conflicted emotional reactions to

Internet video. Marketers need to be aware of the range and complexity of emotional

responses to quickly consumed and produced digital creatives like Internet video.

Similarly, marketers need to guard against allowing their research and analysis methods

to become overly reductive about emotional response. Emotional states are seldom

monolithic. Even if the videos seem self-evident in their meanings, viewers’ reactions to

them are quietly sophisticated. This insight is particularly important, because traditional

measures, such as surveys and focus groups, make it difficult for research subjects to

express—or even be cognizant of—the fullness of their own emotional responses. Simplified

techniques for analyzing Internet videos will lead to both a limited understanding of

viewer response to videos as well as a reduced ability to design Internet videos to quickly

deliver the advertising message and elicit the intended reaction that marketers’ desire.

FigurE 6: most commonly selected Emotional descriptors,

identified by 16 participants

tag number of uses

amusement 34

irritation 9

dissatisfaction 7

interest 7

Pleasure 7

happiness 6

surprise 6

Boredom 6


ratIng QPI geW BehaVIoral QeI

1 — — — —

2 + — — —

3 + + — +

4 — + + +

5 + + + +

insight 2: Engagement scores substantially Enhance interpretability of

user ratings

Likert rating systems, commonly seen as one- to five-star scales, remain dominant in

most Internet applications, including Internet video. As mentioned earlier, videos for this

study were collected from YouTube, Newgrounds and the Albino Black Sheep web sites.

Each of these web sites uses either a five- or six- point rating system. Ease of use and

implementation largely explain the success of the Likert style ratings systems; however, it

remains unknown the extent to which more detailed measures of engagement correspond

to existing and common rating systems, such as those found on most web sites.

As noted earlier, the Quantemo™ Engagement Index (QEI) is a proprietary index of user

engagement based upon physiological (via the Quantemo™ Physiological Index or QPI)

and self-report data. Figure 7 outlines the relationship between Likert style video ratings

and the overall positive and negative result of the baseline scores (QPI, GEW and

behavioral) that create the QEI.

Perhaps not surprisingly, Figure 7 demonstrates that videos with the highest (5) and

lowest (1) Likert ratings also have either entirely positive or negative engagement scores.

Videos rated “1” are the only videos to have all negative QPI, GEW and behavioral scores

resulting in a negative QEI. Similarly, videos rated “5” are the only videos to have all

positive QPI, GEW and behavioral scores resulting in a positive QEI. At the highest (5)

and lowest (1) ratings, the QEI and ratings systems tightly correspond to one another;

however, at ratings 2-4 the QEI scores offer meaningful feedback on why a video

receives a middling rating.

FigurE 7: average positive and negative makeup of sub-scores of the QEi

(QPi, gEW, Behavioral).

Rating systems are notorious for clustering results at central scores (e.g., 3 on a 5-point

system), with few items standing out on either the extremely negative (1) or positive (5)

end. However, due to their limited granularity (only 1 metric), rating systems offer virtually

no feedback as to why an item has a middling rating. Marketers designing and evaluating

digital media creative assets are not well served by the lack of feedback provided by

common ratings systems. Given the importance of ratings systems in video popularity

(Insight 1), it is critical that marketers develop a better understanding of why users might

give a video an undesirable rating. A closer look at the constituent scores of the QEI (QPI,

GEW and behavioral) provide one such method for receiving directed feedback as to why

a video received its rating.

In the case of videos rated “2” only one metric, QPI, was positive overall. This is an

indication to marketers that, while the video is physically stimulating, it does not carry the

emotional effect (GEW) or a mixed/confusing message (behavioral) necessary to improve

the videos rating among the intended viewers. Additionally, videos rated with a “3” or “4”

have one negative score each (behavioral and QPI, respectively) that help explain why

those videos received their imperfect rating. Detailed measures like the QEI and its

components will assist marketers in refining their creative assets for maximum impact.

Note: Figure 7 indicates the average QPI, GEW, behavioral and QEI scores for videos viewed

for this study. These results are not meant to be interpreted as applicable to all Internet

video rating systems or sites. Instead, the Figure highlights the ability of a more detailed

measure, like the QEI, to provide directed feedback concerning why a video received (or

might receive) an undesirable rating.


insight 3: Viewer Engagement and Video success are Positively Linked

Insight 2 establishes the ability of the QEI to operate as a single measure of emotional

and physiological engagement with digital media. Given that the QEI represents our

methodology for measuring affective response to Internet videos, the next question is

how QEI scores compare to other video evaluation metrics. Most sites have rudimentary

indicators of community engagement with videos, including number of views, review

scores, and number of reviews, among others.

Analysis of reported page views, the statistic often used as the primary external measure

of popularity, side-by-side with the QEI yields an interesting trend. Videos with the

highest QEI scores in our study are also the most externally successful videos when

compared against each other (Figures 8 and 9). We must caution that the data for this

trend is not yet sufficient to cite as a statistically valid correlation, but the trend shows

great promise for the potential of the QEI as a partial predictor of the success of an

Internet video. Measuring videos with the QEI provides an indicator that the video itself

is emotionally engaging enough to satisfy the viewers of Internet video, though obviously

other factors will affect a video’s overall success.

