Conceptual Guide: Search and Merchandizing

1. Strategic Overview

Concept Definition

Search and Merchandizing is the unified Kibo Commerce capability for controlling product discovery, relevancy ranking, and the strategic presentation of products to shoppers.

Business Context

This capability serves as the core engine that transforms a static product catalog into a dynamic, responsive, and curated shopping experience. It governs how shoppers find, filter, and interact with products, directly influencing the effectiveness of the conversion funnel and the overall success of the customer journey.

Value Drivers

Enhanced Product Discoverability: A precisely configured search schema, relevant ranking model, and intuitive filtering capabilities fundamentally reduce the friction shoppers experience when looking for products. By enabling shoppers to find what they want quickly and accurately, the platform is designed to improve engagement and session duration.
Strategic Merchandizing Control: The platform provides business users with direct, manage product lifecycle visibility, and align search results with strategic goals. This includes the ability to boost high-margin products, feature new arrivals, or curate category-specific experiences without requiring developer intervention.
Improved Shopper Experience and Intent Matching: The system includes features designed to create a more forgiving and intelligent search experience. Capabilities such as search synonyms, spell correction, and redirects help capture user intent even with imperfect queries, preventing the negative experience of a zero-results page and guiding users toward relevant products and content.

Scope Statement

In Scope: This guide provides a comprehensive conceptual overview of the Search Schema, Search Configurations, and Merchandizing Rules. It details every functional component, explains the relevancy model, and illustrates capabilities through business-focused examples.
Explicitly Excluded: This guide does not cover API endpoints, JSON structures, administrative UI walkthroughs, pricing and promotion logic

2. Core Concepts Explained

What is Search and Merchandizing?

In Kibo Commerce, Search and Merchandizing is not a single feature but a system of interconnected components that work in concert to govern product visibility. The system operates on a logical flow: data from the Product Catalog is indexed according to a defined Search Schema. This indexed data is then governed by platform-wide Search Configurations, which establish the baseline relevancy and ranking model. Finally, for specific business scenarios, this baseline behavior can be tactically overridden by Merchandizing Rules. This layered approach provides a powerful combination of automated relevance and manual, strategic control.

Why does Search and Merchandizing matter?

The strategic management of search and merchandizing capabilities delivers significant operational, financial, and customer experience benefits.

Operational Benefits: The system is designed to empower merchandizing teams by abstracting complex search logic into accessible business controls. The ability to schedule rules for future campaigns, preview their impact before publishing, and apply logic based on existing product attributes allows for efficient, predictable, and scalable campaign execution.This reduces reliance on technical teams for day-to-day merchandizing activities.
Financial Benefits: The platform provides direct levers to influence profitability. Merchandisers can implement strategies to boost products with higher margins, de-emphasize low-margin or low-performing items, and increase the visibility of strategic SKUs or new arrivals to accelerate revenue from key product lines.
Customer Experience Benefits: A highly relevant and intuitive search experience builds shopper trust and confidence. When search results consistently align with expectations, it encourages deeper engagement with the product catalog, reduces bounce rates, and supports a seamless path to purchase. Features that intelligently handle typos or alternative product terms contribute to a positive brand perception.

When to deploy Search and Merchandizing?

While foundational search capabilities should be configured from the outset of any Kibo Commerce implementation, the deployment of advanced merchandizing strategies is often triggered by specific business events and increasing operational maturity. Key triggers include the launch of a new product line, expansion into a new market requiring localized search terminology, the execution of a major seasonal promotion, or the need to respond to competitor strategies with more aggressive product positioning. Advanced merchandizing becomes essential as the product catalog grows in size and complexity, when a business begins managing multiple sites or catalogs.

3. Functional Components & Configuration Deep Dive

Component Architecture: A Multi-Layered System of Control

The Kibo Commerce platform provides a hierarchical model for influencing search results. This architecture allows for both broad, systemic tuning at the foundational level and precise, tactical overrides at the control layer. This structure ensures that sitewide relevancy rules can coexist with specific, time-sensitive merchandizing campaigns.

Layer 1: The Foundation - Search Schema

The Search Schema is the blueprint that defines what product data is indexed and made available to the search engine. It is the foundational step that makes all subsequent configuration and merchandizing possible. An attribute cannot be used in a search query, facet, or merchandizing rule unless it is first defined in the schema. This creates a symbiotic relationship between the product catalog and the search engine: the richness of the search experience is directly dependent on the granularity of the data defined in the catalog. A comprehensive search and merchandizing strategy must therefore begin with a comprehensive catalog and attribute strategy.

