OTOinsights "Emotion, Engagement, Internet Video"

Emotion, EngagEmEnt
and intErnEt VidEo

j e f f rey b a rd z e l l , p h . d . • s h a owe n b a rd z e l l , p h . d . • t y l e r p a c e

ExEcutiVE summary : :

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.

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

introduction : :

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).


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.

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

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.


s t u dy d E s i g n : :

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

Quantemo™ InDex comPonents

Breath rate, heart rate, Body
Quantemo™ Physiological index (QPi)
temperature, galvanic skin response

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

FigurE 3: Quantemo™ index types and component values

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.


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).

VIDeo QPI QeI

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

(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


insights : :

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.

% used overall

Positive Emotion tags 56.86%

negative Emotion tags 43.14%

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

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.


tag number of uses

amusement 34

irritation 9

dissatisfaction 7

interest 7

Pleasure 7

happiness 6

surprise 6

Boredom 6

FigurE 6: most commonly selected Emotional descriptors,
identified by 16 participants

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.


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.

ratIng QPI geW BehaVIoral QeI

1 — — — —

2 + — — —

3 + + — +

4 — + + +

5 + + + +
FigurE 7: average positive and negative makeup of sub-scores of the QEi
(QPi, gEW, Behavioral).

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.


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.

Youtube VIDeos

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

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


neWgrounDs VIDeos

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

FigurE 9: QEi and View counts for newgrounds 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.


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.


rEFErEncEs

1. Madden, M. (2007). Online Video. Pew Internet & American Life Project.
Retrieved August 3, 2008 from
http://www.pewinternet.org/PPF/r/219/report_display.asp

2. Raine, L. (2008). Increased Use of Video-sharing Sites.
Pew Internet & American Life Project. Retrieved August 3, 2008 from
http://www.pewinternet.org/PPF/r/232/report_display.asp

3. McQuivery, J. (2008). How Video Will Take Over The World.
Forrester Research, Inc. Available from
http://www.forrester.com/research/Document/excerpt/0,7211,44199,00.html

4. Waltner, C. (2008). Video Growth Offers Challenges,
Opportunities for Stewards of the Internet. Retrieved August 3, 2008 from
http://newsroom.cisco.com/dlls/2008/ts_061708.html

5. IBM. (2007). IBM Consumer Survey Shows Decline of TV
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

info@otoinsights.com


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