Beauty and Wellbeing
Forecasting Accuracy and Impact of Pricing and Ad Spends for Popular Skincare Brand
Teresa Forecasting Engine combining e-commerce scrape (Amazon), ad-spend, competitor prices, and category rankings to stabilize forecasts and cut OOS.
25% improvement in e-commerce forecast accuracy
Improvement
The Problem
Volatile e-commerce sales driven by fluctuating ad spends, competitive pricing, and category rankings caused inaccurate forecasts and frequent out-of-stocks; limited external data further reduced visibility.
Key Challenges
- Highly volatile and unpredictable e-commerce sales.
- Sales heavily dependent on ad spend and competitor pricing.
- Category rankings strongly impacting visibility and sales.
- Limited integration of external data for effective forecasting.
Our Solution
Scraped real-time marketplace data, integrated ad-spend and rankings, and built a forecasting model that fuses these external sources.
- Intellimark Teresa Forecasting Engine
- E-commerce Scraping (Amazon)
- Price/Rank/Media Fusion
Implementation Approach
- Data Collection: scraped Amazon to monitor brand and competitor prices.
- Media Integration: incorporated ad-spend data to analyze impact on sales.
- Ranking Signals: factored category rankings for visibility effects.
- Modeling: robust forecasting model that ingests external data sources.
Results & Impact
- 25% improvement in e-commerce forecast accuracy (FA).
- 200 pricing and media insights generated.
- 50% reduction in out-of-stock issues.