Sterling OMS

Overview What if your customers could find the perfect product without ever feeling lost? Guided Selling in Salesforce Commerce Cloud makes that possible — transforming browsing into a personalized, confidence-building journey. By asking the right questions at the right time, you help customers discover what truly fits their needs while capturing valuable data that sharpens your merchandising and marketing strategies. Designing the Right Experience Before building, ask: How complex should this journey be? Do your recommendations rely on a few quick choices, or do they require detailed input? Should customers enter data manually, or will you guide them through preset answers? Your answers determine whether a Simple or Robust Guided Selling model best fits your business goals. The Simple Approach Perfect for quick decisions and frequent updates. When to Use: – You want a lightweight, fast experience.– The goal is to improve completion rates and minimize drop-offs.– Content changes frequently (seasonal products, promotions). How It Works in SFCC – Built using Content Folders for flexible configuration in Business Manager.– Questions, answers, and outcomes managed via JSON for quick edits — no code required.– Enables multiple guided flows, each managed independently. Advantages – Faster to complete and maintain. – Easier to test and iterate. Trade-Offs Generates less granular data for personalization.Offers simpler recommendation logic. The Robust Approach Designed for depth and precision, ideal for complex product sets (e.g., apparel sizing, electronics compatibility). When to Use – Your recommendations depend on multiple data points.– You want richer behavioral insights.– Customer education is part of the buying journey. How It Works in SFCC – Experience logic is built in code, with Content Assets handling visual elements.– JSON controls result mapping, while dataLayer tracking captures user inputs across steps.– Data integrates seamlessly with Google Tag Manager and Marketing Cloud for analytics, remarketing, and A/B testing. Advantages – Enables holistic data collection for advanced personalization.– Supports dynamic recommendations across your site — from pre-filtered PLPs to personalized promos using Content Slots. Trade-Offs – Longer experiences may increase drop-offs.– Changes often require development effort. Making the Experience Count Every click in Guided Selling is an opportunity to learn. Monitor where customers drop off, refine question flows, and test layouts to improve retention. Data from the journey can also fuel smarter personalization — from size suggestions to dynamic offers tailored by customer group. Result Display Options: Quick product carousel within a modal. Curated Product Listing Page (PLP). Direct link to a relevant Product Detail Page (PDP). Choose what aligns best with your brand and audience — what works for a cosmetics brand may not suit electronics or pet food. Designing for Every Screen Guided Selling is only as good as its usability. Build for all devices — mobile-first, but equally optimized for desktops, tablets, and emerging large-screen formats (up to 2100px and beyond). Poor viewport design can alienate customers before they even begin their journey. Closing Thought Guided Selling isn’t just about simplifying choice — it’s about shaping trust. Whether you start simple or go deep, Salesforce Commerce Cloud gives you the flexibility to design, test, and refine experiences that turn curiosity into confidence — and browsers into buyers. LinkedIn X Email Author Details Travis Knese Technical Architect.Over 18 years in IT, with 14 years in Salesforce B2C. I have worked with dozens of enterprise-level clients on a variety of projects. Including things like new site development, payment service migrations, troubleshooting, and other custom projects.

OpenSearch in IBM OMS SaaS & Beyond: A Modern, Open, and Scalable Foundation for Commerce Observability As digital commerce platforms continue moving toward SaaS architectures, real-time visibility into order and inventory transactions, integrations, and the associated events has become a core operational requirement. For IBM OMoC, i.e., Order Management on Cloud (OMS SaaS)customers, IBM’s transition from Graylog to OpenSearch brings next-generation observability and log analytics capabilities to their Digital Commerce operations. OpenSearch is not just an open-source search engine; it has become a foundational component of the OMS SaaS observability model, enabling faster troubleshooting, deeper analytics, and richer insights across all OMS workloads. Why OpenSearch Matters in the IBM OMS World IBM has adopted OpenSearch as its strategic platform for logging, search, and analytics in the Next Gen OMS Platform (OMoC 2.0). This shift brings several important benefits: Unified Observability Across OMS Components Instead of relying on distributed log viewers or siloed monitoring tools, customers gain: A centralized view of OMS logs High-speed search for order and integration flows Standardized event formats across applications and system components This drives clearer visibility for operational, support, and integration teams. The dashboards below represent the Server and OMS health errors – Handling Large Numbers of Regions OpenSearch improves the ability to quickly spot and diagnose issues such as: Slow or failing APIs Delayed message queues Payment or tax service failures Integration delays across commerce systems Interactive dashboards make problem detection significantly faster. Built for High-Volume Retail and Peak Seasons Designed for distributed, high-ingest workloads, OpenSearch