How to Analyze Typeform Survey Data: From Export to Insight

Typeform makes it easy to collect answers. Turning those answers into findings that a product, marketing or customer experience team can act on takes more work: a form designed with the analysis in mind, a clean export, a sound way to read ratings and a consistent way to read open-ended comments.

This guide follows that work in order. It explains how Typeform stores and exports responses, how to design a form that is easy to analyze, how to analyze ratings and multiple-choice answers in a spreadsheet, and how to code open-ended answers by hand or with Typeform's own Smart Insights. It then shows where Kimola fits once the number of responses, segments or survey waves grows beyond what a spreadsheet handles well.

How Typeform Organizes Your Responses

Every form has a Results panel. The Summary view shows overall trends for each question, and the Responses view is a table with one row per respondent. On plans that include it, Smart Insights adds an AI summary of the results. In the Responses table, you can filter by date, by answers to Multiple Choice, Picture Choice, Matrix and Dropdown questions, by tag, and by whether a response is partial or completed.

When you export, that table becomes a spreadsheet with one row per response and one column per question. A few details matter for analysis. A multiple-choice question that allows several selections becomes one column per answer choice rather than a single column. Yes/No answers export as 1 and 0. Statements, welcome screens and thank-you screens do not appear, and forms created after June 10, 2025 also have an Ending column that shows which ending screen each respondent saw.

The export carries metadata as well: a Start Date and a Submit Date for each response, both in UTC, and a Network ID, a generated string tied to the respondent's IP address that Typeform uses instead of storing the address itself. If the form uses URL parameters (formerly called Hidden Fields), their values appear in the results too. These columns are easy to delete as clutter, but they are what makes it possible to compare groups and periods later, so keep them.

Designing a Typeform You Can Analyze

Start From a Specific Objective

Many analysis problems start at the design stage. An objective such as "improve customer satisfaction" gives no direction on what to ask or how to read the answers. "Understand why customers who signed up last quarter stop using the product within 30 days" does: it tells you whom to survey, which questions matter and which comparison the analysis has to make.

Tie the objective to a decision someone in the business will make. If the company plans to enter a new market, the survey might focus on unmet needs and local preferences; if the goal is retention, it might focus on what makes customers stay or leave. When each question can be traced back to that decision, the analysis has a clear shape before the first response arrives.

Pair Every Rating With a Follow-Up Question

A rating tells you how many people feel a certain way, not why. Typeform's NPS, Opinion Scale and Rating questions measure, and a Long Text question placed right after the rating, such as "What is the main reason for your score?", is what makes the number explainable. One open question after one key rating is usually enough, and it keeps the form short enough that people answer with care.

Carry Segments in URL Parameters

Typeform's URL parameters, formerly called Hidden Fields, pass values you already know into a form through its link, so you don't have to ask for them. You add them under Logic with Pull data in, and their values are recorded with each response. A CRM or email tool can fill them in automatically, for example with the customer's plan, the channel the invitation came from or the region. These are exactly the columns you will compare later.

Two cautions apply. Pass only what the analysis needs, and avoid personal data such as names or email addresses unless you have a clear reason for it. Decide parameter names before launch, too: Typeform's help center warns that renaming a parameter after it has collected responses deletes the data collected under the old name.

Keep Questions Single-Topic and Use Logic

A question that asks about two things at once, such as "How satisfied are you with our pricing and support?", produces answers no one can interpret. Ask about one thing per question. Typeform's Logic settings, which include branching, let follow-up questions depend on earlier answers: a respondent who gives a low score can be asked what went wrong, and one who gives a high score what they value most. That makes the open-ended answers more focused and easier to code.

Exporting and Preparing Responses

Exporting a CSV or XLSX File

Open the form and go to Results, then Responses. Select all responses with the checkbox in the header row, tick individual responses, or run a search; if nothing is selected or filtered, every response is exported. Click the Export icon, choose CSV or XLSX and click Export.

The file downloads to your device, and Typeform also emails a download link. For large exports, the link arrives by email when the file is ready. Exports cannot be made from the mobile builder, full responses cannot be exported as PDF, and files that respondents uploaded are downloaded separately as a ZIP file. Typeform's help article on exporting responses covers each option.

Sending Responses to Google Sheets

For a survey that keeps collecting, the Google Sheets integration saves repeated exports. Set it up from the form's Connect panel. Each completed response is added as a new row, with a column for each question plus Submitted At (in UTC) and a Token that identifies the submission. Partial responses are sent only if you turn that setting on. Past responses can be sent at setup for forms with up to 1,000 results, and Google's limit of 2 million cells per spreadsheet applies. The integration works on a free Typeform account.

