What are Metadata Correlations used for? 

Metadata Correlations are identified automatically by the system if there's a statistically significant relationship between comments and accompanying data.  


Sample Use Cases

Segmenting customers: Comments & Demographic data
Find out if different categories of customers value different things using demographic data: Male vs. female, different age groups, level of education. 

Prioritizing customers: Comments & Satisfaction rating
Do you need to focus on making happy customers happier or preventing unhappy customers from churning? Prioritize where to take action based on the segment you're interested in. 

Ensure consistent service across all locations: Comments & Location data
Are certain locations doing a lot better or a lot worse? Learn from high-achieving locations and improve the ones lagging behind. 

We will explore a similar example below.

How do I use Metadata Correlations?

Here's a step-by-step walkthrough.  

1. Upload the necessary data

Along with unstructured data such as comments, you'll need to upload accompanying metadata such as location, satisfaction rating or demographic data. Here is an example:

The first column contains review text. These are the comments that will be analyzed with Keatext’s NLP engine. The second column contains location metadata. Once the analysis is complete, the system will calculate and highlight any interesting relationships.

2. Let the analysis finish

The analysis needs to be complete before the system compares the distributions automatically (by running a chi square test) and displays the statistically significant correlations. Attempting to see the correlations before the analysis is done will result in an error message. 

3. Investigate the highlighted Metadata Correlations

For each identified Issue, the Metadata Correlation is computed individually. In this example, we are interested in the Issue “the food is great!”  Click on the row to reveal the analytics summary for that specific Issue. 

Underneath the summary panel, click on the Metadata Correlation tab to reveal what the system found automatically. 

There are two ways the Metadata Correlations are displayed. The default view is a donut chart. When there are too many values, the table is more helpful. Click on the table icon to get a better understanding. 

In the example, 22% of people who said "the food is great!" (or variations of the same idea) were in Paris, followed by 10% in New York City and 9% in London. 

4. Explore other relationships

It is also possible to add correlations manually, but it’s up to the user to decide if they are significant or not. Scrolling below the correlation chart or table will reveal the Create Metadata Correlation button. 

To add a correlation, the user needs to select the data Source and the relevant Field (column from the dataset).  

How accurate are Metadata Correlations?

The purpose of the Metadata Correlations is to highlight interesting information. Statistical relevance depends on the amount of data uploaded. We recommend users upload several thousand responses to reach statistically significant insights.

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