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Patrick Stox SEO Forecast review

Free#12 of 39 in SEO Forecasting Software

Deep, free SEO forecasting for analysts comfortable working from CSV data.

7.8/10Editor score
Patrick Stox SEO Forecast7.8 Visit Patrick Stox

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Patrick Stox SEO Forecast is a free, no-signup browser tool for analysts projecting organic-search performance from historical data. It accepts CSV files from Search Console, GA4, Ahrefs, Semrush, Adobe, Google Trends, and generic date-value sources. The product covers traffic, visibility, keyword rankings, revenue impact, and scenario planning, making it suitable for SEO teams and business stakeholders who need forecasts without a subscription.

Forecasting depth is the main differentiator. Seasonal-naive, drift, ETS, Theta, and updates-aware models can be evaluated through rolling-origin backtesting, with automatic selection by MASE. Google algorithm updates are represented as measurable permanent level shifts rather than treated as unexplained noise. Analysts can compare multiple sites and share of voice, model ranking changes using search volume and click-through rate by position, and apply 80% or 95% prediction bands. Stress testing and anomaly markers add context when reviewing projected outcomes.

The workflow is file-centered: users upload CSV data, configure a weekly, monthly, or uploaded cadence, and export CSV, PNG, or scenario files. Processing runs locally in the browser unless a user explicitly creates a share link, which suits teams that prefer browser-based analysis and local handling. The trade-off is that teams must maintain clean source exports and work within a web-only interface; there are no paid tiers or broader plan options for organizational procurement. Choose it for detailed, self-directed SEO scenarios and business cases. Consider another tool if you need native mobile access, managed data connections, or tiered team administration.

Patrick Stox SEO Forecast pros and cons

  • Where it wins
    • Backtests five forecasting models and selects by MASE
    • Models algorithm-update level shifts and competitor share of voice
    • Connects ranking scenarios to conversions, revenue, ROI, and CPC value
  • Where it doesn't
    • Requires users to prepare and upload CSV files
    • Web browser access only, with no native mobile platform
    • No paid tiers or team-oriented plan options

Patrick Stox SEO Forecast fact sheet, pricing and score →

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