A guide to implementing Relevance AI open-ended text use case, including data structure and sample questions.
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.
- 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
Please refer to this article for more information about preparing data.
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?
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
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.
- Understand emerging themes driving customer feedback / satisfaction
- Summarize open-ended responses with both high-level tags and granular sub-tags
- Filter by customer sentiment: positive, negative, neutral
- Pinpoint feedback by emotion: e.g. anger, dissapointment, frustration
- Understand satifaction across key customer demographics
- Satisfaction by channels, products, regions
- Track themes over time (time series data)
Updated 30 days ago