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      What to do with my open-ended responses?

      This article will provide you with a comprehensive guide on how to analyse open-ended (OE) responses on the platform.

      This article will provide you with a comprehensive guide on how to analyze open-ended (OE) responses on the platform.

      How to analyse open-ended (OE) responses?

      To ease the analysis and report preparation, usually we will group the open-ended responses into buckets or categories. Here are a few ways on how to group responses (AKA OE coding):

      • Grouping the answers as it is
        Example question: When thinking about fast food, what brands come to your mind?
        The best example for this scenario would be a top of mind brand question. The respondents might have some typos or put in the abbreviations instead of the full brands. Further grouping or organizing the responses would ease the data analysis. 
      • Grouping into product categories
        Example question: What is the brand that you think represents your values the most?
        Since we didn't specify or limit to a brand category, the respondents are free to input any brands they like. In this case, it might be not so useful to group the brands as it is because the count may be too low. We can group the brands by their product category along with some top brands mentioned instead, e.g. clothing brands (e.g. Uniqlo, Zara etc.), sports apparel brands (e.g. Nike, Adidas etc.).
      • Grouping into broader topics / subjects
        Example questions: What do you like about brand X? / If you were to use a phrase to describe brand X, what would that be?
        Every consumer cares about different aspects, hence will most likely have a different perspective for a brand. In this scenario, we can group their responses by some broader categories, e.g. price related, product features related, brand value related etc. In our topline analysis, we can determine what is our strength / what people like about us easily. If necessary, we can then further look into their verbatim and understand what the consumers mentioned.
      • Grouping by sentiments (positive, neutral, negative)
        Example questions: What do you think about the initiatives carried out by brand X?
        The responses we will get from this question are likely their thoughts and might be difficult to group into broader topics / categories. Grouping into sentiments might be more suitable for this type of questions, to help us gauge people's opinions.
      • Grouping the answers into categories
        When the answers are too scattered to group by itself, we can consider grouping them into the respective categories.

      How to analyse OE responses on the platform?

      In the dashboard, you will have 4 modes of viewing the open-ended responses.

      • Word Cloud
        Compose of words mentioned in the respondents' answers, in which the size of each word indicates its frequency (*limitations)
      • Word Count
        Show the words mentioned in the respondents' answers, along with the frequency (*limitations)
      • Full Responses
        Verbatim responses that were written in the survey
      • Tags
        Allow users to group/tag the responses directly into buckets, and show the tags in bar chart or word cloud mode. Click on "Manual Tagging" to start assigning tags to the responses!

      How to use the Manual Tagging tool on the platform?

      Here are some of the features in the manual tagging tool:

      Tag single response

      1. Click on "Add Tag" or the "+" icon for the response you'd like to tag manually.
      2. Enter the tag and click "Create New: +" to add the tag. If the tag has already been created, you can click on that tag from the dropdown list directly.
      3. After you click "Save Change", you'll see the the response has been tagged with the tag you created.

      Tag all responses

      1. You can enter the keyword in the search bar. If all the responses are relevant / fit your search, click on "Select all responses" and "Tag x Responses".
      2. In the pop up box, enter the tag and click "Create New: +" to add the tag. If the tag has already been created, you can click on the tag from the dropdown list directly.
      3. After you click "Save Change", you'll see the the responses have been tagged with the tag you inserted.

      Delete / Remove tag(s) from individual response

      To remove a tag, simply click on the "-" icon associated with the tag.

      Delete / Remove tag(s) from all the tagged responses

      To remove a tag entirely, you can hover over the tag, click on the dot icon and select "Remove tag".

      Apply tag filter(s)

      To filter by a specific tag or multiple tags, simply click on the tag(s) and you'll be able to view all the responses that have been tagged with that particular tag.

      To clear the tag filter, simply click on the tag(s) again or click on the "All Responses" tag. The tag "All Responses" and "No Tags" are exclusive tags, i.e. these tags cannot be selected at the same time with the other tags. 

      Merge multiple tags into one

      After you have tagged all the responses, you might want to combine multiple tags that are similar into a bigger category. For example, in this case we would like to combine the tags "Fixed Price" and "Cheap" . We can select the tags that we'd like to combine, and click the "Merge Tags" icon on the right hand side. In the pop-up box, insert the new tag "Price-related" and click "Save Changes".

      Go back to results dashboard

      There are 2 ways to return to the result dashboard:

      1. Click "Back to Insights" on the top left corner of the page.
      2. Click "Preview Data" and "Back to Insights" in the pop-up box.
        Under "Preview Data", you can also look at the tagged responses in either bar chart or word cloud format.

      Some limitations on the word cloud and word count:

      1. The word cloud and word count analyses word per word. In a top-of-mind brand question, the brand name would be split to multiple words under the following circumstances:
        1. If the brand name consists of 2 words or more, e.g. Cotton On, Burger King, Pizza Hut etc.
        2. If the brand name was mistakenly spelt as 2 words or more, e.g. Kit Kat (instead of KitKat), 7 Eleven / Seven Eleven (instead of 7-Eleven) etc.
        3. If the brand name was spelt incorrectly or differently, e.g. McDonald, McD, McDonalds, McDonald's. All these would not be treated as separate words, i.e. these words would appear separately in the word cloud and word count.
      2. The word cloud and word count are not available for the languages that are not using Roman/Latin scripts (e.g. Thai, Chinese).
      3. The word cloud and word count are not optimized for non-English responses, so you may see some 'stop words' (i.e. unimportant words in a sentence) included. Some examples of stop words in Malay are 'beberapa', 'yang', 'ialah' and 'itu'.