Core Fields: These are the standard, out-of-the-box product fields that form the basis of the search index.
Custom Attributes: These are business-defined product attributes (e.g., color, brand, material, season) that are explicitly added to the schema to become searchable, filterable, and available as conditions in merchandizing rules. For an attribute to be available for faceting and sorting, the “Available as Filter & Sort” setting must be enabled on its definition in the catalog.
Analyzers: Also known as “field types,” analyzers are rules for processing text during indexing. They determine how search terms are matched, handling tasks like ignoring case, stemming words to their root (e.g., “running” becomes “run”), and processing synonyms. The choice of analyzer (e.g., lenient for broad matching, exact_match for precise matching) depends on the data type and the desired search behavior.

Layer 2: The Engine - Search Configurations

Search Configurations establish the site-wide, default relevancy and ranking model. These settings act as the global “rules of the road” that govern all searches before any specific merchandizing rules are applied. These configurations are managed per catalog and site combination, allowing for different search behaviors across different storefronts. The platform deliberately separates search into four distinct types, allowing for nuanced tuning of the user experience based on the shopper’s context and intent. This architectural choice recognizes that a direct search query has different requirements than a type-ahead suggestion or a category listing page.

Site Search: Governs the behavior of the main search bar when a user submits a query. It has the most comprehensive set of configuration options, including relevancy tuning, spell correction, and match criteria, as it handles direct, intentional user queries.
Category Suggestion: Manages the category names suggested to a user as they type in the search bar. Configurations are focused on field weights and the data fields returned to ensure speed and relevance.
Product Suggestion: Manages the specific products suggested to a user as they type. It supports relevancy weighting, product slicing, and boost/bury conditions to ensure the most appropriate products are surfaced in real-time.
Listing: Governs the default display and sorting of products on category listing pages. Its configurations focus on personalization and boost/bury capabilities to create curated browsing experiences.

Configuration-Level Deep Dive: Search Schema

The Search Schema is the blueprint that tells the search engine how to understand and index the product catalog. It is the key link between raw product data and searchability.

Field Type Analyzers Explained

An analyzer, or “field type,” is a set of rules that governs how the text within an indexed field is processed. The choice of analyzer is an important decision that determines the search engine’s behavior, impacting how forgiving and accurate the search feels to the end-user. For example, a lenient analyzer will treat “shoe” and “shoes” as the same word (a process called stemming), while an exact_match analyzer will not. This allows administrators to apply different text processing logic to different types of data; a product code field might require exact matching, while a product description field would benefit from more lenient, flexible matching. The table below translates the technical function of key analyzers into their primary business use cases. Table 1: Key Field Type Analyzers and Their Business Impact

Analyzer Name	Functional Description	Key Behaviors	Primary Business Use Case Example
exact_match	Matches only when the query term is an exact, case-insensitive match to the text in the field.	No stemming, no synonyms, considers term order.	Best for fields with a fixed set of values like brand or color, ensuring a search for “Red” doesn’t accidentally match “Reddish”.
lenient	A flexible analyzer that allows for more matches by using stemming and synonym expansion.	Applies stemming (e.g., run/running), expands synonyms, ignores term order.	A general-purpose analyzer ideal for freeform text fields like Product.FullDescription to find relevant products even if the query uses slightly different wording.
lenient_phrases	A supplemental analyzer used with lenient that prioritizes matches where search terms appear in the same order as in the query.	Considers term order, relies on lenient for stemming and synonyms.	Used to ensure a search for “down jacket” more strongly matches products named “Down Jacket” than products with “jacket” in the name and “down” in the description.
code_exact	A specialized analyzer for product codes that looks for case-insensitive exact matches.	Case-insensitive, requires exact character match.	Ideal for Product.Code or UPC fields to allow customers and B2B users to find a product by its exact part number.
code_lenient	A more forgiving code analyzer that matches even if punctuation like spaces or dashes are different.	Case-insensitive, ignores spaces and punctuation.	Allows a search for “part123” to successfully match a product code stored as “PART-123”, improving search forgiveness for technical SKUs.
…_type_ahead	A suffix that can be added to other analyzers (e.g., lenient_type_ahead) to enable partial matching for search-as-you-type suggestions.	Matches partial words from the beginning (e.g., “sho” matches “shoe”).	Powers the type-ahead search box, providing instant product and category suggestions as the user types their query.