scales seamlessly to match the log volume generated by large retailers — particularly during major seasonal peaks. Comparing Distribution Group Model vs. Dynamic Sourcing Model Capability Graylog OpenSearch Examples Full-Text Search Basic Lucene-powered, enterprise-grade Quickly search thousands of OMS logs for a specific order number, API error, or correlation ID using fast, Lucene-based indexing. Scalability Moderate Distributed, high-ingest Handle massive log spikes during holiday peaks or flash sales without performance degradation, due to distributed high-ingest clustering. Machine Learning No Yes, with anomaly detection OpenSearch’s anomaly detection can automatically identify unusual patterns without predefined rules – • Unexpected drops in API throughput for core flows like createOrder or releaseOrder. • Abnormal increases in message queue delays for integrations with WMS, ERP, or tax systems. Extensibility Restricted Plug-ins, ML models, open ecosystem OpenSearch allows additional plug-ins or ML-based features that extend the platform: • Supports ML plug-ins for predicting order volume or latency trends. • Allows visualization plug-ins for richer OMS dashboards. These advancements make OpenSearch a more flexible and future-ready fit for OMS observability. How OpenSearch Enhances Digital Commerce Operations OpenSearch plays a critical role in strengthening observability across digital commerce and OMS implementations, providing deeper operational insight and faster issue resolution. 1. Faster Diagnostics Across the Order Lifecycle OpenSearch dashboards enable teams to: Trace order journeys from capture to fulfillment Detect integration failures in real time Identify API issues, routing problems, or custom logic errors quickly Perform detailed log analysis to accelerate root-cause identification These capabilities help reduce Mean Time to Resolve (MTTR) for OMS-related incidents and improve overall system reliability. 2. Improved Monitoring for Peak Readiness During high-demand periods such as holidays, flash sales, and promotional events, OpenSearch provides visibility into: Log volume spikes and traffic patterns API latency and throughput Fulfillment delays and routing bottlenecks JVM and infrastructure behavior across OMS components This insight supports proactive capacity planning and smoother peak-season operations. 3. Greater Visibility Into Custom Extensions Many OMS implementations incorporate custom elements—such as payment adaptors, inventory or allocation services, specialized order-routing logic, or external commerce integrations.  Here are a few relevant examples: Example 1 – Payment Adaptor MonitoringAn organization using a custom payment adaptor (e.g., for gift cards, or third-party payment gateways) can create an OpenSearch dashboard to track authorization failures, timeout rates, and retry patterns in real time—helping teams detect issues before they impact checkout. Example 2 – Allocation or Inventory Service TrackingIf an implementation uses a custom ATP service to determine inventory availability, OpenSearch can visualize trends such as response latency, allocation decision outcomes, exceptions, or API degradation—ensuring smoother order promising. Example 3 – Custom Order Routing LogicOrganizations with bespoke routing rules (store-first, region-first, cost-based routing, etc) can use OpenSearch to monitor routing decisions, identify bottlenecks, and detect mis-routed orders through custom logs. Example 4 – External Commerce or ERP IntegrationsFor integrations with SAP, Salesforce Commerce, Shopify, or warehouse systems, OpenSearch dashboards can highlight message failures, queue delays, or payload anomalies, enabling faster triage when an external dependency slows down the OMS. OpenSearch enables the creation of targeted dashboards to monitor these custom components alongside core OMS flows, ensuring unified observability across the entire digital commerce ecosystem. OpenSearch dashboards enable teams to: Trace order journeys from capture to fulfillment Detect integration failures in real time Identify API issues, routing problems, or custom logic errors quickly Perform detailed log analysis to accelerate root-cause identification These capabilities help reduce Mean Time to Resolve (MTTR) for OMS-related incidents and improve overall system reliability.During high-demand periods such as holidays, flash sales, and promotional events, OpenSearch provides visibility into: Log volume spikes and traffic patterns API latency and throughput Fulfillment delays and routing bottlenecks JVM and infrastructure behavior across OMS components This insight supports proactive capacity planning and smoother peak-season operations. Many OMS implementations incorporate custom elements—such as payment adaptors, inventory or allocation services, specialized order-routing logic, or external commerce integrations.  Here are a few relevant examples: Example 1 – Payment Adaptor MonitoringAn organization using a custom payment adaptor (e.g., for gift cards, or third-party payment gateways) can create an OpenSearch dashboard to track authorization failures, timeout rates, and retry patterns in real time—helping teams detect issues before they impact checkout. Example 2 – Allocation or Inventory Service TrackingIf an implementation uses a custom ATP service to determine inventory availability, OpenSearch can visualize trends such as response latency, allocation decision outcomes, exceptions, or API degradation—ensuring smoother order promising. Example 3 – Custom Order Routing LogicOrganizations with bespoke routing rules (store-first, region-first, cost-based routing, etc) can use