add eCommerce usecase

_data/header_navmenu_datafile.json

@@ -178,7 +178,7 @@
                             "SubLinks": [
                                 {
                                     "Name": "E-Commerce",
-                                    "URL": "/docs/latest/search-plugins/searching-data/index/"
+                                    "URL": "/platform/ecommerce/index.html"
                                 },
                                 {
                                     "Name": "Document Search",

_platforms/ecommerce.md

@@ -0,0 +1,112 @@
+---
+layout: platform-solution
+title: "eCommerce: Elevate Your Search Experience"
+categories:
+  - search
+primary_title: "eCommerce: Elevate Your Search Experience"
+permalink: '/platform/ecommerce/index.html'
+breadcrumbs:
+  icon: search-magnifying-glass
+  items:
+    - title: The OpenSearch Platform
+      url: '/platform/index.html'
+    - title: Search
+      url: '/platform/ecommerce/index.html'
+feature_area_icon_type: search-magnifying-glass
+feature_area_category_name: Search
+feature_area_solution_name: eCommerce
+callouts:
+  - name: 'Advanced full-text search'
+    description: 'Advanced full-text search capabilities with customizable analyzers and tokenizers'
+  - name: 'Near-instantaneous search results'
+    description: "Leverage OpenSearch's inverted index structure for lightning-fast full-text searches across product names, descriptions, and attributes."
+  - name: 'Faceted navigation'
+    description: 'Implement dynamic faceted navigation using bucket and metric aggregations, enabling users to refine search results efficiently.'
+  - name: 'Customizable scoring'
+    description: 'Customizable scoring with script_score and function_score queries.'
+  - name: 'Semantic Search'
+    description: 'Combine vector search with traditional keyword search for hybrid retrieval models'
+
+meta_keywords: 'eCommerce, faceting, prduct search'
+---
+
+## The Critical Role of Search in eCommerce Success
+
+Search functionality is the cornerstone of any successful eCommerce platform, serving as the primary interface between customers and your product catalog. It directly influences user experience, conversion rates, and ultimately, your bottom line. Unlike physical stores where browsing can be a fun activity, online shoppers often arrive with specific intent, making the search bar their first point of interaction. 
+
+A powerful, intuitive search experience can dramatically reduce friction in the customer journey, transforming casual browsers into committed buyers. Moreover, search data provides invaluable insights into customer behaviour, preferences and trends, informing inventory decisions, marketing strategies, and personalization efforts. eCommerce companies attribute a significant portion of their revenue to search-driven sales, with some reporting that search users are twice as likely to convert compared to non-search users.
+
+As such, investing in a robust, intelligent search solution like OpenSearch is not merely a technical decision but a strategic  imperative for any eCommerce business aiming to thrive in the competitive online retail landscape.
+
+## Distributed Architecture for Massive Product Catalogs
+
+OpenSearch's distributed architecture provides the technical foundation for handling product catalogs at scale, easily accommodating millions or billions of items. 
+
+At its core, OpenSearch utilizes a sharding mechanism that horizontally partitions data across multiple nodes in a cluster. This design allows for linear scalability - as your data grows, you can simply add more nodes to the cluster, and OpenSearch automatically rebalances shards for optimal performance. 
+
+The distributed nature of OpenSearch also facilitates high availability and fault tolerance through replica shards, ensuring that your eCommerce platform remains operational even in the face of hardware failures.
+
+To maintain real-time indexing capabilities at scale, OpenSearch employs a sophisticated write-ahead logging system and segment-based storage. New documents are first written to a transaction log and then to in-memory buffers, which are periodically flushed to disk as new segments. 
+
+This approach allows for high-throughput indexing while maintaining durability. The background merge process continuously optimizes these segments, balancing between write efficiency and read performance.
+
+OpenSearch utilizes inverted indices for fast full-text search, complemented by doc values for efficient aggregations and sorting. When dealing with high-cardinality fields, OpenSearch can leverage adaptive replica selection to route queries to the most responsive data nodes, significantly reducing latency.
+
+Furthermore, OpenSearch's caching mechanisms, including query, filter, and field data caches, help in delivering near-instantaneous results even for complex queries on massive datasets. 
+The cache management is adaptive, automatically evicting less useful entries based on usage patterns and available memory. 
+
+For geospatial queries, which are often crucial in eCommerce applications, OpenSearch employs geohash grid aggregations and bounding box filters to efficiently handle location-based searches across millions of products.
+
+This technical architecture ensures that as your product catalog expands, OpenSearch continues to provide sub-second query responses and real-time indexing capabilities.  
+
+## Seamless Integration with Your Inventory Management System
+
+OpenSearch integrates with your existing inventory management system, creating a dynamic and responsive eCommerce ecosystem. 
+
+With OpenSearch's flexible ingestion capabilities, you can ensure that your product search always reflects the most up-to-date inventory information, leading to improved customer satisfaction and reduced operational cost. 
+
+For businesses with inventory data in JSON format, OpenSearch offers a straightforward indexing API. When combined with webhooks, this allows for real-time updates to your search index every time a product is added, modified, or removed from your inventory. This provides the competitive advantage of having new products instantly searchable or out-of-stock items automatically suppressed from search results. 
+
