Understanding Emotient Analytics Key Performance Indicators

Transcription

Understanding Emotient Analytics Key Performance Indicators
Understanding Emotient Analytics Key Performance Indicators
Emotient Analytics measures and provides reports
on three Key Performance Indicators (KPIs):
Attention, Emotional Engagement, and Sentiment.
These metrics are important in many use cases:
Advertising: an important goal of successful
advertising is to capture and hold the attention of
the customer, as well as to create an emotional
connection. The ability to attract attention is
important for evaluating both ad content and ad
location/timing, particularly in cluttered sensory environments such as out of home (OOH)
venues.
Media Programming: TV and online shows and segments are expensive to produce and are
critical to driving ad revenue for entertainment and media companies. Knowing whether the
audience is paying attention to and engaging with the plotline, character, news anchor or
“laugh lines” is key information that helps to determine if, where and when to run new content
and how to improve the performance of existing media.
Product Evaluation: Well-designed physical and web-based products can create joy while poor
implementations may generate confusion and frustration. Discovering which aspects deliver
positive sentiment is essential to creating product and service experiences that delight rather
than repulse.
KPI Definitions
Attention
Emotient Analytics assesses Attention by measuring how often faces are frontal to the camera.
For example, let’s say you have a video with two people and the first person faces the camera
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Understanding Emotient Analytics Key Performance Indicators
80% of the time while the second person faces the camera 20% of the time. The Attention
measurement for this video is 50%.
Emotional Engagement
Emotient Analytics assesses Emotional Engagement by measuring how often faces display
emotions. For example, you have a video with two people. The first person displays emotional
expressions 10% of the time. The second person displays emotional expressions 2% of the time.
The Emotional Engagement KPI is 6%.
Sentiment
Emotient Analytics assesses Sentiment by estimating the proportion of positive emotions to
overall expressed emotions. A Sentiment score of 100% would indicate that, of the emotions
detected, all were positive. A sentiment score of 0% would indicate a complete absence of
positive emotions because detected faces all were coded as negative or neutral at all times.
KPI Charts
For each KPI, Emotient Analytics provides a summary chart and multiple detailed charts. Each
chart displays either a percentage of the total FaceSecond(s) detected or the absolute number
of FaceSecond(s) detected. A FaceSecond is an Emotient term for a single face being detected
for an entire second. For example, a 10 second video with two faces, where one is detected for
all of the video but the other disappears or become obstructed for 5 seconds in the middle of
the video would have 15 FaceSecond(s) total.
The following images are of second-by-second charts. They are also available in quarters
(measurements being provided for each 25% of the total video length) and can be filtered by
Gender.
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Understanding Emotient Analytics Key Performance Indicators
Attention
Summary Chart - Attention by Second
In the chart above, at timestamp 00:00:25, the Attention KPI is 75%, which means that from
timestamp 00:00:24.5 to 00:00:25.5, people were facing the camera 75% of the time.
Detailed Charts
Overall Attention: The average attention paid across the entire length of the video and across
all faces detected.
Participants by Gender: A time-weighted male/female percentage split of the faces detected in
the video, whether or not they were paying attention or showing emotion.
Attention by Gender – Attention by second or quarter for each gender. This chart is formatted
the same as the summary chart but with a line for each gender.
Emotional Engagement
Summary Chart - Emotional Engagement by Second
Emotient Analytics assesses Emotional Engagement by measuring how often faces display
emotions. In the chart above, from timestamp 00:04.5 to 00:05.5 people displayed emotional
expressions 10% of the time.
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Detailed Charts
Emotion Detected by Second (or by Quarter): This stacked volume chart shows the composition
of the Emotional Engagement detected across time. It shows the total FaceSecond(s) for each
emotion and the percentage of the total emotional engagement that is attributable to that
emotion.
Overall Emotional Engagement: The average Emotional Engagement measured across the
entire length of the video and across all faces detected.
Participants by Gender: A time-weighted male/female percentage split of the faces detected in
the video, whether or not they were paying attention or showing emotion.
Overall Emotion Detected: A pie chart showing the composition of the total Emotional
Engagement detected across the video. It is split into wedges for each of the seven primary
emotions.
Emotions by Gender: A stacked bar chart for each emotion showing the FaceSecond(s) in which
each emotion was displayed by each gender.
Emotional Engagement by Gender: Shows Emotional Engagement by second or quarter for each
gender. This chart is formatted the same as the summary chart but with a line for each gender.
Sentiment
Summary Chart - Sentiment by Second
Emotient Analytics assesses Sentiment by measuring how often the expressed emotions are
positive versus negative. For example, in the chart above, at 00:00:05, the sentiment is 8%,
indicating expressions of positive emotion modestly outnumbered expressions of negative
emotions between timestamp 00:04.5 to 00:05.5.
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Detailed Charts
Overall Sentiment by Second (or by Quarter): Breaks down the summary chart into Positive,
Negative and Neutral lines displayed on a single graph.
Overall Sentiment: A pie chart dividing the FaceSecond(s) across the entire video into Positive,
Negative and Neutral wedges.
Sentiment by Gender: A bar chart showing a single overall Sentiment number for each gender.
Participants by Gender: A time-weighted male/female percentage split of the faces detected in
the video, whether or not they were paying attention or showing emotion.
Overall Sentiment by Gender - a bar chart showing a count of Positive, Negative and Neutral
Sentiment FaceSecond(s) captured for each gender.
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