From disorganized survey submissions to actionable insight with Make and Rows

Surveys are essential for collecting customer feedback and market insights. However, there's an inherent tension in survey design: the more freedom you give respondents to express themselves authentically, the harder it becomes to extract meaningful data. You're stuck choosing between rich responses and scalable analysis.

How can you overcome this tradeoff? This is where an intelligent data analysis tool comes into play. In this article, I will guide you to build a simple Make workflow that exports your survey’s responses to Rows.com and helps you make sense of your data.

AI-Powered Data Analysis.

Rows.com is an AI data analysis tool that puts business teams in control of their data by enabling them to ingest, analyze, and transform data from documents and tools using natural language.

It bridges the gap between spreadsheets and advanced analytics through a flexible interface, a smart Copilot that understands natural language commands, and native AI functions that can tag data, analyze sentiment, extract information, and enrich content.

Building Your Automated Workflow with Make

In the following steps, you will see how to create an automated system that turns survey responses into business intelligence using Make and Rows.

Step 1: Create a Make Scenario

Start by setting up your automation workspace:

1. Log in to your Make account

2. Click on "Create a new scenario" from your dashboard

3. You'll see a blank canvas where you can add modules and connect your workflow

Step 2: Connect Typeform to Rows

Set up the automation bridge in Make with detailed field configuration:

Typeform - List Response Module:

• Connection: Select "My Typeform connection" (or create a new one using your Typeform API key)

• Form ID: Choose the survey name from the dropdown (this selects which form to monitor, e.g. "PMF Survey")

• Fields: Make automatically detects all form fields:

  • Item 1: This refers to the first item/question in the survey. In our case, "Please type your email address" - captures respondent contact info

  • Item 2: "How would you feel if you could not use this product anymore?" - the classic PMF question

  • Item 3: "What is the main benefit you receive from this product?" - identifies value propositions

  • Item 4: "How can we improve Rows for you?" - collects improvement suggestions

  • Item 5: Additional feedback field

• Map: Toggle on to access individual field data in the next module

Rows - Add Row Module:

• Connection: Select "My Rows connection" (authenticate with your Rows account)

• Spreadsheet: Choose from your available spreadsheets (this is the destination of the data, e.g. "Demo - Make")

• Table: Select "Table1" as your destination table

• Table contains headers: Set to "No" if your table doesn't have header rows

• Range Values: Set to "A-AZ" to ensure all columns are available for data insertion

• Values: Map each Typeform field to specific columns:

  • A (A): Map to Typeform's email field (Item 1)

  • B (B): Map to PMF response (Item 2)

  • Continue mapping remaining fields to subsequent columns

This setup creates a real-time pipeline in which every form submission instantly appears in your Rows spreadsheet, with structured data ready for AI analysis.

Step 3: AI-Powered Classification and Analysis

Once data flows into Rows, it’s time to leverage the AI Analyst to transform and extract insights from our respondents:

• Response Classification: Type in AI Analyst the following prompt: "add a column to classify the main benefit received in Rows into: UI, integrations, analysis, and automation". This way, Rows automatically categorizes responses into these four key areas, helping you understand what features matter most to users. <ASSET>

• Create Visual Insights: Follow up with: "now create a column chart to show the number of people who gave disappointment feedback in column D". This generates an instant visualization of user sentiment, making patterns immediately visible.

  <ASSET>

• Email Domain Extraction: Use the prompt: "Add a column with the email domain" to extract the domains from email addresses, enabling segmentation between consumer (gmail.com) and business respondents (company domains).

  <ASSET>

Step 4: Data Enrichment

Once the basic transformations are complete, we can move on to the enrichment phase. For example, you might want to know whether specific feedback is more common in certain industries or company sizes.

You can use Rows’ GET_COMPANY function to draw insights about each respondent. Simply use =GET_COMPANY(domain) where domain is your extracted email domain.

This function returns:

• Company size (employee count)

• Industry classification

• Company description

• Location data

• Founded year and other business metrics

Bonus Step: Slack Notification

Finally, you may decide to follow up specifically with respondents from a particular industry or company size. To do so in a timely manner, set up a Slack notification using =MESSAGE_CHANNEL_SLACK(channel, message) to send notifications based on specific conditions.

You can configure alerts to trigger just by using common spreadsheet syntax, for example:

• Responses come from companies below a certain size threshold: use regular IF to trigger the Slack function, e.g., =IF(E2<500, MESSAGE_CHANNEL_SLACK(<channel_name>,<message>)

• Negative sentiment is detected in the feedback

From Data to Strategy

This workflow removes the usual trade-off between letting people respond freely and getting meaningful analysis. Respondents can share their thoughts naturally, while AI handles categorizing and enriching the data.

It saves hours of manual work and surfaces insights you might otherwise miss. For example, if a small software company mentions pricing concerns, the system flags it as a priority lead, adds relevant company info, and notifies sales. All in minutes.