+But what if your inventory data isn't in JSON format? That's where OpenSearch DataPrepper comes to the rescue. This lightweight data ingestion pipeline tool effortlessly transforms your raw inventory data into JSON format on the fly, ensuring compatibility with OpenSearch without requiring changes to your existing systems. Whether you're dealing with CSV files, relational databases, or proprietary formats, Data Prepper acts as a powerful intermediary, enabling you to leverage OpenSearch's capabilities regardless of your current data structure. By bridging the gap between your inventory management system and OpenSearch, you're not just improving search functionality – you're creating a more agile, responsive, and efficient eCommerce platform that can adapt to inventory changes in real-time, ultimately driving more sales and enhancing the overall shopping experience for your customers.
+
+## Optimizing Search Relevancy and Personalizing Results
+
+OpenSearch provides a wealth of options for fine-tuning search relevancy and personalizing results, allowing you to create an optimized eCommerce search experience for your customers. 
+
+At the core of relevancy tuning is the ability to adjust field weights and boost values, giving you granular control over how different product attributes influence search rankings. For instance, you might assign higher importance to product titles and brand names compared to long-form descriptions. 
+
+OpenSearch's function_score query allows for even more sophisticated relevance calculations, incorporating factors like product popularity, profit margins, or inventory levels into the ranking algorithm. 
+
+This flexibility enables you to balance business objectives with user intent, ensuring that search results align with both customer expectations and your commercial goals. Personalization takes this a step further, leveraging user behavior data to tailor search results to individual preferences. You can implement techniques such as collaborative filtering, where the search algorithm considers the browsing and purchase history of similar users to improve result rankings. 
+
+Additionally, OpenSearch's support for geospatial queries allows for location-based personalization, prioritizing results based on a user's proximity to physical store locations or distribution centers. 
+
+These advanced tuning and personalization capabilities, combined with OpenSearch's robust analytics tools, empower you to continuously refine and optimize your search experience, driving higher engagement, conversion rates, and customer satisfaction.
+
+## Mitigating Zero Result Queries and Enhancing Search Resilience
+
+In the realm of eCommerce, zero result queries can be a significant detriment to user experience and potential sales. OpenSearch offers a multifaceted approach to mitigate this issue and ensure users always find relevant products. 
+
+One technique is the implementation of query expansion, where synonyms and related terms are automatically added to the user's original query. This can be achieved through OpenSearch's synonym support, allowing you to define custom synonym sets tailored to your product catalog and industry-specific terminology. 
+
+Another effective strategy is the use of fuzzy matching, which can account for minor misspellings or variations in product names. By adjusting the fuzziness settings, you can control the degree of tolerance for character differences, striking a balance between flexibility and precision in matching. 
+
+## Vector Search Integration: The Next Frontier in eCommerce Relevance
+
+Taking your eCommerce search capabilities to the next level, OpenSearch's vector search integration opens up new possibilities for enhancing relevance and user experience. 
+
+By implementing semantic search using dense vector representations of products and queries, you can capture nuanced relationships between products that go beyond simple keyword matching. This approach is particularly powerful for handling long-tail queries or finding conceptually similar products that may not share exact keywords. For instance, a user searching for "beach vacation attire" might be presented with relevant results like sunhats, sandals, and swimwear, even if these products don't explicitly mention "beach vacation" in their descriptions.
+
+The integration of k-NN (k-Nearest Neighbors) search within OpenSearch provides a robust framework for similarity-based product recommendations. By representing products in a high-dimensional vector space based on their features and attributes, you can efficiently find the most similar items to a given product or query. This capability is invaluable for implementing "More like this" functionality or for generating personalized product recommendations based on a user's browsing history. 
+
+Furthermore, OpenSearch's implementation of the HNSW (Hierarchical Navigable Small World) index algorithm ensures that these vector similarity searches remain fast and efficient, even as your product catalog grows to millions of items.
+
+## Search Metrics Visualizations: Meet OpenSearch Dashboards
+
+OpenSearch Dashboards serves as a powerful companion to OpenSearch, providing a comprehensive interface for managing, analyzing, and visualizing your eCommerce search data. 
+
+The Dev Tools console offers a direct line to the OpenSearch API, allowing technical teams to prototype queries, debug search behavior, and fine-tune relevance algorithms with immediate feedback. This hands-on access is invaluable when optimizing complex search scenarios or implementing custom scoring functions tailored to your unique business rules.
+
+Beyond its technical utility, OpenSearch Dashboards shines in its ability to create custom visualizations and dashboards that offer deep insights into search performance and user behavior. By setting up real-time monitoring of search trends, popular products, and user journey maps, you gain a holistic view of how customers interact with your search functionality. These visualizations can reveal patterns in search behavior, highlight underperforming product categories, or identify opportunities for merchandising and catalog optimization. 
+
+The ability to set up alerts for anomaly detection in search patterns or system performance ensures that your team stays ahead of potential issues, maintaining a smooth and efficient search experience for your customers at all times.

