Automated Competitor Price And Feature Monitor With Make.com

Most marketing teams do competitive research the same way: one person owns a browser bookmarks folder of competitor pricing pages and manually checks them whenever they remember to. That means your team often finds out about a competitor’s pricing drop when a prospect mentions it on a call — not before. This workflow catches those changes the morning they happen, every week, automatically.

The architecture uses Apify for scraping because it handles JavaScript-rendered pages (modern SaaS pricing pages almost universally use React or Vue), rate limiting, and proxy rotation out of the box. GPT-4o then does the heavy lifting of semantic comparison — it doesn’t just diff the raw text, it understands that “Starting at $49/seat” and “From $49 per user” represent the same pricing even if the wording changed. The filter for NO_SIGNIFICANT_CHANGES keeps Slack noise low: your team only gets alerted when something real happened.

Over 6 hours saved per week is conservative. Most marketing teams spend 30–60 minutes per competitor per week on manual checks. If you monitor 6 competitors, this workflow pays for itself in under two days.

Prerequisites

1. A Make.com account (the Core plan at $9/month is sufficient — this scenario uses ~5 operations per run)
2. An OpenAI API key with GPT-4o access (pay-as-you-go; typical monthly cost under $3 for weekly runs against 10 competitors)
3. An Apify account (free tier includes 5 actor runs/month; paid starts at $49/month for unlimited runs)
4. A Google Sheet with columns: Competitor Name, Pricing URL, Last Scraped Text, Last Run Date
5. A Slack workspace with a #competitive-intel channel and an incoming webhook URL

Setup Guide

1. Set up your competitor tracking sheet in Google Sheets. Add one row per competitor with their name and pricing page URL. Leave Last Scraped Text blank initially — it will populate on first run.
2. Configure Apify — create a new task using the “Web Scraper” actor. Set the start URL dynamically (you’ll pass this from Make.com). Set the page function to return document.body.innerText for simplicity, or write a custom selector for the pricing table specifically.
3. Import the Make.com blueprint from the download link above. Connect your Google Sheets, Apify, OpenAI, and Slack credentials in the Connections panel.
4. Map Google Sheets columns in the “Read competitors” module — ensure Pricing URL feeds into the Apify module’s start URL field, and Last Scraped Text feeds into the GPT-4o prompt as [PREVIOUS_DATA].
5. Set the GPT-4o model to gpt-4o (not gpt-4o-mini) — the quality difference in detecting subtle pricing changes is meaningful.
6. Configure the Slack webhook — create an incoming webhook at api.slack.com/messaging/webhooks. Paste the URL into the Slack module in Make.com.
7. Add a filter between the GPT-4o module and the Slack module: condition = GPT-4o output does NOT contain NO_SIGNIFICANT_CHANGES.
8. Run manually once to populate the Last Scraped Text column. After that, activate the weekly schedule.

Who This Is NOT For

• Competitors with login-gated pricing — Apify cannot authenticate to fetch pricing behind a paywall or sign-up wall without additional session management work.
• Teams monitoring more than 20 competitors weekly — Apify costs will scale significantly; consider batching runs bi-weekly or filtering to top 10.
• Companies in industries with legal restrictions on web scraping — review the competitor’s robots.txt and Terms of Service. This workflow is intended for publicly available pricing pages only.
• Teams that need real-time monitoring (within hours of a change) — this is a weekly batch workflow. For real-time alerts, look at purpose-built tools like Visualping or Competitors App.