Cleaning the File

Before any analysis, remove test submissions made while building the form, and decide how to treat partial responses; for most analyses, completed responses are the cleaner base. Check for duplicates, for example several submissions with the same Network ID a few minutes apart, and look at them before deleting, since several people can share one network.

Remove personal data the analysis does not need, such as names, email addresses and phone numbers, or replace it with an internal ID. Data protection rules such as the GDPR expect personal data to be limited to what a purpose requires, and an anonymized file is also safer to share with colleagues. Keep everything else: the dates, the ratings, the multiple-choice answers and the URL parameter columns.

Analyzing Ratings and Multiple-Choice Answers

Start with Typeform's Summary view, which shows the distribution of answers to each question, and use the filters in Responses to check a subgroup quickly. For anything beyond a quick look, work in the exported file, where the quantitative analysis is easier to control and to repeat.

Report distributions, not only averages. Rating scales are ordinal: the step from 6 to 7 is not necessarily the same size as the step from 9 to 10, so the share of respondents in each band often says more than a mean. For NPS, use the standard calculation defined by Bain & Company: respondents who answer 9 or 10 are Promoters, 7 or 8 Passives and 0 to 6 Detractors, and the score is the percentage of Promoters minus the percentage of Detractors, which ranges from −100 to 100.

Then cross-tabulate. A pivot table that puts rating bands against a URL parameter column such as plan or channel shows whether satisfaction differs by segment, and the Submit Date column shows whether it changes over time.

Be careful with small groups. If a segment has 15 responses, two or three answers can move its score by many points, and a difference that looks large may be chance. Report the number of responses next to every percentage, and treat differences between small groups as questions to check rather than findings.

Analyzing Open-Ended Answers

Open-ended answers explain the numbers, and reading them well is a qualitative research task. There are two practical ways to approach them inside a Typeform workflow.

Coding Answers by Hand

The standard method is thematic coding. First, read a random sample of perhaps 50 to 100 answers and note the topics that come up. Turn those notes into a codebook: a short list of codes, each with a name, a one-sentence definition and an example answer. Then read every answer and assign the codes that apply, using one column per code in the spreadsheet. An answer that mentions several topics gets every code it mentions. Mark whether each mention is positive or negative, because delivery as a compliment and delivery as a complaint are different findings.

Once coding is done, count how often each code appears, overall and by segment, and pick a few representative quotes for each important theme. If two people code a shared sample independently and compare their results, the disagreements show which codes need clearer definitions and reduce the influence of one person's assumptions.

Using Typeform's Smart Insights

Typeform has its own AI analysis, Smart Insights, opened from the form's Results panel. For open-ended questions, it writes a summary of the main themes, groups responses into topics under Positive, Negative, Neutral and Mixed tabs, and shows up to three supporting quotes per topic. For closed questions, it shows charts with short summaries and lets you ask follow-up questions with Ask AI.

It is a quick way to read the main themes of a single form. Its documented limits define what it suits: it needs at least 10 responses, its topic analysis uses only the most recent 1,000 responses, and the form's language must be set to English. Its documentation does not describe comparing themes across your own segments, across periods or across several forms, which is where a research-grade analysis begins.

Where Spreadsheets Stop

Manual coding works well for a few hundred answers. Beyond that, three problems appear.

Keeping the coding consistent. Coding thousands of answers takes days, and the same person codes differently on the first day and the last. Adding a new code halfway through means rereading everything already coded.

Handling answers that cover several topics. An answer such as "The app is easy to use, but support took a week to reply" is positive about one thing and negative about another. A spreadsheet can hold this with one column per code and a sentiment for each mention, but the structure becomes hard to manage as the codebook grows.

Knowing which differences are real. With ten codes, five segments and four quarters, there are hundreds of possible comparisons, and some will look different by chance. Testing each one properly is rarely practical by hand. Percentages alone also mislead: a theme with 5 comments that are all positive is weaker evidence than a theme with 50 comments of which 43 are positive.

General chat assistants can summarize a few dozen comments well. Classifying thousands of records consistently, testing whether differences between segments are statistically significant, getting the same result from the same data every time and showing the source of every figure and quote require tools built for that work.

Analyzing Typeform Responses in Kimola

Kimola is an AI platform that turns customer feedback into enterprise-grade market research. It analyzes survey responses, reviews, support conversations and social discussions the way a research team would. It does not host or send surveys; it works with the file you export from Typeform.

Uploading the Export

From Home in Kimola, upload the Excel or CSV file. Choose the column that holds the open-ended answers and, optionally, a Date column; Submit Date works well for this. Every other column in the file is kept, including the NPS or rating answer, the multiple-choice answers and the URL parameter columns. Kimola reads each column's name and values to understand how it relates to the feedback, and these columns become filters and comparison groups in the Report. This is why the export should not be cut down to a single column of comments.

