Key Concepts of NoChannel Search
Understanding the core concepts of NoChannel Search is essential for maximizing the potential of its powerful search engine. Here's a comprehensive guide to key terms and fundamental concepts used within NoChannel Search.
A searchable collection of data structured for efficient retrieval. Imagine an organized library catalog where each product is a book and its details are searchable information.
An ecommerce platform uses an index containing product details like name, description, category, brand, and price.
A user's specific request for information from the index. It's like a question you ask the library catalog to find relevant books.
A customer searches for "women's running shoes" on the ecommerce platform. This will indicate a query.
A set of information assigned a unique address within an index. These documents can be blog posts, PDFs, or product detail pages within a catalog.
Dividing search results into manageable, numbered pages for easier browsing. It helps prevent information overload.
The search result for "women's running shoes" displays 10 products per page, allowing customers to navigate and consume the results easily.
A measure of how well a document matches a specific query. The more relevant a document is, the higher it ranks in search results.
When a customer searches for "affordable running shoes," the ecommerce platform prioritizes documents (product pages) featuring shoes with competitive prices.
The arrangement of search results is based on chosen criteria, like price, popularity, or brand.
Customers can choose to sort search results for "running shoes" by price (low to high) to find the most affordable options first.
The intermediary layer connects various components like indexes, queries, and access levels to fulfill search requests. Think of it as the behind-the-scenes traffic control system directing users to the relevant information.
A route on the ecommerce platform connects the "running shoes" index with relevant filters, sorting options, and personalized recommendations based on the customer's browsing history.
Customized sequences of steps that refine and process search queries. Imagine a recipe for perfect search results with different steps like filtering, boosting, and sorting.
A query pipeline in the ecommerce platform might filter search results for "running shoes" to only show items in stock, boost results with high customer ratings, and sort them by popularity.
Defined rules for query matching and generating specific responses. This involves the customization of query rules for different search scenarios.
- Filter
- Rules that refine search results based on specific criteria, allow users to narrow down their options. On an ecommerce platform, users might use filters to narrow their search for "running shoes" by selecting specific brands, price ranges, or features like waterproof materials.
- Phrase
- A sequence of words is treated as a single unit in a search query, ensuring accurate matches. Searching for "running shoes" as a phrase ensures results containing both "running" and "shoes" in close proximity, filtering out irrelevant documents with individual mentions of these terms.
- Results
- Information retrieved from the index is based on a user's query. When a user searches for "laptops," the search engine displays a list of product pages (documents) containing relevant information about laptops available on the platform.
- Boost & Bury
- Adjusting the positions of search results to emphasize specific items or hide irrelevant ones. Think of it as highlighting your bestsellers in a physical store. You can boost popular brands while burying out-of-stock products.
- Field Searcher
- Specifying which fields (e.g., product name, brand) should be searched within a document for precise results. Searching for "iPhone" only within the "Brand Name" and "Product Name" fields.
- Rewrite Rule
- Automatically correcting misspelled queries or applying default filters to improve search accuracy. Automatically correcting "womans shoes" to "women's shoes" and filtering for shoes in the appropriate size range.
- Default Aggregation
- Predefined aggregations are automatically applied to search results when specific ones aren't defined, ensuring relevant information is displayed. When searching for "shoes," the platform might automatically aggregate products by brand.
- Aggregation Appenders
- Tools to dynamically add additional filters or groupings (aggregations) to refine search results based on user interaction. Allowing consumers to filter "running shoes" by price range dynamically using an aggregation appender.)
Words with similar meanings are treated as equivalent for broader search coverage. Like treating "sneakers" and "trainers" as synonyms for "running shoes".
Automatic suggestions are displayed as users type their search queries, saving time and effort.
As you type "pur," suggestions like "purple," "purchase," or "pure" appear.
- Interest Phase: Engagement typically begins on the homepage.
- Consideration Phase: In-depth research often occurs on category or blog pages.
- Purchase Phase: Conversions happen on landing pages, product details (PDP), and shopping cart pages.
- Promoting Relevant Products: Tailoring product placement based on user context and intent.
- Optimizing Position, Person & Timing: Strategically placing products in the right position at the right time for the right user.
- Prime Real Estate: Top six positions considered valuable for showcasing products.
- Product Relations & Merchandising: Automated bundling, cross-sells, up-sells, and defining product relations to enhance engagement and revenue.