Coding open-end responses in Protobi

Open-end coding is an essential process for analyzing qualitative data in market research, such as survey responses and customer feedback.

It connects free-text survey responses to structured, quantifiable analysis by assigning consistent labels (or codes) to free-text answers. This helps you:

• Understand why customers feel the way they do.
• Uncover research insights and nuances to guide decision-making.
• Discover new topics you may not have thought about when planning the research.

For example, several open-end answers to the question "What do you like most about this product?" could be coded as "Easy to Use" — turning dozens of unique answers into a single measurable theme.

Protobi groups similar responses into clear themes and displays the percentage for each theme.

Protobi provides multiple tools for analyzing and visualizing qualitative data.

Word clouds

A Word Cloud is a visual representation of your text data where the size of each word indicates how frequently it appears in responses. This helps analysts instantly spot dominant topics across large sets of unstructured data.

Read the full guide on Word Clouds →

Verbatim charts

Verbatim Charts are a visual tool used to summarize and display qualitative data. They allow users to review individual open-end responses directly within the analysis view.

Read the full guide on Verbatim Charts →

How open-end coding works

Coding is a way to distill many text responses into key ideas that can be quantified.

For example, given the following responses, a researcher might extract these codes:

Given a set of text responses...	Extracted codes
"Reusable a plus. Looks easy to use and handle. Easy to read."	Reusable, Easy to use
"Looks very user friendly and I am happy it can be reused."	Easy to use, Reusable
"it may be awkward to manipulate if you are arthritic. I like the idea of reusable though"	Accessibility, Reusable

Code is a category, a theme, or an idea that we can use to classify responses. A group of codes is called a codeframe.

When a response is assigned under a code, that is called an assignment - basically a response-category pair.

Nets allow you to roll up detailed codes into broader thematic categories for reporting and high-level analysis.

Limitations of manual coding

Manual open-end coding requires analysts to read every response individually, build a codeframe from scratch, and assign codes one by one. For large datasets, this process can take hours, and multiple revision rounds are often needed to ensure consistency across coders.

This is where Protobi AI Autocode can help. By automating the most time-consuming parts of the coding process — theme identification, codeframe generation, and response tagging — AI Autocode significantly reduces the manual effort required while keeping the researcher in full control of the final output.

Coding verbatims with Protobi AI Autocode

Press the AI Autocode button (bot icon) in the top-right corner of your open-end element.

The Autocode dialog appears, where you can select the type of autocode. Select the "Create + Apply" option.

Protobi AI will then process your responses and automatically generate a codeframe with relevant categories, tag each response with the appropriate codes, add a short description for each code, and display an overall summary in the Autocode results panel.

Common open-end coding use cases

Not all open-end questions look the same in your data. Here are the three most common structures and how Protobi handles each one.

Single question coding

The simplest case is one column, one response per respondent, one code assigned. For example, in a question such as "What do you like most about this product?" — each respondent gives a single answer, and Protobi tags it with the most fitting code from your codeframe.

Multiple response coding

Sometimes respondents can answer in more than one way — resulting in several columns capturing different parts of the same answer.

Rather than treating each column in isolation, Protobi consolidates them using the Condense (Squish) transform and tags the combined response with a single unified code.

This provides a consolidated view while preserving the full detail of each individual response.

Multiple independent questions into a common codeframe

When you have several distinct open-end questions that all touch on similar themes, you can run them through the same codeframe. Instead of building a separate codeframe for each question, Protobi lets you apply one shared codeframe across all of them — making it easy to compare patterns and spot trends across questions consistently.

Multi-level coding with Nets

When a codeframe grows large, Nets help by grouping related detailed codes into broader thematic categories — giving you two levels of analysis: granular codes for deep dives and rolled-up Nets for high-level reporting and client presentations.

For example, codes like "Easy to Use," "Dose Indicator," and "Similar" can roll up into a single Net called "Product Experience." To generate Nets automatically, check Generate higher-level net categories in the AI Autocode dialog when running Create or Augment. Users can also edit and reorganize Nets at any time inside the Net editor.

