Autocoded Verbatim Chart
Ever wonder what are the respondents saying about certain aspects of the stim you’re testing? Well, lucky for you, there is a way to see this open-ended feedback! When looking at the heatmaps, simply drag your cursor, forming a square, over any section of the heatmap and the open-ends will pop up.
But there's more, the Autocoded Verbatim chart from Zappi’s Autocoding functionality will help you explore the most prominent topics that arise from responses to open-ended questions. The chart gives a high-level overview of what is being said about the stimuli.
To see the open-ends, click into a specific colored section, open the sidebar and drop down the carrot icon to see what the respondents had to say! Learn how:
Learn how to navigate through the chart
Deep dive into the Autocoded Verbatim chart and learn how to navigate with this interactive walkthrough. You can drill down through our three-tiered hierarchy of topics to explore the proportion of mentions against each topic.
Features of this chart include:
- Click to drill down through the three-tiered topic hierarchy (Supertopic, Topic, Subtopic).
- See verbatims classified against each topic.
- Topics can be hidden from view (note that hiding topics does not recalculate metrics on the chart).
- Search for verbatims with keywords or phrases.
- Configurable to include verbatim responses across one or more tests.
- Compatible with filtering for cohort analysis, so you can break down the audience into smaller groups.
- Can be built into stories and exported to Excel, PPT, and PDF.
- (For non-English studies) Verbatim responses and classifications can be viewed in English and the fielded language.
- (For non-English studies) Excel export contains the original language and translated verbatim responses.
Autocoding’s “Percentage of mentions” metric
A verbatim response can contain more than one topic. Therefore, we calculate the proportion of topic allocations where a given subtopic is mentioned:
[Number of verbatims that contained topic X] / [total number of topics extracted from all verbatim]
For example, 150 respondents answer a question around what they like about an ad, 200 topics are present within the verbatim responses, and 20 of the verbatims mention the ad demonstrated being better than other brands. In this case, the percentage of mentions of “better than other brand” will be 10% (20 verbatims / 200 topic-verbatim allocations).