Configuration-Level Deep Dive: Search Configuration Settings

The following table details the core attributes available within Search Configurations. These settings provide the primary levers for tuning the baseline search algorithm, translating technical parameters into direct business decisions.

Attribute Name	Business Purpose	Available Options/Data Type	Impact and Trade-offs	Concrete Example
Field Weights	To control the baseline importance of different product attributes in determining search result ranking.	Integer (1-20) for weight and phraseWeight.	Higher weights on attributes like ‘Brand’ will prioritize brand matches over other attributes. The trade-off is balancing multiple attributes to match common user intent.	Giving productName a weight of 10 and description a weight of 3 ensures product name matches are ranked significantly higher.
MinMatch	To define the minimum percentage of words in a search query that must match a product for it to appear in results.	Percentage (0-100%). Kibo recommends 100%, 75%, 50%.	A high MinMatch (100%) yields highly relevant but fewer results. A lower value increases the result count but may reduce relevance.	Setting MinMatch to 75% for a 4-word query means products matching at least 3 of the words will be returned.
Phrase Slop	To control how close words in a search phrase must be to each other within a product’s data to be considered a match.	Integer (0 or greater). Kibo recommends 1 or 2.	A slop of 0 requires an exact phrase match. A higher slop allows for more flexible matching but can introduce less relevant results.	A slop of 1 allows “red running shoe” to match “red lightweight running shoe”.
Auto Correct	To automatically correct a misspelled query and show results for the corrected term when the original query yields no results.	Boolean (Enable/Disable).	Prevents a zero-results page, improving user experience. The trade-off is that the system makes an assumption about user intent which could be incorrect.	A user searching for “runnig shoos” is automatically shown results for “running shoes”.
Did You Mean	To suggest an alternative, corrected search term when the original query has few or no results.	Boolean (Enable/Disable).	A less intrusive way to guide users than Auto Correct, giving them control. It requires an extra click from the user.	A user searching for “shiirt” sees a “Did you mean: shirt?” link above the (empty) results.
Product Slicing	To determine if product variations (e.g., different colors of a shirt) appear as single, distinct items in search results.	Boolean (Enable/Disable).	Enabling slicing increases the number of results and is useful for visual merchandizing. Disabling it groups variants into one result, simplifying the view.	With slicing enabled, a search for “shirt” returns separate results for the red shirt, blue shirt, and green shirt.

Layer 3: The Control Layer - Merchandizing Rules

Merchandizing Rules are the tactical layer of the system. They allow business users to manually override the default search algorithm for specific, targeted business purposes, often within a defined timeframe. These rules provide the agility needed to react to market trends, execute promotions, and curate the shopping experience. The platform provides two distinct scopes for rules, which represents a strategic choice about what to target: shopper intent (a specific search query) or shopper location (a specific category page).

Site Search Rules: These rules are triggered by specific Search Terms entered by the shopper. This scope is ideal for campaigns tied to keywords, brands, or product types that users are actively searching for.
Category Rules: These rules are triggered when a shopper navigates to a specific Category page. This scope is used to curate the browsing experience within a particular section of the site, regardless of how the shopper arrived there.

Configuration-Level Deep Dive: Merchandizing Rule Settings

The following table details the attributes for configuring a Merchandizing Rule. This toolkit enables merchandisers to precisely define the trigger, duration, and impact of their strategic overrides.

