Home > Optimizing Adidas Reselling with Pandabuy Spreadsheet: Demand Prediction & Seasonal Trends

Optimizing Adidas Reselling with Pandabuy Spreadsheet: Demand Prediction & Seasonal Trends

2025-07-20
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Here's the HTML content with the requested article about Pandabuy spreadsheet and Adidas demand prediction:

Harnessing Search Trend Data for Smarter Purchasing

The Pandabuy spreadsheet tool

Key advantages include:

  • Real-time popularity ranking of 250+ Adidas SKUs
  • Automated keyword clustering (e.g. "Solarboost" vs "Solar Glide")
  • Historical trend analysis back to 2018 season launches

Seasonal Demand Forecasting Models

Winter 2023 data shows predictable surges for specific categories:

Product Category Avg. Winter Increase Peak Sales Weeks
Running Shoes (Boost variants) 18-22% Jan W2-W4
Training Apparel 33-40% Dec W1 - Feb W3
Retro Sneakers 12-15% Nov W4 - Dec W2

The Pandabuy algorithmic adjustment feature automatically scales (orders ±25%) based on these historical patterns three months before seasonal shifts.

Geographic Preference Mapping

Combining search trends with shipping data reveals stark regional variations:

Terror Run styles

Three-stripe track pants (11.3% of Germany's winter requests vs. 2.7% in UK markets)

Our dynamic ad templates auto-localize by:

  1. Importing regional performance data from Pandabuy sheets
  2. Applying location-specific price adjustments (±5-8%)Generating targeted creatives with local currency/influencer imagery

Implementation Guide

Download our pre-configured Adidas template sheets 

1. Connect your Pandabuy API key
2. Set seasonal adjustment parameters (recommended: ±22%)
3. Define priority regional markets 
4. Schedule weekly data pulls (Sundays 00:00 UTC advised)

Data updated quarterly. Next demand prediction model refresh: December 15, 2024. See our 2025 Seasonal Forecast White Paper →

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