How to scrape automotive data from BikeDekho using the Minexa.ai extension
- Minexa.ai
- 13 hours ago
- 3 min read
Collecting bike specification and pricing data from BikeDekho manually means opening dozens of pages, copying values one by one, and still ending up with a spreadsheet that goes stale the moment prices update. This walkthrough shows how to extract that data automatically using the Minexa.ai Chrome extension, no code required.
Watch the full video tutorial first, then follow the steps below.
Step 1: Open Minexa.ai and navigate to BikeDekho
Install the Minexa.ai Chrome extension, then navigate to the Honda bikes listing page at bikedekho.com/honda-bikes. Once the page has fully loaded, open the extension.
The extension popup appears with a confirmation prompt. Click the 'I'm on the right page' button to tell Minexa.ai you are ready to begin detection on this page.
Step 2: Confirm pagination detection
Minexa.ai scans the page and identifies how additional results are loaded. For the BikeDekho Honda listing, it detects the pagination mechanism automatically. You will see a summary of what was found along with a Continue button.
This means the scraper will follow pages automatically across the full catalogue without any manual intervention on your part.
Step 3: Choose list-only or list-and-detail scraping
After confirming pagination, Minexa.ai asks whether you want to extract data from the listing page only, or also follow each bike's link and pull data from its individual detail page. For most pricing and spec use cases, the list page contains everything needed.
Step 4: Select simple or advanced mode, then identify the data container
Next, choose between simple scraping and advanced mode. Simple mode covers the standard extraction flow. Advanced mode is available if you need custom interactions. After selecting, Minexa.ai highlights the repeating data container on the page automatically.
You do not need to click individual fields or write selectors. Minexa.ai identifies the repeating pattern and maps all data points within it.
Step 5: Review extracted data points
Once you confirm the container, Minexa.ai creates the scraper and displays all extracted columns. You can navigate through them using the prev and next controls to verify what was captured before running the full job.
Here is a sample of what the extracted data looks like for three Honda models:
[{"bike_id":"2294","bike_model":"Honda SP 125","engine_displacement":"123.94 cc","engine_specification":"10.87 PS","mileage_engine":"63 kmpl","price_range":"89,279","reviews_count":"| 260 Reviews","website_link":"https://www.bikedekho.com/honda/sp125"},{"bike_id":"1294","bike_model":"Honda Shine","engine_displacement":"123.94 cc","engine_specification":"10.78 PS","mileage_engine":"55 kmpl","price_range":"82,253","reviews_count":"| 469 Reviews","website_link":"https://www.bikedekho.com/honda/shine"},{"bike_id":"1525","bike_model":"Honda CB350RS","engine_displacement":"348.36 cc","engine_specification":"21.07 PS","mileage_engine":"35 kmpl","price_range":"1,97,939","reviews_count":"| 82 Reviews","website_link":"https://www.bikedekho.com/honda/cb350rs"}]The website_link field returns a full absolute URL per bike, so you can deep-link directly to any model page without constructing URLs manually. The bike_id field provides a stable numeric identifier per row, which makes deduplication straightforward when you run the scraper again later. The detailed_specs field returns a structured array encoding mileage, engine displacement, and power output together, giving you complete spec data without needing to visit individual pages.
Step 6: Run the job and export your data
After reviewing the columns, the scraper is saved and appears in your jobs list. Click Run to start extraction. Results populate in a table as pages are processed.
Once the job finishes, export to Excel or JSON directly from the results view. The job summary screen also shows options to connect Google Sheets or set up a recurring schedule so the data refreshes automatically.
The scraper you just built is reusable. Run it again on the same page type and extraction starts almost instantly because the structure has already been learned. This is useful for tracking price changes across the Honda catalogue over time without any additional setup.
For more on extracting automotive and product listing data without code, see this related post: Scraping book listings for publishers: a structured guide to extracting catalogue data without code.
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