Multiwave and tracking studies

When the same survey is fielded across multiple waves, consistency in coding is critical. Protobi's Apply option tags all new responses against your existing codeframe without generating new categories or modifying existing codes — ensuring clean wave-over-wave comparability. If new themes emerge in a later wave, Augment lets you add new codes without disturbing the existing codeframe.

Before coding a new wave: Clone your element first to preserve the original raw responses as a clean backup.

Human and AI in open-end analysis

Protobi AI handles the time-consuming parts — sorting through large volumes of responses and identifying recurring patterns. You stay in control of the final decisions. AI Autocode generates an initial codeframe that researchers review, refine, and approve before finalizing. This approach reduces manual effort while preserving the researcher's judgment and domain expertise throughout the process.

The role of AI

• Speed: Significantly accelerates the initial categorization of responses compared to fully manual workflows. Processing time varies based on dataset size and response complexity.
• Patterns: Identifying recurring themes across large response sets.
• Summaries: Distilling massive datasets into quick thematic groups.

The role of humans

• Context: Reading between the lines to find the why.
• Quality control: Catching nuances and sarcasm that algorithms miss.
• Strategy: Applying domain expertise to interpret findings and develop actionable recommendations.

Note: AI Autocode accelerates the initial coding process, but thorough human review remains essential — particularly for nuanced or high-stakes research.

Step-by-step guide to coding open-ends in Protobi's recode tool

The following steps walk you through coding open-end responses using Recode (advanced).

1. Select the open-end element Press on the element you want to code.

2. Open the Recode tool Open the toolbar and select Recode (advanced).

3. Run Autocode Press Autocode with Protobi AI in the lower-right corner.

You'll see two main options:

• Create — Builds a codeframe from the open-end responses, but does not tag any responses yet. Use this if you want to review and adjust the codeframe before applying it.
• Create + Apply — Builds the codeframe and tags every response automatically. As part of this step, Protobi also runs an internal AI evaluation behind the scenes, so what you get back is already a validated, ready-to-review result — not just a first pass.

Select the option you want and click Ok.

4. Review the results Browse through the codes and assignments Protobi generated.

5. Make adjustments Unapply a code to remove its tags from responses while keeping the code in your codeframe. Delete a code to permanently remove it from the codeframe and all associated assignments. When your review is complete, press Save in the project toolbar to make all changes permanent.

6. Evaluate your coding Once you are satisfied with your codeframe, you can use the Evaluate option in the AI Autocode dialog to validate the quality of your coding assignments before finalizing.

How to run it:

• Press the AI Autocode button in Recode (advanced).
• Select Evaluate and press Ok.
• Once complete, press Review in the toolbar.

What it does: Evaluate generates an independent AI reference solution and compares it against your current coding — highlighting where the two agree and where they differ. This helps you catch any assignments that may need a second look before delivering final results.

Reading the comparison:

• In both schemes — Both solutions agree → Keep
• In current scheme only — Only in your coding (Blue Tags) → Review
• In reference scheme only — Only in AI reference (Dotted White Tags) → Consider adding

1. + / - next to a code — the AI reference suggests adding (+) or removing (-) that code for this response.
2. Thumbs up / thumbs down — your decision. Press thumbs up to approve the AI's suggestion, or thumbs down to reject it.
3. -1 / +9 style numbers in the Code set panel — the net change to that code's count if you accept all its suggested additions/removals.

Go through the flagged (+ / -) codes and press thumbs up or thumbs down to record your decision on each one before finalizing.

For a full walkthrough of the Evaluate workflow, including how to review comparison results and accept changes, see this guide on evaluating coding quality →

Recode tools

Protobi gives you two ways to work with open-end responses depending on the size of your dataset and how much control you need.

Simple Recode

Best for small datasets — a quick concept test or a pilot study. You can filter by keyword, group similar responses into a theme, and reorganize as you go. Simple Recode requires no configuration and is ready to use immediately.

Read the full guide on Simple Recode →

Recode (advanced)

Built for large studies. When you have hundreds or thousands of responses, Recode (advanced) gives you a full workspace — AI-assisted coding, quality review, hierarchical Nets, and export options.

Read the full guide on Recode (advanced) →