Now that we’ve covered why the shift from CPQ to Agentforce Revenue Management is happening in Part 1, let’s dive into the specifics. Understanding the differences between Agentforce Revenue Management Advanced, Billing , and Growth (RCG) is crucial for making the right investment decision for your business. Agentforce Revenue Management Advanced (RCA): The Complete Quote-to-Contract Solution What Makes RCA Different from CPQ Native Platform Architecture Unlike CPQ’s managed package approach, RCA is built directly on Salesforce’s Einstein 1 platform using standard objects. This architectural difference delivers: Better performance with large quotes and complex pricing Native AI integration with Einstein and Agentforce capabilities Modern Lightning UI that feels seamless with the rest of Salesforce API-first design enabling headless commerce and omnichannel experiences1 Flexible Product and Pricing Models Here’s a game-changer: in RCA, a single product can support multiple pricing models without creating separate SKUs. Want to sell the same software as a one-time license, monthly subscription, or usage-based model? RCA handles it all within one product record. Enhanced User Experience RCA delivers a modern Lightning Web Component (LWC) based interface with features like: Excel-like grid for quote lines Drag-and-drop product configuration Real-time pricing visibility Guided Selling and recommendations. Core RCA Capabilities Product Catalog Management Centralized product database accessible across all sales channels Intuitive bundle creation and management Template-based product introduction for faster time-to-market. Advanced Pricing Management Dynamic pricing procedures that adapt to different scenarios Complete pricing waterfall transparency Real-time discount guidance and approval routing. Contract Lifecycle Management Built-in contract creation, redlining, and approval workflows Clause libraries and templates AI-assisted contract management No need for separate tools like DocuSign or Nintex. Order Management & Fulfillment Dynamic Revenue Orchestrator (DRO) for complex fulfillment processes Ability to decompose commercial orders into technical fulfillment tasks Integration with downstream systems4 Simplified Asset Management Subscriptions become assets with a cleaner data model Ability to amend individual assets instead of entire contracts Complete historical tracking of all changes. Agentforce Revenue Management Growth (RCG): Streamlined Essentials for Growing Businesses Agentforce Revenue Management Growth is designed as a lightweight, cost-effective edition tailored for mid-market companies or organizations with less complex revenue processes. It delivers the fundamental capabilities needed to automate quoting, pricing, and order management on the Salesforce platform. Key Features: Product Catalog & Price Management Configure, Price, Quote (CPQ) capabilities for streamlined quoting Order and Asset Lifecycle Management This edition does not include advanced features like order decomposition and orchestration, contract lifecycle management, billing schedules, revenue management intelligence, or Salesforce’s AI-powered Agentforce. Its simpler feature set makes it ideal for businesses with standard sales models and relatively straightforward fulfillment and billing. Agentforce Revenue Management Billing (RCB): The Financial Powerhouse Agentforce Revenue Management Billing is where sophisticated billing happens. While RCA includes basic billing capabilities, RCB is designed for businesses with complex billing requirements and advanced financial operations. Advanced Billing Capabilities Flexible Billing Policies & Schedules RCB supports any billing frequency you can imagine—monthly, quarterly, anniversary-based, milestone-driven, or completely custom schedules. The system handles complex scenarios like: Mid-cycle proration calculations Custom billing schedules that align with customer budget cycles Automated billing policy application based on customer attributes. Usage-Based & Consumption Billing This is where RCB really shines for modern business models: Digital wallets for prepaid services Rollover credits and usage grants Tiered pricing based on consumption Real-time usage monitoring and alerts. Intelligent Invoice Management Automated grouping of multiple billing items into consolidated invoices Professional invoice presentation that matches customer processes Smart invoice preview and validation capabilities3 Automated Collections & Payment Processing AI-powered collection strategies based on customer history Automated follow-up for overdue accounts Multiple payment methods and gateway support Automated credit memo application. Financial Accounting Integration Automatic journal entry generation for all billing transactions GL account mapping and multi-currency support Compliance with ASC 606 and IFRS 15 revenue recognition standards Real-time financial reporting and analytics. RCG vs RCA vs. RCB: The Complete Feature Comparison Based on the official Salesforce documentation, here’s what’s included in each license: Feature Category Agentforce Revenue Management Growth (RCG) Agentforce Revenue Management Advanced (RCA) Agentforce Revenue Management