Looking for Shopify merchants for a free 3-month demand forecasting case study

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Topic summary

A developer is recruiting Shopify store owners for a free 3-month case study of a demand forecasting tool launching in early 2026.

Tool Features:

  • Provides SKU-level demand forecasts directly within the Shopify dashboard
  • Identifies normal inventory patterns and flags stockout/overstock risks
  • Designed for business decision-makers rather than data analysts
  • No spreadsheets or analyst hiring required

Case Study Details:

  • Participants receive three months of free forecasting service
  • In exchange: provide feedback on tool usage and results
  • Preview available at TSFDemand.com
  • Interested merchants can respond in thread or contact through the website

The post emphasizes this is a feedback request rather than a sales pitch, seeking input from active Shopify operators before public launch.

Summarized with AI on October 24. AI used: claude-sonnet-4-5-20250929.
1

Hi everyone —

I’m developing a new tool designed specifically for Shopify store owners who want accurate SKU-level demand forecasts without hiring analysts or building spreadsheets.

The idea is simple: forecasting for decision-makers, not data scientists.
It shows what “normal” looks like for each SKU, when you’re at risk of stockouts or overstock, and what confidence range you can plan within — all inside your Shopify dashboard.

Before the public launch (scheduled for early 2026), I’m inviting a small group of merchants to join a case study program.

Participants receive three months of forecasts completely free, in exchange for feedback on how they use the results.

This isn’t a sales post — just a genuine request for input from real Shopify operators.

You can preview the concept and visuals at TSFDemand.com.

If you’re curious or want to be part of the case study, please reply here or reach out through the site — I’d love to hear your perspective.

Thanks!
— Kevin

(Founder, Targeted Seasonal Forecasts — “Forecasts built for decision makers, not data scientists.”)