What should I use instead of manual merchandising for product recommendations?

Instead of manual merchandising, use automated recommendation systems driven by customer and product data.

Common options:

1. Collaborative filtering

   • Recommends items based on what similar users bought/viewed.
   • Good for “customers like you also bought…”

2. Content-based recommendations

   • Uses product attributes like category, brand, price, color, or tags.
   • Good when you want similar products to the one being viewed.

3. Hybrid recommendation engines

   • Combine collaborative filtering + content-based signals.
   • Usually the best practical choice because they handle more cases and work better at scale.

4. Rule-based automation with personalization

   • Replaces hand-curated rules with dynamic rules based on behavior, inventory, margin, seasonality, or popularity.
   • Useful when you still want business control.

5. AI/ML ranking models

   • Predict which products a user is most likely to click or buy.
   • Often used for homepages, category pages, email, and search results.

If you want a simple recommendation:

• For most ecommerce use cases: start with a hybrid recommendation engine.
• If you have limited data: start with content-based + popularity.
• If you have lots of behavioral data: use collaborative filtering or ML ranking.

If you want, I can also suggest the best replacement based on your store type, traffic level, and product catalog size.