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Data Visualization Techniques

Interactive Visualizations to Illustrate Customer Insight

Amplify the impact of text analytics even further by combining with efficient data visualization techniques to turn data into information for better and more efficient decision making.

Data visualization technologies can be as powerful as they are easy to use, allowing you to quickly and easily understand, articulate and share the insights across your organization to others who are less comfortable with the nuances of data analysis.

Below are some sample data visualization outputs that can add impact, clarity and value to your customer experience analysis.

Dashboards

Dashboard
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Dashboard management information systems are easy to read global displays of real-time data. The user interface consists of graphical representation(s) of key indicator. They offer a global view of the data in question and therefore enable quick, visual identifications of problematic areas. All the charts are interconnected: clicking on an element of one chart filters all the other charts on that particular element.

Co-Occurrence Charts

Co-occurrence
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Co-occurrence charts, maps or matrices go further by representing the strength of the relationship between words, using frequency of co-occurrence. It is often here where the most valuable insights are found. A Co-occurrence Map allows you to go beyond the relative popularity of the concepts and identify concepts that are strongly associated as well as those rarely associated in the minds of participants.

Correlation Matrix

Correlation
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A Correlation Matrix provides a short cut to words that are either positively or negatively correlated, often revealing otherwise highly obscure relationships and potentially critical operational issues or opportunities.

Mirror Charts

MirrorChart
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Mirror Charts quickly show you your customers’ sentiment relative to a baseline average, revealing key competitive strengths and weaknesses.

Star Charts

StarPlots
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Star Charts allow you to easily see relative importance of various elements within a category, so you can optimize your focus on the ones that will truly drive sentiment. These also show sentiment shifts on certain elements across categories, where something may be extremely relevant in one, and of little consequence in another.

Heat Maps

HeatMap1
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Heat Maps represent Sentiment across four quadrants: strong positive, positive, negative and strong negative. These give you a view of the factors driving satisfaction or dissatisfaction, and allow you to quickly see areas of particular concern or opportunity.

Enhanced Word Clouds

Cloud
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Word Clouds are graphical representations commonly used to give word frequency data more impact, but they reveal nothing about relationships between words.

For more information on Data Visualization capabilities and products, download the Ascribe Illustrator product sheet.

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