Emotion, Engagement and Internet Video

Transcription

D e cemb er 20 0 8
Jeffrey Bardzell, Ph.D., Shaowen Bardzell, Ph.D., and Tyler Pace
D e cemb er 20 0 8
Jeffrey Bardzell, Ph.D., Shaowen Bardzell, Ph.D., and Tyler Pace
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.
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].
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].
Figure 1: Demographic Breakdown of Internet Video
Viewers. Source: Pew Internet & American Life
Project (2007).
Percent Watch/Download Internet Video
63%
Men
51%
Women
76%
Ages 18-29
57%
Ages 30-49
Ages 50-64
Age 65+
HS Grad or Less
Some College
46%
39%
46%
62%
64%
College Grad
The explosive success of Internet video sites, including both those
52%
Less than $30k
which focus on consumer uploaded content (e.g., YouTube) and
63%
$30k-$50k
commercially released content (e.g., Hulu), have put additional
pressure on traditional television viewership. Similar to digital video
63%
$50k-75k
recorders (DVR), Internet video is seen as another means for time
displacing television viewing, which often results in skipped or deleted
62%
$75k+
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.
© 2008 One to One interactive Reproduction prohibited
Figure 2 Visits to Miss Teen South Carolina Videos Each dot equals 1000 video plays
August 25th
August 26th
August 27th
August 28th
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 success of future Internet video advertising campaigns.
Study Design
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™ neuromark eting 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 websites 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
Figure 3: Quantemo™ index types and component values
Quantemo™ Index
Components
Quantemo™ Physiological Index (QPI)
Breath Rate, Heart Rate, Body Temperature, Galvanic Skin Response
Quantemo™ Engagement Index (QEI)
QPI, Ratings, Emotion Scores
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).
Figure 4: List of Analyzed Videos with QPI and QEI Scores
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)
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.
Figure 5: Breakdown of Overall Use of Positive and Negative Emotion Descriptors
% Used Overall
Positive Emotion Tags
56.86%
Negative Emotion Tags
43.14%
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 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
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 websites. Each of these websites
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 websites.
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.
Figure 7: Average positive and negative makeup of sub-scores of the QEI (QPI, GEW, Behavioral).
Rating
QPI
GEW
Behavioral
QEI
1
-
-
-
-
2
+
-
-
-
3
+
+
-
+
4
-
+
+
+
5
+
+
+
+
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, computerbased recommendation systems, and trendy cultural topics and memes) will influence a given video’s viral ability.
Figure 8: QEI and View Counts for YouTube Videos. Views current on July 27, 2008.
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 9: QEI and View Counts for Newgrounds 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
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. Retrieved August 3, 2008 from
http://www.guardian.co.uk/media/2008/may/19/digitalmedia.advertising
7 Bagg, S. (2007). YouTube Revolutionized Embarrassment. Retrieved August 8, 2008 from
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 works. 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 collection of primary and secondary research
offerings that is breaking new ground in nueromarketing to offer clients
advanced and scientific levels of insights into how their consumers engage
with them across the landscape of media channels.
Quantifying Emotional Connection
OTOinsight’s NeuroMarketing Research Lab that offers a scientific approach
to measuring a target audience’s emotional reactions to digital media (web
sites, online advertising, streaming video, virtual worlds, etc.).
Exploring New Media Universes
OTOinsight’s secondary research offering that will provide timely critical
analysis and insights regarding emerging digital platforms. Focus will be
paid to Virtual Worlds, Online Games, Social Networks, Amateur Multimedia
and Mobile Applications.
Next Generation
Digital Marketing Holding Company
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 cuttingedge 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 our site at www.onetooneinteractive.com
OTOinsights | 529 Main Street, Charlestown, MA 02129 | www.quantemo.com | 617 425 7300 | info@otoinsights.com

Emotion, Engagement and Internet Video

Transcription

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