How we forecast seasonal SKU demand using time series + Shopify data (less guessing, more signal)

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Hi Shopify founders :waving_hand:

I’ve been working on demand forecasting for Shopify stores and wanted to share a practical approach that helped us reduce guesswork around seasonal products through DemandMind-Sales Forecasts.

The challenge:
Seasonal SKUs (holidays, collections, drops) behave very differently from evergreen products. A simple 30–60 day average often underestimates peaks and overestimates off-season demand.

What we’re doing instead:

  • Separate SKUs into evergreen vs seasonal

  • Use time-series forecasting on historical order data

  • Detect seasonality patterns (yearly cycles, promo spikes, trend shifts)

  • Flag SKUs that don’t have enough history so forecasts aren’t misleading

  • Combine live Shopify data + optional multi-channel CSV imports (POS, Amazon, Etsy)

The goal is to give directional buying signals with high accuracy so you don’t overbuy or stock out.

If you’re managing seasonal inventory, I’d love to hear:
How do you currently plan demand for seasonal products? Spreadsheets, intuition, tools?

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