_redesign_use_cases/search-e-commerce.markdown

@@ -0,0 +1,66 @@
+---
+layout: redesign-use-case
+title: eCommerce Use Cases
+description: 'OpenSearch can be used to implement powerful eCommerce search applications. Following are a few examples of solutions you can build.'
+feature_area_category_name: Search
+feature_area_solution_name: eCommerce
+how_to_get_started: 
+  - 'You can get started with OpenSearch&apos;s eCommerce functionality by exploring our <a href="https://opensearch.org/docs/latest/" target="_blank">documentation</a>. To learn more or to start a discussion, join our <a href="https://opensearch.org/slack.html"  target="_blank">public Slack channel</a>, check out our <a href="https://forum.opensearch.org/"  target="_blank">user forum</a>, and follow our <a href="https://opensearch.org/blog/"  target="_blank">blog</a> for the latest on OpenSearch tools and features.'
+button_stack:
+  - download
+---
+{::comment}
+    Implementation note: HTML tables are used instead of markdown, because markdown 
+    does not support the use of colspan which is needed to make all first columns 
+    across tables the same width without worrying about the length of the header text.
+{:/comment}
+<table>
+  <thead>
+    <tr>
+      <th colspan="2">Search</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Product Name and Description Search</td>
+      <td>Enables customers to find products by entering keywords related to the product's name or description. This fundamental use case forms the backbone of eCommerce search, allowing users to quickly locate items based on their knowledge or expectations of the product.</td>
+    </tr>
+    <tr>
+      <td>Category and Faceted Navigation</td>
+      <td>Allows users to browse products by category and refine results using various attributes (facets) such as price range, brand, or color. This use case enhances the user experience by providing intuitive navigation and filtering options, especially useful when customers have a general idea of what they want but need to narrow down their choices.</td>
+    </tr>
+    <tr>
+      <td>Autocomplete and Search Suggestions</td>
+      <td>Provides real-time suggestions as users type their query, helping them formulate their search and find products faster. This feature not only speeds up the search process but also helps users discover relevant products they might not have initially considered.</td>
+    </tr>
+    <tr>
+      <td>Semantic Search and Natural Language Processing</td>
+      <td>Interprets the intent behind a user's query, understanding context and synonyms to provide more relevant results. This advanced capability allows the search engine to handle complex queries and understand user intent, even when the exact product names or attributes aren't used in the search query.</td>
+    </tr>
+  </tbody>
+</table>
+<table>
+  <thead>
+    <tr>
+      <th colspan="2">Personalization</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>User-level content targeting</td>
+      <td>Personalize web pages by using OpenSearch to retrieve content ranked by user propensities using embeddings trained on user interactions.</td>
+    </tr>
+    <tr>
+      <td>Recommendation engine</td>
+      <td>Generate product and user embeddings using collaborative filtering techniques and use OpenSearch to power your recommendation engine.</td>
+    </tr>
+    <tr>
+      <td>Geo-located Product Search</td>
+      <td>Incorporates the user's location to provide relevant results, such as showing nearby store availability or adjusting shipping estimates. This feature is especially valuable for businesses with both online and physical presence, enhancing the omnichannel shopping experience.</td>
+    </tr>
+    <tr>
+      <td>Bundle and Accessory Recommendations</td>
+      <td>Suggests related products, bundles, or accessories based on the items in the search results or the user's cart. This use case not only improves the shopping experience by helping users find complementary products but also increases average order value through cross-selling.</td>
+    </tr>
+  </tbody>
+</table>