The Agentic CRO System
Continuously tests and optimizes your top-of-funnel without a growth team
How It Works
The Agentic CRO System monitors your traffic sources, landing pages, and conversion paths continuously. When it detects underperformance or opportunity, it generates test hypotheses with supporting data. A human reviews and approves which tests to run. Approved tests are implemented automatically via your CMS or testing tools. Results are analyzed by the AI, and winning variants are promoted while learnings are added to a growing optimization playbook.
Step-by-Step Flow
Connect your analytics, heatmap, and CMS/testing tools
Define your key conversion goals and traffic thresholds for testing
AI analyzes traffic and behavior data, generates test hypotheses
Human reviews proposed tests and approves which to run
Approved tests implemented automatically. Traffic split begins.
Results analyzed, winner promoted, learnings added to playbook
Best For
- Marketing teams driving 2,000+ visitors/month to conversion-focused pages
- Companies where conversion rate improvements have direct revenue impact
- Teams that know they should be A/B testing but never have time to do it
This is customized for your business.
Every node, tool, and logic path shown here gets adapted to your team structure, your CRM, and your existing workflows. What you see is the proven pattern. What we build together is built specifically for you.
Implementation Notes
Analytics integration supports Google Analytics 4, Mixpanel, and Amplitude via API. Heatmap and scroll data comes from Hotjar or Microsoft Clarity. CMS integrations for test implementation include Webflow via API, WordPress via plugin, and Next.js via feature flag injection through Vercel or LaunchDarkly. The hypothesis engine analyzes bounce rate by page and traffic source, scroll depth and click map patterns, form drop-off rates by field, and conversion rate variance by traffic segment. Hypotheses are structured objects with fields for the observed problem, the proposed change, the predicted lift range, the confidence level, and the minimum traffic threshold needed for statistical significance. A human reviewer sees each hypothesis in a Slack message or Notion board and can approve, reject, or request additional data. Approved tests implement as A/B splits with configurable traffic allocation (default 50/50). Results are analyzed when statistical significance is reached at p less than 0.05 at 95 percent confidence, or after 21 days, whichever comes first. Winning variants are promoted automatically. Results and reasoning are logged to a living optimization playbook. Prerequisites: 2,000 or more monthly visitors to the pages being tested, and at least one analytics tool with event tracking configured.