Open-Ended Text Use Case
A guide to implementing Relevance AI open-ended text use case, including data structure and sample questions.
Problem Statement
Understanding verbatim comments, answers to open-ended survey questions, is often key to unlocking insight. Identifying emerging themes from text data often forms part of strategic initiatives required for executive reporting - such as in the context of customer or employee feedback projects/surveys - or ongoing business operations, such as in the context of customer service and support teams, requiring insight to be presented at team standups or weekly huddles.
Who Are the Typical Editors and Viewers of This Use Case?
- Editors: Market Researchers, Insight Analysts, Business Analysts, Data Analysts
- Viewers: Insight Managers, Research Managers, Project Managers, Team Leaders, Customer Teams, Chief Experience Officers, Chief Customer Officers
How Does It Work?
An Example of Data Structure: Open-Ended Text Use Case
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Please refer to this article for more information about preparing data.
Text Fields
A text field, also called a string variable, is a qualitative open-ended response. Here are some of the typical text fields we find in a survey use case used in Relevance AI:
- Open-ended customer feedback. Answers to questions like: Please give reasons for this rating
- Identify opportunities for improvement: The single biggest improvement we could make to the website for you is?
- Impact analysis: If, at all, how has this impacted you to date?
- Product feedback: Which features and functionality do you most like to use and why?
Measures
A measure is a quantitative, numeric value. Here are some of the typical measures we find in survey data used in Relevance AI:
- Feedback Metrics: Satisfaction, Net Promoter Score (NPS), Revenue, Sales, Financial Metrics
String or Categorical Fields
Unless a numeric measure, Relevance AI treats all other fields as string variables. Here are some of the typical string fields we find in a survey use case used in Relevance AI:
- Customer Demographics: Gender, Age, Location, Education, Income etc.
- Product characteristics: Product category, Product sub-category, Product type, Product name, etc.
- Team attributes: Team Name, Region, Channels, Sales Office Location, Account Manager, etc.
What Sort Of Insights Can Relevance AI Help Me Uncover?
Identify Key Themes
- Understand emerging themes driving customer feedback / satisfaction
- Summarize open-ended responses with both high-level tags and granular sub-tags
Understand Feedback Patterns
- Filter by customer sentiment: positive, negative, neutral
- Pinpoint feedback by emotion: e.g. anger, dissapointment, frustration
Uncover New Opportunities
- Understand satifaction across key customer demographics
- Satisfaction by channels, products, regions
- Track themes over time (time series data)
How To Get Started: Open-Ended Text Use Case
Recommended Workflow
Tips
- Experiment with clustering. Finding the optimal number of clusters requires at least a few Clustering and reviewing the results
- Spend time reviewing the AI suggested tags before actually tagging the dataset. This will decrease the edit time.
- Include all fields that you might need to extract insight when analysing your data
- Make sure to anonymize sensitive data before uploading your data. Alternatively, you can use anonymize but a review over the results is highly recommended.
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Updated 4 months ago