Attribute Name	Business Purpose	Available Options/Data Type	Impact and Trade-offs	Concrete Example
Schedule	To define the active period for a rule, enabling automated start and end for campaigns.	Start Date/Time (required), End Date/Time (optional).	Enables “set it and forget it” campaigns. If no end date is set, the rule runs indefinitely, which requires manual management.	A “Black Friday” rule is scheduled to start at midnight on Friday and end at 11:59 PM on Sunday.
Criteria (Search Terms)	To trigger a rule based on specific keywords or phrases a shopper enters in the site search.	Text input, case-insensitive.	Allows for precise targeting of user intent. Requires anticipating all relevant search terms for a campaign.	A rule for “winter coats” boosts down jackets and buries raincoats.
Criteria (Categories)	To trigger a rule when a shopper is browsing specific product categories.	Selection from available catalog categories.	Curates the browsing experience for key categories. Does not affect site-wide search results for products in that category.	A rule for the “New Arrivals” category pins the top 5 featured items to the first five positions.
Boost/Bury Condition	To dynamically alter the ranking of products based on their attributes.	Field/Attribute, Operator, Value, Boost/Bury Value (-10 to 10).	A powerful, scalable way to promote or demote groups of products without manual pinning. More efficient for large product sets.	For the query “laptops”, a condition boosts any product where brand equals “Apple” by +10.
Blocked Products	To completely remove specific products from the results for a given rule’s criteria.	Search and select products by Name, Code, or Product Type.	Ensures unwanted or irrelevant products do not appear for a specific query or in a specific category.	For the query “vegan snacks”, any products containing dairy are added to the Blocked Products list.
Manual Ranking	To manually set the exact position of a product in the results, locking all products above it.	Drag-and-drop or enter a rank number in the preview pane.	Provides absolute control over the top results. Less flexible than boosting, as it locks positions.	A merchandiser drags the “Hero Product” to rank #1 for a campaign query.
Pinning	To lock a specific product into a set position without affecting the ranking of other products around it.	Click the pin icon in the preview pane.	Guarantees a product’s position. Recommended for a small number of items.	Pinning a specific accessory to position #3 ensures it always appears there, even if other products are boosted.
Add Products	To inject products into a result set that would not normally appear.	Search and select products to add to the result set.	Useful for cross-selling or promoting related items that don’t match the search criteria.	For a search of “running shorts”, a merchandiser uses “Add Products” to also show a best-selling water bottle.

4. Key Capabilities and Business Applications

The functional components of the Search and Merchandizing system enable a wide range of powerful business strategies. The following examples illustrate how these capabilities can be applied in real-world scenarios across different industries.

Capability: Strategic Relevancy Tuning

Functional Explanation: This capability involves using Field Weights in Search Configurations to establish a baseline search relevance model that aligns with business priorities. It provides a global answer to the question: “For any given search, which product attributes are most important?” This allows the core search algorithm to be tuned to match the primary ways customers search for products.
Business Application Example:
- Industry: B2B Industrial Distributor
- Scenario: The distributor knows its professional clients search by precise part numbers (SKUs) and manufacturer brands far more often than by descriptive keywords. To optimize for this behavior, the search administrator configures the site’s Field Weights to give the productCode attribute the highest weight (e.g., 20), the brand attribute the second highest (e.g., 15), and the productShortDescription attribute a much lower weight (e.g., 5). This configuration ensures that an exact part number match always appears at the top of the results, resulting in a faster, more accurate ordering process for their buyers and reducing the risk of incorrect parts being ordered.

Capability: Dynamic and Automated Merchandizing

Functional Explanation: This capability leverages Boost/Bury Conditions within a Merchandizing Rule to automate the promotion or demotion of entire groups of products based on their attributes, eliminating the need to manage each product individually. This is the most scalable and efficient method for merchandizing large catalogs, as a single condition can affect thousands of SKUs.
Business Application Example:
- Industry: Fashion & Apparel Retailer
- Scenario: To prepare for an upcoming season, the merchandizing team wants to promote all “New Arrival” items and de-emphasize last season’s clearance stock. They create a Categories rule that applies to the “Dresses” category. Within the rule, they add a condition to boost any product where the custom attribute season equals “Fall 2024” by a value of +10. They add a second condition to bury any product where the on_clearance attribute is true by a value of -10. This automatically pushes new inventory to the top and old inventory to the bottom across the entire category, resulting in higher visibility for full-price items and a more efficient sell-through of new stock.

Capability: Search Intent Correction and Guidance

Functional Explanation: This capability combines several tools—Synonyms, Search Term Redirects, and Stop Words—to interpret, guide, and correct user search queries. Synonyms broaden search results to include related terms. Redirects guide users from a search query directly to a curated landing page. Stop words are common words (e.g., “the”, “and”) that can be ignored in queries to focus on the most significant terms.
Business Application Example:
- Industry: Direct-to-Consumer (DTC) Home Goods Brand
- Scenario: The brand sells “sofas,” but analytics show many users search for “couches.” The merchandizing team creates a two-way Synonym set for to ensure all these searches return the same relevant products. Additionally, they are launching a major campaign around their new “Outdoor Living” collection. They create a Search Term Redirect for the query “patio furniture” that sends users directly to the /outdoor-living content page instead of a standard search results page. This provides a more curated brand experience and improves conversion for the high-priority campaign.

