How to Extract Services and Pricing From Business Websites (Without Manual Cleanup)

Why this is hard

Pricing pages are inconsistent. Some use tables, some use cards, some bury prices in PDFs or collapsible menus. If you don’t normalize fields, you’ll spend more time cleaning than extracting.

The exact fields that matter

For most teams, these fields cover 90% of use cases:

service_name
category
price or price_range
duration
package_or_addon
promo

Step‑by‑step workflow

Find the right pages

Crawl the site and target URLs like /services, /pricing, /menu, /treatments.

Choose a consistent schema

Decide your field names up front (this prevents messy outputs).

Extract with a service template

Use a template designed for service menus to avoid missing structured fields.

Normalize results

Ensure consistent units (60 min vs 1 hour) and currency format.

Export and QA

Export JSON/CSV and spot‑check 5–10% of results.

Example output

{
  "business": "Lakeside Spa",
  "services": [
    {
      "service_name": "Deep Tissue Massage",
      "category": "Massage",
      "duration": "60 min",
      "price": "$120"
    }
  ]
}

Where this creates real value

Competitor benchmarking: compare price ranges by city or category.
Sales enablement: equip teams with accurate service catalogs.
Market research: identify trending services and promotions.

Common mistakes to avoid

Scraping only the homepage: most pricing pages are deeper.
Ignoring add‑ons: they often impact true price comparisons.
No schema: unstructured data is hard to analyze.

Final takeaway

The goal isn’t just “get the data.” It’s get consistent data you can analyze the same day. That’s why schema + targeted pages matter.