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What is Heatmaps?

Heatmaps is a tool for visualising which data sets in a group are most different and similar. The feature allows you to map the differences in each pair of data sets within a group.

Common uses

Heatmaps is especially useful when you are working with a large number of data sets and investigating each individual comparison may be impractical. Common use cases include:

  • Competitor (brand and product) benchmarking

  • Customer segmentation comparisons

  • Geographic market comparisons

Creating a heatmap

  1. Navigate to the Data Library and click the relevant project folder

  2. Select the checkbox next to the data sets you want to include in the Heatmap

  3. Select the generate heatmap icon in the action menu at the top of your data list

  4. Name your heatmap

A heatmap must include a minimum of three and a maximum of twenty data sets.

All data files must be in a single folder in the Data Library.

Difference percentiles

What are difference percentiles?

The topical differences in a pair of language sets are compared to determine the difference percentile.

The difference percentile represents the magnitude of the differences present in a pair of language sets in comparison to a benchmark sample - the higher the percentile the more different the pair of language sets are.

For example, a comparison scoring in the 80th percentile indicates that comparison is more different than 80% of all comparisons.

To view the difference percentile for each comparison, enable the toggle in the bottom-right of the heatmap.

How are difference percentiles calculated?

The difference percentile is derived from the correlation score between the linguistic features in your data sets.

This score for each pair of data sets is then compared against a benchmark distribution of difference scores generated from a sample of over 5,000 comparisons to understand how different a pair of data sets are.

Why do we use difference percentiles instead of raw scores?

The difference percentile is used rather than the raw correlation score because it provides a clearer understanding of the magnitude of differences between your two data sets. This approach helps you develop an understanding of the differences between your two data sets in the context of the wider landscape as represented by a sample of over 5,000 comparisons.

For example, knowing a pair of data sets is more different than 70% of all comparisons is more insightful than knowing that a pair of data sets returned a correlation score of 0.4578.

Using your heatmap

Clicking on a tile will present you with several options:

  • View - this will take you to the explore screen for the comparison where you can see all of the detail of the analysis and build insight cards

  • Dismiss - this icon can be used to note that you are not interested in further analysis for a particular comparison

  • Pin - this icon can be used to note that you want to do further investigation for the comparison

  • Favourite - this icon can be used to note a comparison that you have looked at and contains interesting insights

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