Billing (RCB) Product Catalog & Price Management ✔️ ✔️ ✔️ (Basic Access) CPQ ✔️ (Streamlined) ✔️ (Enhanced, AI-Guided) ⚠️ (Basic quoting only) Order and Asset Lifecycle Management ✔️ ✔️ ✔️ Order Decomposition & Orchestration ❌ ✔️ ⚠️ (Basic order processing) Contract Management ❌ ✔️ (Full Lifecycle) ⚠️ (Basic contract billing) Billing Schedules ❌ ✔️ ✔️ (Advanced) Revenue Management Intelligence ❌ ✔️ ✔️ (Advanced Recognition) Agentforce for Revenue ❌ ✔️ ❌ Usage-Based Billing ❌ ❌ ✔️ (Full capabilities) Payment Processing ⚠️ (Limited) ⚠️ (Limited) ✔️ (Full payment lifecycle) Collections Management ❌ ❌ ✔️ (Automated, AI-Powered) Financial Accounting ⚠️ (Basic GL Integration) ⚠️ (Basic GL Integration) ✔️ (Full Accounting Integration) Subscription Management ✔️ ✔️ ✔️ (Full Lifecycle & Billing) Making the Right Choice for Your Business Choose Agentforce Revenue Management Growth if: You want core quoting, pricing, and order management with a simple, efficient platform optimized for mid-market businesses with straightforward revenue models. Choose Agentforce Revenue Management Advanced if: You need a full quote-to-contract solution with advanced pricing, contracts, order orchestration, and AI-powered sales enhancements. Add Agentforce Revenue Management Billing if: Your organization requires complex, consumption-based billing, sophisticated payment processing, collections automation, and deep financial accounting integration. The Decision Framework Small to Mid-Market Companies: Agentforce Revenue Management Growth or optionally RCA alone may suffice if billing is straightforward and usage-based billing is limited. Enterprise Organizations: RCA + RCB is the comprehensive path for handling complex order, billing, and financial workflows at scale. The Migration Path from CPQ Don’t wait for CPQ to end-of-life. Companies that plan their migration now have significant advantages: More time for thorough requirements analysis Ability to clean up years of technical debt Opportunity to implement modern best practices Better negotiating position with implementation partners The Bottom Line Agentforce Revenue Management Growth, Advanced, and Billing together form a strategic suite designed to meet diverse revenue lifecycle needs, from streamlined quoting to sophisticated billing and financial recognition. Choosing the right edition or combination empowers your business to drive
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The AI Agent Stack Decoded: When to Use MuleSoft, Data Cloud, or MCP
Every week, a new AI platform promises to “revolutionize” your business, but which ones actually fit your use case? Should you be building with MuleSoft, Data Cloud, or MCP? The answer depends on what you’re trying to make your agents do: connect systems, understand data, or take action. This post breaks down how these three technologies work together and includes a handy heatmap that shows which tool is best for each AI use case, from order-to-cash automation to personalized marketing. The “front end” of modern applications isn’t just a screen with buttons anymore – it’s becoming a team of smart agents that can talk, think, and get things done for you. The real magic happens when those agents are trained with the right, high-quality data and equipped with the right tools so they can take meaningful action. Without that, they’re just chatbots in nicer clothes. This is where MCP is changing the game – making it easier for agents to securely connect to any system, share context, and deliver real results. MCP, or Model Context Protocol, is quickly emerging as the universal language for AI agents. Traditionally, each agent needed custom integrations – a messy, repetitive, and fragile approach. MCP solves this by providing a secure, standardized handshake between agents and the tools or data they need. Think of it as giving every agent a universal passport and translator, allowing them to tap into Salesforce, ERP, analytics platforms, and even niche APIs without complex rewiring. This doesn’t just accelerate development – it tears down the silos that have kept enterprise data and actions locked away, unlocking a new era of collaborative, context-aware agents. MCP has moved from concept to reality with stable message types, multiple transports, and official SDKs and native support in Claude Desktop, Cursor, and OpenAI’s Responses API. Salesforce is going all-in with Agentforce 3, letting agents connect to any MCP server without custom code, while shipping built-in MCP servers for CRM data, Salesforce DX developer tasks, and Heroku app management. The real breakthrough is MuleSoft MCP integration – any MuleSoft API instantly becomes an MCP-ready tool, giving agents secure access to their vast integration network of Salesforce, ERP systems, databases, and more. Next up: multi-agent orchestration by pairing MCP with the upcoming A2A protocol for agent-to-agent collaboration. But connectivity is only half the equation. To make agents truly intelligent, they need the right data at the right time. That’s where Salesforce’s Data Cloud steps in. Acting as the real-time unified data backbone, Data Cloud merges structured and unstructured information from CRM, data lakes, warehouses, videos, audio, and