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How do I use Metadata Correlations to find insights on the Analysis page?
How do I use Metadata Correlations to find insights on the Analysis page?

Learn how to identify relationships between unstructured feedback and accompanying structured data on the Analysis page.

Amanda Robinson avatar
Written by Amanda Robinson
Updated over a week ago

What are Metadata Correlations used for? 

Comments, Topics an Opinions can be broken down by metadata fields to reveal volume and sentiment trends.

Sample Use Cases

Segmenting customers - Feedback & Demographic data:

Find out if different categories of customers value different things using demographic metadata: For example, male vs. female, different age groups, level of education.

Prioritizing customers - Feedback & Satisfaction rating:

Do you need to focus on making happy customers even happier or preventing the unhappy ones from churning? Prioritize where to take action based on the segment you're interested in. 

Ensure consistent service across all locations - Feedback & Location data:

Are certain locations doing a lot better or a lot worse? Learn from high-achieving locations and improve the ones lagging behind. 

For each identified Comment, Topic or Opinion a Correlation is displayed individually.

In this example, we are interested in the Comment “customer service is horrible.'' Click on the row to reveal the Comment Details for that specific Comment. 

The Comment Details popup has tabs displaying Records, Correlations and the Level of Interest over time.

Select the Correlation tab to reveal a bar graph that can be adjusted to whichever metadata field you choose. In this case we chose "Age" range. Most of the Comments meaning "horrible customer service" came from people over the age of 65, followed by those in the 25-39 age range.

The metadata field can be adjusted by choosing from the dropdown accessible by clicking the filter icon at the top-right of the graph.

Note: When creating Correlations from the Comments view only one Sentiment will appear as Comments are bucketed into one of the sentiments during the text mining phase.

4. Let's look at a Correlation on a Topic!

Here in addition to Volume we can see a breakdown by Sentiment. In this case we moved to the Topic view and chose the Topic "Driver" and the metadata field "City". We can see that customers from New York, Toronto, and Los Angeles had a lot more negativity toward their food deliver driver that those in Denver and Chicago. This is a good opportunity to see what issues customer is those cities have related to "diver" and get ahead of them to keep those clients happy!

You can also display a relative distribution to compare the ration of positive to negative for each city.

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