Combines advertising, website and visitor-behaviour data to explain what is working, what is wasting money and what should be tested next.
Simulated campaign data is used to keep the diagnosis public-safe. A real engagement would replace it with platform data.
Simulated campaign data is used to keep the diagnosis public-safe. A real engagement would replace it with platform data.
Ad dashboards report what changed, but marketers still spend hours connecting platform performance, search intent and landing-page behaviour to explain why it changed.
Campaigns, ads, keywords, spend, leads and sales.
Visitors, landing pages, funnel steps and conversions.
Clarity recordings, heatmaps or written observations.
Target cost, margins, location, offer and sales cycle.
Automation does the repeatable work. Important decisions and anything customer-facing remain reviewable.
API, CSV and export adapters keep live credentials optional.
A common campaign schema aligns metrics across sources.
Rules and agents test spend, intent, creative and CRO hypotheses.
Every recommendation links back to metrics or observed friction.
Dashboard, report and experiment backlog remain human-controlled.
The system supports live connections when available and upload or dry-run modes when client access is restricted.
Live Google campaign and search-term ingestion; CSV mode is available.
Live campaign and creative performance; sample mode is available.
Funnel, landing-page and conversion events.
Organic query and landing-page context.
Behaviour evidence and friction signals.
Structured diagnosis, explanation and recommendation drafting.
Paid-media analysis, conversion reviews and marketing reporting.
Campaign analysis, landing-page improvement and test planning.
Lead-cost diagnosis, wasted-spend analysis and landing-page CRO.
Channel and campaign-performance analysis.
Built and consolidated by Vipul Sharma from several versions used for campaign analysis and conversion improvement.