Predictive SEO Trends

The Challenge: Fast fashion e-com: volume data lags peaks by 2-4 weeks. Competitors snag early traffic; client needed pre-peak trend spotting for first-mover SEO. The Solution: Python Scikit-Learn/Prophet model. Data Aggregation: APIs pull TikTok/IG hashtags (via Scrapfly), rising Google Trends, internal GSC/site search. Time-Series Forecasting: Prophet detects velocity (MoM growth >50%), forecasts 4-week peaks with 85% accuracy; anomaly detection flags breakouts. Content Alerts: Slack bot pings "High Probability" KW with outlines for 24hr publish. Implementation Details: Daily Airflow DAGs; backtests vs historical trends. Integrates Ahrefs for competition velocity. ​ The Results: Published 2 weeks early: 40% Position 0 Snippets, 150% holiday traffic (like 30% e-com lifts). Proactive content boosted ranking predictability 35%.