This guide introduces a collection of articles that describe how Bloomreach Search works, how you can optimize your search quality, and how to troubleshoot search issues.
Bloomreach Search and Merchandising is a state-of-the-art search engine using the latest Machine Learning technologies to deliver a highly engaging and optimized search experience. The engine automatically adjusts search results to varying expressions of user intent over time and engagement behavior on the site.
Maximize Search Quality
At a high level, here's what you can do with Bloomreach Search and Merchandising to maximize the quality of your search results:
- Maintain a working API and pixel integration.
- Provide a timely product feed.
- Ensure the parity of the product grid in search and browse experiences.
- Use the Ranking Diagnostics tool to simulate.
- Use the Merchandising tools to curate search results and left navigation refinements.
Search Quality Overview
The search engine must first find and return the set of products in the catalog that match the intent expressed by a visitor in a search query. However, the search query often differs from how the product is described in the catalog. For example, a furniture catalog may contain ottomans but users may search for “footstools”. Or an apparel catalog contains “t-shirts”, but users may search for “tee”.
To handle such scenarios, the query is enhanced by referring to a list of synonyms. While we do recommend adding your own synonyms, the Bloomreach engine also maintains a robust synonym dictionary accumulated over the years based on queries entered across our customer base.
The query is then compared to the fields in your product catalog-- such as title, category, and other fields that you can designate to be "search-able" -- to find the relevant matches. In case there is no matching product found, the engine automatically correct any misspelled words and/or relax the query to one that returns a broader result set.
Once a set of products has been returned for a search query, Bloomreach adjusts the order of those products to optimize for higher expected revenue per visit (RPV) for the query. Several factors contribute to the algorithm that optimizes product ranking. A high-level explanation of the various signals is included below.
Once the relevant set of products is determined (referred to as the recall set), Bloomreach will adjust the order of those products to maximize site Revenue.
There are several factors involved in determining a product's rank in the recall set. A few of the most important signals are:
Product performance for a specific query or category,
Personalization - driven by a user's preferences determined through their site behavior, and
Relevance - based on Bloomreach's semantic understanding of the query and product.
To know more about Bloomreach's Ranking, go to the Search Ranking page.
This collection of articles provides an overview of how the Bloomreach Search and Merchandising engine works. For an in-depth understanding of search quality and optimization, you can read each of the articles in order. Alternately, you can review the contents of this collection and select the specific topics that you're interested in.
Explains what happens when Bloomreach receives a search query, and how Bloomreach runs that query.
Breaks down the primary functions of Bloomreach Search, describing how it works overall.
Provides guidelines for using merchandising tools to curate search results, especially boost, bury, and synonyms.
Describes how Bloomreach shows personalized results on a 1:1 basis.
Describes how Bloomreach search results show the most relevant SKU image for a product with Color-based image SKU selection feature.
Describes how Bloomreach displays the most relevant search results for a broad search query using SmartSorting.
Discusses common scenarios that you might like to use when optimizing or troubleshooting search experiences on your site.
For information about the API, pixel, and product feed, take a look at the integration part of our site: Bloomreach Integration.
Updated 6 months ago