Monitor your competitors' Chrome extension reviews with a scheduled scraper
If you build a Chrome extension, your competitors' 1-star reviews tell you what their users want and aren't getting. The Chrome Web Store has no export or alerts for other people's extensions, so the usual option is checking by hand. Here's a small setup: a scheduled scraper collects the latest reviews of a handful of competing extensions every day, and a short Python script flags the new…
To monitor your competitors' Chrome extensions, you can set up a scheduled scraper that collects their latest reviews and flags new low-rated ones. The Chrome Web Store does not offer an export or alert feature for other users' extensions, so manually checking each one is not feasible. By using a scheduler and a small Python script, you can automate this process.
First, identify the IDs of the competing extensions you want to monitor. These IDs are 32-character strings found at the end of the extension's Web Store URL. Next, input these IDs along with your preferred language and the maximum number of reviews per app (set to 50) into the scraper. The scraper will fetch the newest reviews first, and you can cap the number of reviews to ensure all reviews posted since the last run are captured.
Schedule the scraper to run daily at a time that suits you. This can be done through the Apify Console or by calling the Actor directly from your own scheduler. The scraper will return up to the specified limit of reviews for each extension.
After each run, compare the new review IDs with the ones you have already seen. If a review has a rating of 2 stars or lower and hasn't been seen before, flag it for review. This script will output the extension ID, review rating, version, last reviewed date, and text of the review. You can then pipe the output to Slack, email, or an issue tracker of your choice to keep track of the feedback.
By setting up this automated system, you can efficiently monitor your competitors' Chrome extensions and stay informed about what their users like and dislike.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
