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Examining consumer perception via sophisticated text analysis tech.
Depending on who you talk to, between 60% and 80% of data collected by companies goes unused. That means that most companies are expending great effort to gather and store valuable information, but don’t have easy access to solutions or tools to make use of it.
At the same time, every year, billions of dollars are spent on costly, time-consuming qualitative and quantitative research despite the time- and cost-saving opportunity that text analytics presents to uncover rich, meaningful insights.
We call this waste.
At Metametrix, we’re changing that scenario. We’ve created an approach that coaxes more and deeper meaning from text, with the unique ability to decipher values, emotions, concepts. We are raising the bar on sentiment, turning it from a binary classification to one that ties the system of values, emotions, and concepts together. With a more nuanced view of sentiment, we can capture drivers of change and identify emerging behaviors. We’ve also added time to the equation, providing our users with a dynamic view of behaviors and attitudes as a platform for projecting market trends. Our system acts like Truth Serum For Big Data.
This capability can complement qualitative studies and quantitative formal surveys in meaningful ways – or sometimes replace them altogether.
To demonstrate this innovative service, we focused on the electric vehicle (EV) category to map its fascinating evolution. It’s been an exciting category to watch over the years, but we wanted to find out what was driving the changes in the market and in consumer perception, in what direction it was headed, and what could keep it moving forward.
We started by gathering 5000 articles from the public web, news source, and blogosphere, all related to electric cars and electric trucks. Then we processed the data through our analytics engine.
The typical place to start with text analytics is identifying keywords. We take a deeper, more complex approach to understanding the nature and context of the words and phrases that represent dominant themes or concepts in text. I love this stage. Key themes are where you get a sense for the focus of the content and the subject.

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Next, the Metametrix superpower is to identify Values and Emotions, the why of consumer insights. This is the Truth Serum at work. The Metametrix engine identifies the presence of Values (fundamental beliefs or needs that motivate our choices) and Emotions (psychological reaction to something we’ve encountered) to lend crucial understanding to this stage’s analysis.

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But we don’t stop there. Metametrix drills deeper by tying Values and Emotions to a shift in Sentiment, taking sentiment to another level of meaning and allowing for deeper analysis. First, Values below.
Sentiment Score By Top Values and Year

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* Horizontal red line corresponds to ratio value = 1 measuring where the values cross into the positivity range.
We also analyze for positivity movement of Emotions.
Sentiment Score By Top Emotions and Year

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* Horizontal red line corresponds to ratio value = 1 measuring where the emotions cross into the positivity range.
Another Truth Serum skill is to offer more understanding to the core issues for EVs by tying the key themes to values and emotions in order to see how the field is changing and what themes are driving the changes. Our analysis showed that battery is the key theme behind the top values of Ingenuity and Empowerment, and the emotions of Trust and Excitement, more than any of the other key themes. Right behind battery comes climate, which is highly tied to the same top values and emotions.
To get a big picture view, we overlayed the sentiment we calculated in the analysis with Google search trends and public sales data for EVs to examine how the results compare with other measures.

METAMETRIX
Instead of isolated and fixed keywords and sentiment, our unique approach to extracting and combining Values, Emotions, and Sentiment adds unparalleled depth to text analytics to point your business in the right direction.
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The views, opinions, data, and methodologies expressed above are those of the contributor(s) and do not necessarily reflect or represent the official policies, positions, or beliefs of Greenbook.
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