A Report analyzes one text column. If your form has several open-ended questions, create one Report per question, starting with the one that matters most, usually the follow-up to the main rating. Kimola's guide to uploading a dataset describes each step, including choosing the Report's output language and seeing how many queries it will use before it is created.

Themes, Sentiment and Dimensions

Kimola finds themes in the data itself rather than from a predefined list, and names each one after what respondents actually say. With multi-label classification, an answer that praises the interface and complains about response times gets both themes, each with its own sentiment, which is known as aspect-based sentiment. Answers in 30+ languages can be analyzed together in one Report, and theme names and summaries are written in the output language you choose.

Dimensions go further by asking a research question of every answer. The core Dimensions are Motivations, Pain Points and Unmet Needs; others include Personas, Experience Stages and Underlying Causes. They can be added when the Report is created or afterwards, they use additional queries, and they do not change the original dataset.

Signals Across Your Own Columns

Signals are where the export's other columns matter most. Kimola crosses every theme and Dimension with the columns in your file and with time, tests each combination, and keeps only the statistically significant ones. Differences show a theme mentioned more often, or more negatively, in one group than in the rest, for example among Detractors on one plan. Trends show a theme becoming more or less common across survey dates. Signals are scored and ranked with deterministic methods, so the same data always produces the same results. If the dataset is too small or no Dimensions are selected, a Report may have no Signals; that means nothing stood out, not that the analysis failed.

Volume is part of the scoring elsewhere too. On the Performance screen, the Satisfaction and Dissatisfaction Scores take the number of comments into account along with the share, so a theme with 5 positive comments does not outrank one with 43 positive comments out of 50.

From Findings to Action Plans

On the Actions screen, Kimola can write an Action Plan for a Profile, which describes the company, brand or product the plan is for. It forms hypotheses from the Report, tests each one against the feedback, leaves out recommendations the evidence does not support, and shows the evidence behind each action along with a confidence level. Reports can be exported to PowerPoint, PDF and Excel for sharing.

What Kimola Does Not Do

Kimola does not design surveys, host forms or collect responses; Typeform does that, and Kimola starts once the data exists. If a survey receives a few dozen comments, reading them directly is usually enough. Kimola becomes worth using when there are hundreds or thousands of answers, several segments or several survey waves to compare. Kimola has a free Starter plan with no credit card required, and paid plans start at $49 per month, as listed on the pricing page.

Turning Findings Into Decisions

A finding is useful when it reaches the team that can act on it, together with its evidence. Send each finding to its owner: a pain point about onboarding to the product team, a complaint about delivery times to operations. Include the number of responses, the segment it applies to and two or three quotes, so the team can judge its weight for themselves.

Then measure again. Running the same survey on the next wave, with the same questions and URL parameters, shows whether a change made a difference. For a survey that runs continuously, such as a quarterly NPS survey, Kimola's Enterprise package offers Spaces: each new file is classified with the same set of models and Dimensions, so results stay comparable over time, and Signals run again after every new file.

Frequently Asked Questions

Can Typeform analyze open-ended answers by itself?

To a degree. Smart Insights, opened from the Results panel, summarizes the main themes of open-ended answers, groups them into topics by sentiment and shows example quotes. It analyzes the most recent 1,000 responses of one form and requires the form's language to be English. For comparisons across segments, periods or several surveys, export the responses and analyze them in a spreadsheet or a dedicated tool.

How do I export Typeform responses to Excel?

Open the form, go to Results, then Responses, select the responses you want (or none, to export them all), click the Export icon and choose XLSX. The file downloads, and a download link is also sent by email. Exports are not available from the mobile builder.

How many responses do I need before the analysis is meaningful?

There is no fixed number, because it depends on how many groups you want to compare. A few dozen responses can show the main topics, but each segment you compare needs enough responses of its own for a difference to be more than chance. Report the number of responses with every percentage, and treat results from small groups with caution.

Can I analyze several Typeform surveys together?

Yes, if they ask the same open-ended question. Combine the exports into one file, add a column that identifies the survey or wave, and keep the answers to that question in a single text column. In Kimola, the survey column becomes a comparison group, so Signals can show which themes differ between surveys.

Conclusion

Typeform data becomes useful when the work is done in order: a form designed around a decision, an export that keeps its dates and segment columns, ratings read as distributions and by segment, and open-ended answers coded consistently. A spreadsheet handles this well for a few hundred responses.

When responses run into thousands, or when the question is which differences between segments and periods are real, the same export can go into Kimola. An organization that already has Typeform data can upload it as it is and turn it into enterprise-grade market research, without designing a new study. You can create a free account and start with an export you already have.