5. Platform Integration Map

The Search and Merchandizing system is deeply integrated with other core areas of the Kibo Commerce platform. Its effectiveness is dependent on upstream data sources and has significant downstream impacts on the customer experience and other platform capabilities.

Upstream Dependencies

Catalog and Product Attributes: This is the most important dependency. The richness, accuracy, and structure of the Master Catalog directly determine the potential of the search and merchandizing engine. Attributes must be created with their end use in mind—for example, an attribute intended for use in a merchandizing rule must be defined in the catalog, assigned to products, and then added to the Search Schema to be available for selection.

Downstream Impacts

Storefront Experience: All search and merchandizing configurations directly manifest on the customer-facing site. They influence category navigation, the relevance of search results, and the overall ease of product discovery, which are key factors in determining conversion rates.
Facets and Filtering: Product attributes that are indexed in the Search Schema and marked as “Available as Filter & Sort” can be configured as facets. Facets are the primary tool shoppers use to refine search results and category listings, making the Search Schema a direct prerequisite for a functional faceted navigation experience.

Synergistic Features

Personalization (Monetate Integration): This is the most powerful synergy available on the platform. The system is designed to combine the algorithmic relevance score from Kibo Commerce with a personalization score from the integrated Monetate engine. The final ranking a user sees is a hybrid score. Kibo’s Search Configuration sets the baseline product and attribute relevance, while Monetate adds a user-specific behavioral boost on top. This allows a business to ensure that even personalized results still adhere to core merchandizing strategies. For example, a high-margin item can be given a baseline boost in Kibo, making it more likely to be selected and further promoted by the personalization engine for a specific user. This provides a strategic safety net and control over the personalization algorithm.
Product Slicing: When the Product Slicing feature is enabled, it interacts directly with the search and listing engine. Instead of displaying a single configurable product (e.g., a T-Shirt) with selectable options (e.g., Red, Blue, Green), slicing instructs the search results to display each variation as a distinct, individual product in the grid. This can increase the visual surface area for popular products with many variations, potentially improving engagement and click-through rates.

To fully leverage the Search and Merchandizing capabilities, a comprehensive understanding of related platform concepts is essential.

For foundational knowledge, refer to:

Catalog: This guide explains how to structure master catalogs, catalogs, categories, and product attributes. This knowledge is a prerequisite for building an effective Search Schema, creating category-scoped merchandizing rules, and fueling faceting, filtering, and attribute-based merchandizing rules

To understand downstream impacts, refer to:

Cart & Checkout: This guide explains the process shoppers follow after discovering products. A successful search and merchandizing strategy directly impacts this process by ensuring shoppers can find and add the right products to their cart.

For complementary strategies, refer to:

Promotions: Merchandizing strategies are often executed in direct support of specific promotional campaigns. While the logic for applying promotion is separate, a merchandiser will typically create rules to boost the visibility of the products that are part of an active promotion.

​Conceptual Guide: Search and Merchandizing

​1. Strategic Overview

​Concept Definition

​Business Context

​Value Drivers

​Scope Statement

​2. Core Concepts Explained

​What is Search and Merchandizing?

​Why does Search and Merchandizing matter?

​When to deploy Search and Merchandizing?

​

​3. Functional Components & Configuration Deep Dive

​Component Architecture: A Multi-Layered System of Control

​Layer 1: The Foundation - Search Schema

​Layer 2: The Engine - Search Configurations

​Configuration-Level Deep Dive: Search Schema

​Field Type Analyzers Explained

​

​Configuration-Level Deep Dive: Search Configuration Settings

​

​Layer 3: The Control Layer - Merchandizing Rules

​Configuration-Level Deep Dive: Merchandizing Rule Settings

​

​4. Key Capabilities and Business Applications

​Capability: Strategic Relevancy Tuning

​Capability: Dynamic and Automated Merchandizing

​Capability: Search Intent Correction and Guidance

​5. Platform Integration Map

​Upstream Dependencies

​Downstream Impacts

​Synergistic Features

​6. Related Conceptual Guides

​For foundational knowledge, refer to:

​To understand downstream impacts, refer to:

​For complementary strategies, refer to: