Overview
An onsite search audit helps ecommerce teams see whether shoppers can actually find products once they use the store search box. Juno tests shopper queries for no-results, weak relevance, buried products, synonym gaps, filter issues, and inventory noise.
The output is an audit table and a short discovery brief. Each row explains the query, the observed result, the likely fix type, and how important the issue is.
Why you should treat site search like a sales assistant
Store search is often where high-intent shoppers reveal exactly what they want. When the result page fails, the store has ignored a very clear buying signal.
Baymard's ecommerce search research shows how search quality, query handling, and filtering shape product discovery. Juno turns that idea into a practical test set for your own store.
This is different from SEO keyword research. The point is not ranking in Google. The point is helping a shopper already on the site find the right product faster.
That distinction changes the recommendations. Some fixes belong in synonyms or search rules, others in product data, filters, collection structure, or no-result handling. Juno keeps those paths separate so the issue lands with the right owner.
Step-by-step
- 1Confirm the store, categories, search surface, known issues, and available search query data.
- 2Build a shopper query set covering exact products, categories, problems, attributes, brands, sizes, materials, and synonyms.
- 3Test whether each query returns relevant products, useful filters, in-stock options, and helpful no-result recovery.
- 4Classify issues as synonym gaps, product-data gaps, merchandising rules, filter problems, inventory noise, or no-result handling.
- 5Prioritize fixes by shopper intent, likely demand, revenue relevance, confidence, and effort.
- 6Produce the search audit table and a discovery brief.
Frequently asked questions
What if we do not have search analytics?
Juno can create a first-pass query set from navigation, collections, product names, reviews, support themes, and likely shopper language.
Is this a technical search configuration audit?
It is a business-facing audit. It identifies what should change, then points search, merchandising, or product-data owners toward the right fix.
How often should we run it?
Monthly is useful for active catalogs, especially after product launches, seasonal changes, or search configuration updates.
