For multichannel ecommerce retailers, guessing what will sell is a risky—and expensive—game. Predicting bestsellers based on data, however, transforms your inventory, marketing, and fulfillment strategies into a science. Whether you’re selling on Amazon, Walmart, eBay, or your own Shopify store, knowing which SKUs are likely to take off gives you a powerful competitive edge.

In this article, we’ll break down how to use your store and marketplace sales data to forecast future bestsellers, reduce dead stock, and double down on proven performers.

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Why Predicting Bestsellers Matters

✅ Improves inventory purchasing
✅ Reduces warehouse clutter and stockouts
✅ Focuses ad spend on proven products
✅ Speeds up decision-making in merchandising
✅ Helps automate restock and repricing strategies

Especially when managing hundreds or thousands of SKUs across channels, this insight lets you operate lean and smart.

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Key Data Sources for Bestseller Prediction

1. Your Ecommerce Store (Shopify, WooCommerce, BigCommerce)

• Pageviews and conversion rates per SKU
• Add-to-cart and checkout abandon rates
• Bounce rate per product page
• Return frequency
• UTM campaign attribution

2. Marketplace Sales Data (Amazon, Walmart, eBay)

• Buy Box win rate
• Fulfilled vs. merchant-shipped performance
• Customer review velocity and sentiment
• Sales rank movement (especially on Amazon)
• Pricing elasticity across competitors

3. Cross-Channel Analytics

• SKU performance by channel
• Repeat purchase rates by listing
• Geographic trends and shipping velocity
• Time to sell (days on hand per SKU)

When integrated through a platform like EcomBiz.AI, this multichannel data forms the foundation for predictive insights.

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Key Metrics to Watch

Metric	Why It Matters
Conversion Rate	High rate = clear demand for that product
Units Sold Per Day	Reveals sales velocity and potential reorder frequency
Stock Turnover Rate	Faster turns = stronger product performance
Add-to-Cart Rate	Indicates buying intent—even without full purchase
Review Growth	Suggests traction and word-of-mouth momentum
Refund/Return Rate	High rate may invalidate otherwise good performance

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How AI Helps Forecast Future Bestsellers

Platforms like EcomBiz.AI use AI and machine learning to analyze thousands of data points across your store and marketplaces, identifying patterns like:

• Seasonal trends (e.g. spike in Q4 or spring)
• Demographic preferences
• Bundling potential (frequently bought together)
• SKU cannibalization (similar listings competing)
• Pricing sensitivity thresholds

This allows the platform to recommend reorder quantities, adjust pricing proactively, and prioritize high-potential SKUs before they peak.

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Real-World Use Case

Example:

A multichannel retailer selling accessories notices that a necklace on Amazon has a:

• High add-to-cart rate
• 4.7-star average over 100+ reviews
• Increasing sales velocity week-over-week
• Identical product underperforming on Shopify

By identifying this trend, they:

• Increase ad spend and push through email on Shopify
• Mirror the listing copy and keywords from Amazon
• Auto-reorder inventory via supplier integration
• Add a variant to test (new metal finish)

Within 30 days, that one SKU becomes the top earner across both channels.

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Pro Tips for Building Your Bestseller Prediction System

• Connect all channels to a centralized dashboard
• Set up alerts for SKU velocity or sudden drops
• Tag your SKUs by category, price tier, margin, seasonality
• A/B test titles, images, and pricing across marketplaces
• Include soft data like customer questions and reviews

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Final Thoughts

Predicting bestsellers doesn’t require a crystal ball—just the right data and tools. With real-time performance tracking and AI-driven forecasting, you can scale what’s working, pivot away from what’s not, and make confident inventory and marketing decisions.

🔗 Want to forecast your next winning products?
Join the waitlist at EcomBiz.AI to automate product performance insights and get ahead of the curve.