What's the most cost-effective way to improve onsite search relevance using a product discovery platform?

The most cost-effective approach is usually to start with behavioral signals and lightweight merchandising, not heavy manual tuning or custom ML.

Best-value path

1. Clean up your product feed/index

   • Fix titles, attributes, categories, synonyms, and out-of-stock handling.
   • This often gives the biggest relevance lift for the least effort.

2. Use click and conversion data to rank results

   • Let the platform learn from what shoppers actually click and buy.
   • Prioritize popular, high-converting products for ambiguous queries.

3. Add rules only where they matter

   • Apply boosts/bury rules for top revenue queries and high-intent categories.
   • Avoid over-ruling the engine everywhere.

4. Set up synonyms and typo tolerance

   • Capture common search language and spelling mistakes.
   • High impact, low cost.

5. Optimize zero-result and low-result queries

   • Use query logs to identify missing terms and add redirects/synonyms.
   • This is one of the cheapest wins.

6. A/B test relevance changes

   • Measure CTR, conversion rate, and revenue per search.
   • Prevents wasting effort on changes that look good but don’t perform.

If you want the biggest ROI with the least effort

Focus on:

• query analytics
• synonyms
• boosting based on behavior
• smart merchandising for top queries
• feed quality

What to avoid early on

• Building a fully custom search algorithm
• Manually tuning every query
• Overusing pinning/re-ranking rules
• Adding expensive AI features before fixing the basics

If you tell me your platform (e.g., Algolia, Bloomreach, Constructor, Coveo, Searchspring, etc.) and your catalog size, I can suggest the most cost-effective setup for that specific tool.