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Minexa.ai

How to scrape restaurant data from Travel Wisconsin using the Minexa.ai extension

Travel Wisconsin's food and drink directory lists hundreds of restaurants, bars, breweries, cafes, and distilleries across the state. If you want that data in a spreadsheet rather than clicking through pages one by one, the Minexa.ai Chrome extension handles the full extraction without any code.

Here is the complete walkthrough, step by step.

Watch the full tutorial first

The video below covers the entire workflow from installing the extension to downloading your export. Watch it before going through the steps if you prefer a visual overview.

Step 1: Install Minexa.ai and open the extension

If you have not installed Minexa.ai yet, add it from the Chrome Web Store. Once installed, you will see the extension icon in your browser toolbar. Click it to open the dashboard where your scrapers and jobs are managed.

This is your starting point. From here, you can create new scrapers, run existing ones, and access your exported data.

Step 2: Navigate to the Travel Wisconsin food and drink page

Go to travelwisconsin.com/food-drink in your browser. This is the main directory listing page. You will see cards for restaurants, bars, breweries, and other venues across Wisconsin, each with a name, city, category, and image.

Keep this tab open. Minexa.ai reads the page that is currently active in your browser, so you do not need to copy any URL manually.

Step 3: Confirm you are on the right page

Click the Minexa.ai extension icon while on the Travel Wisconsin page. The popup will appear and ask you to confirm that this is the page you want to scrape. Click the confirmation button to proceed.

Minexa.ai will then begin detecting the structure of the page automatically, including the listing cards and the pagination method used by Travel Wisconsin.

Step 4: Review pagination detection

Minexa.ai detects how the directory pages are connected and shows you the pagination it found. Travel Wisconsin uses multiple pages of listings, and the extension captures all of them automatically.

Review the pagination list and click Continue when you are ready to move to the next configuration step.

Step 5: Choose list-only or list-plus-detail scraping

At this point you decide how deep to go. The list page gives you the restaurant name, city, image, and URL per listing. If you also want the full venue description and any additional details from each individual restaurant page, you can instruct Minexa.ai to follow each link and extract that layer too.

For most use cases, the list-level data is enough to build a structured directory. Select your preferred option and continue.

Step 6: Select scraping mode

Minexa.ai offers a simple mode and an advanced mode. Simple mode is recommended for most users and covers all the fields visible on the listing cards. Advanced mode gives you more control over which fields are included.

Select simple mode and proceed. Minexa.ai will handle field detection automatically from here.

Step 7: Highlight the data container

Minexa.ai will ask you to identify the repeating container on the page, which is the element that wraps each restaurant card. Click on one card to highlight the container. Minexa.ai uses this to understand the repeating structure and extract the same fields from every card on every page.

Once confirmed, Minexa.ai identifies all the data points within that container automatically.

Step 8: Review extracted data points

Minexa.ai displays all the fields it found across the listing cards. For Travel Wisconsin, this includes the restaurant name, city, image source URL, image alt text, a description field, and the relative URL for each venue page.

Here is a sample of what the extracted data looks like across a few listings:

[
  {
    "restaurant_name": "1951 West",
    "city": "Green Bay",
    "image_alt_text": "1951 West",
    "image_source_url": "/getContentAsset/43b05b6c.../1951_DirectoryCard.webp",
    "restaurant_url": "/food-drink/restaurants/1951-west"
  },
  {
    "restaurant_name": "3 Sheeps Brewing",
    "city": "Sheboygan",
    "image_alt_text": "A sampling sampler tray from 3 Sheeps",
    "image_source_url": "/getContentAsset/3719ad3b.../3-Sheeps-flight_DirectoryCard.webp",
    "restaurant_url": "/food-drink/breweries/3-sheeps-brewing"
  },
  {
    "restaurant_name": "29 South Cafe",
    "city": "Janesville",
    "image_alt_text": "A plated piece of quiche with fruit at 29 South",
    "image_source_url": "/getContentAsset/3f51037a.../29South@FSP_DirectoryCard.webp",
    "restaurant_url": "/food-drink/cafes-coffee-shops/29-south-cafe"
  }
]

Notice that the restaurant_url field encodes the venue category directly in the path, such as /food-drink/breweries/ or /food-drink/cafes-coffee-shops/. This means you can filter and segment your dataset by venue type without any extra processing, just by parsing the URL slug. The description_long field returns a structured array when image metadata is present, carrying the alt-text value as a typed object rather than a plain string.

Step 9: Complete configuration and run the job

Once you have reviewed the fields, complete the scraper configuration. Minexa.ai saves this scraper so you can reuse it at any time without repeating setup. Then click Run to start the extraction across all pages.

The job will process each page of the Travel Wisconsin directory in sequence, collecting every listing automatically.

Step 10: Export your data

When the job finishes, your data is ready to export. Choose Excel, JSON, or Google Sheets depending on how you plan to use it.

The exported file contains one row per restaurant listing and one column per field, structured and ready to filter, sort, or load into any tool you use.

If you want to collect this data again in the future, the scraper is already saved. Just open Minexa.ai, find the saved job, and click Run again.

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