This data suggests that a certain level of emotional engagement is a necessary, though

not sufficient, predictor of a viral video’s success. In other words, it is unlikely that a video

lacking a certain amount of emotional engageability will spread virally, regardless of other

factors. At the same time, just because a video has this emotional engageability by no

means guarantees that it will go viral; other factors (e.g., word of mouth, computer based

recommendation systems, and trendy cultural topics and memes) will influence a given

video’s viral ability.


movie QEi Views

Web 2.0 … the machine is us/ing us 304.70 6090620

completely uncalled For 219.12 5727017

World of Warcraft BigBluedress 195.44 3609728

the Evil strawberry 181.53 943792

Piece of mind — Vancouver Film school 123.73 864647

Youtube VIDeos

FigurE 8: QEi and View counts for youtube Videos. Views current on July 27, 2008.

Note: The figures above list the QEIs and views for a portion of videos in the study. QEIs

were only analyzed for videos after a sufficient number of participants viewed the video

(5+). The videos listed in the chart have the highest QEIs of any videos analyzed for the

study. Comparing QEI and views across different video sites is not recommended due to

the innumerable differences between each video sharing site (user base, favored genres,

traffic ratings, etc). Finally, all videos are at least one year old, so it is unlikely to see spikes

in their views in the future that would change the ordering of this chart.


movie QEi Views

the ultimate showdown 294.70 10333504

the matrix has you (Burly Brawl) 290.11 3184030

the People’s mario 212.10 961967

neWgrounDs VIDeos

FigurE 9: QEi and View counts for newgrounds videos. Views current on July 27, 2008.

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concLusions

The findings of this report suggest that combining self-report and physiological data to

measure engagement with viral videos is a fruitful process. Self-report data provides

a necessary means for interpreting physiological data, while physiological data provides

an unbiased look at a participant’s level of physiological engagement. Combining the two

types of data yields a powerful, holistic representation of engagement that can be used,

in part, to measure the efficacy of an Internet video.

OTOinsights Quantemo™ system is an industry-leading platform for holistically measuring

engagement with digital media like Internet video. A unique and diverse multimodal

approach to measuring engagement combined with a proprietary scoring system yields

a valuable, single-point measure of use engagement in the Quantemo™ Engagement

Index (QEI). The QEI offers a convenient and reliable measure for benchmarking and

investigating the effectiveness of digital media campaigns. Additionally, the QEI offers

more detailed feedback regarding viewer reaction to digital media than the standard

rating systems.

The study presented in this report was not designed to discover a “be all, end all”

strategy for Internet video. However, when combined, several of the Insights in this report

inform current Internet video strategies in novel ways.

Emotional engagement is at the core of Internet video watching, so understanding the

relationships between a given video effort and how people will react emotionally is

key. Our findings reinforce the importance of measuring engagement with Internet

video prior to release. As the preliminary results of this study suggest, videos with the

most positive engagement scores were the most successful videos on their respective

video-sharing sites.

There is no magic formula for creating successful viral video campaigns. But, as with any

design problem, designers’ chances of creating a connection are much higher if they

have empathy with the users. Part of that is knowing how they think about a given

domain, such as viral video; but another part of it is understanding how they emotionally

engage with it.


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Pew Internet & American Life Project. Retrieved August 3, 2008 from

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3. McQuivery, J. (2008). How Video Will Take Over The World.

Forrester Research, Inc. Available from

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as Primary Media Device. Retrieved August 3, 2008 from

http://www-03.ibm.com/press/us/en/pressrelease/22206.wss

6. Sweney, M. (2008). Internet ad spending will overtake television in 2009.


http://www.guardian.co.uk/media/2008/may/19/digitalmedia.advertising

7. Bagg, S. (2007). YouTube Revolutionized Embarrassment.


http://blog.compete.com/2007/10/18/miss-teen-couth-carolina-maps-youtube-views/

8. O’Shaughnessy, J. and O’Shaughnessy, N. (2003). The Marketing Power of Emotion.

Oxford University Press: New York.

9. Scherer, K. (2005). What are emotions? And how can they be measured?

Social Science Information. London: SAGE Publications.

Vol. 44(4), pp. 695-729.

amPLiFying usEr EngagEmEnt

New knowledge about human behavior brought to light by social and neuroscience

has fundamentally called into question the old mental models of how advertising

and marketing work. Gone is the notion that consumers make decisions in a

linear think-feel-do way and behavior is guided by rational-only principles. Instead,

memories, emotions, associations, and thoughts play a primary role in how

individuals relate and ultimately engage with brands.

OTOinsights is a primary research offering that is breaking new ground in neuro-

marketing to offer clients advanced and scientific levels of insights into how their

consumers engage with them across the landscape of new media channels.

to learn more about otoinsights, visit www.otoinsights.com

comPLEtE onE-to-onE soLutions For Brands, agEnciEs, and PuBLishErs

OTOinsights is a One to One Interactive company. Established in 1997, One to One

Interactive is the first enterprise to assemble a complete solution for brands, agencies,

and publishers executing one-to-one marketing strategies. By bringing together one

of the nation’s leading digital marketing agencies, the world’s most comprehensive

portfolio of permission marketing platforms, unique performance-based social media

networks, and cutting-edge neuromarketing research techniques, the companies

of One to One Interactive build informed and creative customer/constituent strategies

on the belief that digital media’s ability to enable engaging one-to-one dialogues is

the future of marketing.

to learn more about one to one Interactive, visit www.onetooneinteractive.com

OTOinsights

529 Main Street, Charlestown, MA 02129

617.425.7300

www.otoinsights.com

[email protected]


Technology

OTOinsights "Emotion, Engagement, Internet Video"