more – without duplication – into a rich, governed Customer 360. Features like real-time ingestion, hybrid search for Retrieval-Augmented Generation, vectorized knowledge, and semantic models keep agents contextually sharp and action-ready. Whether it’s resolving a support ticket, spotting a fraud pattern, or triggering the next best action in Slack, Data Cloud ensures agents respond with both speed and precision. Choosing the Right Pillar for AI-Driven and Agentic Workflows MuleSoft – Deep system reach for agentsWhen AI agents need to navigate a web of enterprise systems – ERP, CRM, finance, supply chain, or even mainframes – MuleSoft is the bridge. Its API-led approach transforms complex integrations into reusable building blocks that agents can call in real time. Perfect for workflows like creating a sales order, updating inventory, and triggering logistics all in one go. Data Cloud – Context-aware, data-driven agentsEven the smartest agent is useless without good context. Data Cloud builds the live, unified, governed Customer 360 that powers AI decision-making. It’s the choice when the mission is deep personalization, accurate recommendations, and real-time insights – like tailoring an offer on the spot or solving a service issue before the customer even calls. MCP – Secure, consistent action-taking agentsWhen the goal is enabling agents to do things, MCP is the standard. It gives agents a secure, governed “command panel” to access approved tools and APIs without custom connectors. Ideal for workflows that require agents to pull live data, update records, and trigger actions across SaaS, on-prem, and cloud systems – all in a single conversation. Heatmap of Use Cases Across MuleSoft, Data Cloud, and MCP Use Case Best with MuleSoft Best with Data Cloud Best with MCP Order-to-Cash Automation Orchestrating ERP, CRM, and payment systems into a seamless workflow Not primary use Agent triggers order creation, invoicing, and payment collection across systems Customer 360 for Service Agents Connecting siloed systems (support, billing, product usage) Creating a unified, real-time view of the customer for context Agent retrieves and updates records on the fly during a support chat Fraud Detection Aggregating data feeds from banks, payment gateways, and risk engines Running real-time fraud detection models across unified data Agent blocks a suspicious transaction or alerts compliance team Personalized Marketing Journeys Integrating ad platforms, CRM, and campaign tools Powering segmentation, next-best-action, and personalization models Agent auto-creates campaigns or updates segments dynamically Inventory Supply Chain Visibility Connecting warehouse, logistics, and ERP systems Analyzing trends and predicting shortages with real-time signals Agent reorders stock or reprioritizes shipments automatically Healthcare Patient Experience Integrating EHR, scheduling, and billing systems Creating a longitudinal patient record with context Agent books appointments, updates charts, and sends reminders Sales Forecasting Consolidating CRM, ERP, and partner sales data Running predictive models on live pipelines Agent updates forecasts, sends reports, and flags risks Field Service Management Connecting IoT device data, parts inventory, and service apps Providing predictive maintenance insights Agent schedules technician visits, orders parts, and closes tickets Multi-Agent Orchestration Providing APIs from enterprise systems into orchestration layer Supplying context data across agents Enabling agents to securely call each others tools and coordinate tasks
Salesforce Data Cloud Series Part 3: What the Informatica Acquisition Means for Enterprises That Count on Data Trust and Compliance
In regulated sectors like Medtech, Life Sciences, Manufacturing and IoT, innovation doesn’t begin with data—it begins with trusted data. These industries already invest heavily in ensuring data confidence, compliance, and actionable insights, all while navigating complex regulatory requirements. Salesforce’s $8 billion acquisition of Informatica becomes particularly significant within this landscape. More than just a tech merger, this move signals enhanced data governance for Salesforce, accelerating enterprise data trust and compliance across industry standard regulations like GDPR, HIPAA, and DSAR. In this final post of our Salesforce Data Cloud series, we explore what this acquisition could unlock for organizations managing complex, regulated data environments, and for the future of trusted, scalable enterprise data. In case you missed previous installments in this series, here’s Part 1: The Foundation for AI-Ready Data, where we covered the essential building blocks of Salesforce Data Cloud and Part 2: AI-Ready Data in Action, where we explored critical use cases for AI-ready data across Medtech, High-Tech & IoT, and Manufacturing. What Informatica Brings to This Acquisition: Scalable Trust and Intelligent Governance Informatica has long been recognized as a leader in enterprise-grade data management. According to Gartner’s 2024 Magic Quadrant for Data Integration Tools, Informatica was positioned as a Leader for the 19th consecutive year, ranked highest for its ability