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Why This Format Matters

CoLoop’s Open End Analysis product is designed to handle diverse data from various sources efficiently. By adhering to this standardized format, you ensure that your data is processed accurately, regardless of its original structure. This approach allows us to provide a consistent, high-quality analysis experience for all users.

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File Requirements

• File format: .xlsx (Excel)
• Maximum responses: 10,000

Files exceeding 10,000 responses (n.b. not respondents) will trigger an error. Contact support if your use case exceeds this limit.

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Data Types

Open Ended data will either be question data (survey style data) or conversation data (from chatbots or interview transcripts). CoLoop requires different formatting for these two types of data. Additionally you can add Respondent data, which is demographic information about your participants. Question Data is survey-style question and answer data. Required columns:

• “Respondent ID”: Matching the ID from the Respondent Data sheet
• Question columns: Headers contain the question text, cells contain answers

Optional columns:

• “[Tag]” or “[Tag] TagName”: Tags applied to all responses for that respondent
• “[Segment]” or “[Segment] SegmentName”: Additional segmentation data

Example:

Respondent ID	What improvements would you suggest?	[Tag] Satisfaction	[Tag] Survey Round	[Segment] Customer Type
R001	Add more integrations	Very satisfied	Round 1	New
R002	Improve Wi-Fi connectivity	Somewhat satisfied	Round 1	Returning

**Conversation Data **is conversational data, such as chatbot data and interview transcripts. Required columns:

• “Respondent ID”: Matching the ID from the Respondent Data sheet
• “Turn Number”: Integer representing the order of conversation turns (must start at 1 and increment sequentially for each respondent)
• “Question”: The question asked in that turn
• “Answer”: The respondent’s answer

Optional columns:

• “[Tag]” or “[Tag] TagName”: Tags for each conversation turn
• “[Segment]” or “[Segment] SegmentName”: Additional segmentation data

Example:

Respondent ID	Turn Number	Question	Answer	[Tag] Sentiment	[Segment] Device
R001	1	How was your experience?	It was great!	Positive	Smartphone
R001	2	What did you like most?	The quick setup.	Positive	Smartphone

Respondent Data is demographic information about your participants (e.g. age, gender, country etc…). Required column:

• “Respondent ID”: Unique identifier for each respondent

Optional columns:

• “Respondent Name”: Name of the respondent
• “[Segment]” or “[Segment] SegmentName”: Demographic or other segmentation data

Example:

Respondent ID	Respondent Name	[Segment] Age	[Segment] Gender
R001	John Doe	25-34	Male
R002	Jane Smith	35-44	Female

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Excel Structure Overview

There are two main ways to format your excel files depending on how your study is structured. Option 1: Combine Respondent and Response Data in One Sheet Best for: Simple studies with one sheet of responses per file (such as open ended responses from surveys). In this format, every row contains both the response and the respondent’s segment (demographic) information. Option 2: Separate Respondent Sheet + Question Sheet Best for:
Studies with multiple sheets of responses (e.g. one sheet per product or condition), or when doing within-participants testing. In this format:

• You include a Respondent Info sheet with metadata like age, gender, or market segment.
• Each Question/Response sheet contains open-ended feedback, linked by Respondent ID.

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Downloadable Examples

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Open Ended Survey - One Sheet

This sheet is an example of the Option 1 formatting style, where respondent information and open-ended responses are combined in a single sheet. Download Here Example data is a simple open ended survay about the taste and texture of crisps.

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Open Ended Survey - Respondent & Question Sheet

This sheet is an example of the Option 2 formatting style, where respondent information and response data are stored in separate from sheets. Download Here Example data is a simple open ended survay about the taste and texture of crisps.

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Open Ended Conversation Data (Between Participants)

This sheet is an example of open ended conversation data, so each respondent has multiple turns. Respondent information and response data are stored in separate from sheets for clarity. Download Here Example data is a chat-bot conversation where each participant responded to a series of questions about their experience using a specific painkiller. This is a between-participant example so participants trialled either Ibuprofen or Paracetomol. Each participant had 5 conversation turns about their experience, with responses tagged by the product (e.g., Paracetamol, Ibuprofen). The respondent data sheet contains demographic segments; they enable analysis by user group.

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Open Ended Conversation Data (Within Participants)

This sheet is an example of open ended conversation data, so each respondent has multiple turns. Respondent information and response data are stored in separate from sheets for clarity. Download Here Example data is a chat-bot conversation where each participant responded to a series of questions about their experience trying 4 different chocolate products. This is a within-participants example so participants all tried and answered questions about all 4 chocolate bars. Each product has its own sheet which contains the conversation data per product and a [Tag] for that product, and a separate respondent data sheet includes participant demographics.

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Best Practices and Tips

1. Focus on Open-Ended Data: Ensure that your Excel file only includes open-ended questions and responses. Close-ended questions should be formatted as tags or segments instead.
2. Data Cleaning: Before import, thoroughly clean your data:
   • Remove any fully blank rows or columns
   • Ensure consistent formatting across cells
   • Check for and remove any hidden sheets or data
3. Validate Respondent IDs: Use Excel’s data validation tools to ensure Respondent IDs are unique and consistent across sheets.
4. Check Turn Numbers: For Conversation Data sheets, verify that Turn Numbers start at 1 and increment sequentially for each Respondent ID.
5. Use Clear Naming Conventions: Choose descriptive names for your segments and tags to facilitate analysis.
6. Keep It Simple: If a column doesn’t apply to your data, leave it out rather than including empty values.
7. Review Before Import: Double-check your file for accuracy and completeness before attempting to import it into CoLoop.

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Common Pitfalls to Avoid

1. Mixing Open and Closed-Ended Data: Don’t include closed-ended questions as separate columns. Instead, use tags or segments to represent this information.
2. Inconsistent Respondent IDs: Ensure that Respondent IDs match exactly across all sheets where they appear.
3. Non-Sequential Turn Numbers: In Conversation Data sheets, make sure Turn Numbers are sequential without gaps for each Respondent ID.
4. Exceeding Response Limit: Remember that projects with more than 10,000 responses will by default trigger an error.
5. Using Unsupported File Formats: Only .xlsx files are supported. Convert your data if it’s in a different format.
6. Incomplete Data: Ensure all required columns are present in your sheets.
7. Inconsistent Naming: Use the exact column headers specified in this guide, including the brackets for [Tag] and [Segment] columns.

By following these guidelines, you’ll ensure that your data is correctly formatted for import, enabling smooth and accurate analysis of your open-ended responses. If you encounter any issues or have questions about formatting your data, please don’t hesitate to reach out to our support team.