conversion-ops

To automate a CRO audit for landing pages, you can fetch live URLs and score them across 8 dimensions (0–100). The tool generates per-dimension scores, before/after recommendations, and benchmark comparisons for each page.

To turn survey data into lead magnets, ingest survey exports to cluster responses by pain points. The system segments the data and auto-generates tailored lead magnet briefs for each identified segment.

Yes, you can run batch CRO analysis across multiple URLs. This benchmarks landing page performance, identifies top improvement opportunities, and outputs JSON/text reports for your pages.

No, this CRO tool runs offline with local data after fetching the live URLs. It requires Python dependencies like beautifulsoup4, scikit-learn, and pandas to execute the local CLI workflow.

The best way to segment survey responses for CRO is using clustering algorithms on pain points. This approach groups similar responses and automatically generates targeted lead magnet briefs for each segment.

Scikit-learn and pandas are used for landing page analysis to process data and apply machine learning clustering. This enables survey segmentation, benchmarking, and automated scoring across 8 CRO dimensions.