to execute and furthest for completeness of vision. Before we explore the potential significance of this acquisition, it’s worth looking at what Informatica already delivers. Known for its depth in data management, Informatica brings a mature set of capabilities that help enterprises across highly regulated industries govern, clean, and protect data at scale. Data Governance and Transparency Informatica provides tools for data cataloging, metadata management, and lineage tracking that show how data moves through the enterprise. This visibility supports audit readiness, simplifies compliance reporting, and improves oversight across increasingly complex data estates. Data Quality and Profiling With automated profiling, cleansing, and anomaly detection, Informatica ensures that data used in AI, analytics, and operations is consistent and reliable from the start. That means fewer delays, less manual cleanup, and stronger confidence in downstream decisions. Automated Privacy and Compliance Management Informatica streamlines regulatory compliance with built-in tools for data masking, anonymization, and consent tracking. It also supports DSAR (Data Subject Access Requests) compliance, required under privacy laws like GDPR and CCPA, helping teams respond quickly to individual data rights requests without tying up valuable resources. These enterprise-grade Master Data Management capabilities make Informatica a key enabler of data trust and governance in industries requiring accuracy, transparency, and compliance. Building on Salesforce’s Existing Strengths A Unified AI-Data Platform Salesforce already offers a robust suite of tools—Einstein AI, Data Cloud, MuleSoft, and Tableau—that help enterprises unify data, extract insights, and operationalize intelligence across the business. With Informatica, these capabilities are reinforced by deeper governance and quality controls that support more reliable, scalable data strategies. Data Cloud: Informatica’s Master Data Management (MDM) creates “golden records” by resolving duplicate profiles and standardizing key attributes. This enables a single, trusted view of customers and stakeholders—critical for personalization, reporting, and compliance. MuleSoft: With cleaner, governed data flowing through APIs, integrations are more dependable. This reduces failure points between systems and improves the consistency of data powering downstream workflows. Tableau: In addition to visualization, users can access metadata such as data lineage, quality scores, and compliance status. That context improves confidence in analytics and supports audit-readiness. Together, these capabilities strengthen Salesforce’s role as the backbone of enterprise data strategy—supporting more accurate reporting, faster automation, and smarter AI-driven outcomes. The Opportunity: Real-World Impact of Improved Trust & Governance The integration of Informatica’s capabilities into the Salesforce ecosystem could help enterprises build governed, high-confidence data workflows with greater scale and precision. Here’s a glimpse into what that could look like across regulated industries. MedTech & Life Sciences Clinical trials, patient records, EHR systems, and connected devices generate massive volumes of sensitive data, often trapped in disconnected systems. Informatica’s Master Data Management (MDM) creates unified “golden records” (consolidated, accurate customer or patient profiles) that resolve duplicates and enable cleaner, audit-ready datasets. Paired with Data Cloud’s real-time activation, organizations could spend 20% less time chasing orders, respond faster to care delivery needs, and automate HIPAA compliant workflows. The result: faster clinical decisions, fewer data risks, and greater patient trust. Manufacturing & IoT Siloed ERP data, supply chain systems, and production line sensors often lead to costly inefficiencies and blind spots. With Informatica’s data integration and quality layers feeding governed data into Salesforce and Einstein AI, predictive agents can proactively surface issues—whether it’s a delayed component delivery or a machinery failure. Early pilots show up to 30% efficiency gains in sales and production planning with significant cost savings from predictive maintenance powered by cleaner, more consistent data. Compliance at Scale From GDPR to HIPAA, compliance requires continuous and verifiable control over how sensitive data is accessed, processed, and stored. Informatica’s privacy tools automate consent tracking, data masking, and DSAR fulfillment, ensuring AI systems only act on data that meets privacy policies. For example, if consent is missing or inconsistent, the system can automatically pause related processes—minimizing compliance risks and reducing manual audit efforts. The combination of these platforms and strategic implementation could empower enterprises to move beyond data firefighting towards faster, more confident decisions on trusted, compliant data. CLAIRE + Agentforce: Context-Aware AI That Operates With Confidence Informatica’s CLAIRE engine brings deep, metadata-driven intelligence to enterprise data. When combined with Salesforce’s Agentforce platform, it powers a new generation of AI agents—ones that don’t just access data, but understand the context, rules, and relationships that govern it. Imagine asking, “Why is revenue different in Salesforce vs. Tableau?” Instead of raising a ticket for manual investigation, an AI agent powered by Claire GPT could trace the data lineage, flag the inconsistency, and suggest next steps with complete transparency. Or consider an AI agent detecting an anomaly in an IoT device, checking regulatory impact, verifying service policies, scheduling a technician, and notifying the customer. Every action is logged to meet compliance requirements, and decisions
Salesforce Data Cloud Series Part 2: AI-Ready Data in Action
No matter how advanced your AI models get, the true power of enterprise AI boils down to the quality and structure of data that fuels it. Without a clean, unified foundation, even the most ambitious AI strategies can fall short, leading to fragmented insights, unreliable automation, and limited scalability. In our previous post, Salesforce Data Cloud Series Part 1 — The Foundation for AI-Ready Data, we covered the various steps your enterprise data takes to become AI-ready—from ingestion and harmonization to identity resolution and real-time activation. But, what can this AI-ready data unlock for your enterprise? In Part 2 of this 3-part series, we move from architecture to impact—highlighting real-world use cases of Salesforce Data Cloud’s AI capabilities across Medtech, Manufacturing, High-Tech and IoT. Figure 1: Unified, comprehensive customer profiles are a core outcome of Salesforce Data Cloud’s Identity Resolution step, post data ingestion and harmonization. (Source: Salesforce.com) AI-Ready Data in MedTech – Enhancing Patient Outcomes and Device Management The Challenge In MedTech, enterprise data is everywhere—scattered across clinical trials, patient records, and connected devices. Without a unified view, your teams lack real-time insights, and are often left waiting or relying on guesswork to answer critical questions like: “How are our devices performing in the field?”, “Are we pricing effectively?”, “How can we accelerate deal cycles?”, and “Where are our biggest compliance gaps?” This data fragmentation undermines efforts to drive patient outcomes, operational efficiency, and regulatory compliance. Gaining a clean, unified view of your data is essential to ensure better device performance and tangible business impact. How Salesforce Data Cloud Unifies MedTech Data to Power Enterprise Outcomes Salesforce Data Cloud transforms MedTech’s fragmented data into actionable insights in real-time by: Ingesting data from clinical trials, patient records, and connected devices into a unified platform. Standardizing and cleaning data to resolve inconsistencies across systems. Resolving patient identities across scattered sources to create accurate, consolidated patient profiles. Segmenting data by geography, usage trends, provider, or clinical pathways to enable targeted actions. Leveraging AI models to identify patterns in device usage and patient response—helping teams anticipate maintenance, spot at-risk patients, and support care proactively. In practice, this means your teams have consistent, unified access to real-time insights that enable proactive device management and patient outcomes. What Enterprise Outcomes Can AI-Ready Data Drive for MedTech? AI-ready data delivers tangible, enterprise-level benefits, including: Better patient outcomes with personalized care and proactive device management that cut downtime and boost treatment effectiveness. Greater operational efficiency through automated insights, streamlined pricing, and reduced manual work and errors. Stronger regulatory confidence with accurate, harmonized data, full audit trails, and real-time quality monitoring. For example, fragmented data can cause MedTech teams to spend up to 20% of their time on administrative tasks like order tracking. By integrating disparate systems and leveraging AI-ready data, MedTech organizations can significantly reduce these inefficiencies—cutting operating costs by up to 30% and enabling teams to focus on strategic priorities. AI-ready data is critical for MedTech companies aiming to simplify operations, improve patient outcomes, and drive growth, while remaining compliant. Wondering what AI-ready data can do for your MedTech enterprise? Get in touch to explore our offerings. AI-Ready Data in Manufacturing – Maximizing Operational Efficiency and Forecast Accuracy The Challenge In manufacturing, enterprise data is scattered across ERP systems, production lines, inventory, and quality control. Without a unified view, your teams lack real-time visibility and are often forced to rely on manual workarounds or incomplete data to answer questions like: “Where are we running short or overproducing?”, “Are we catching downtime risks before they escalate?”, and “How reliable is our forecast?” This data fragmentation undermines your efforts to prevent downtime, optimize planning, and maintain consistent production quality. Gaining a clean, unified view of your data is essential to streamline operations, improve forecast accuracy, and deliver tangible business impact. How Salesforce Data Cloud Unifies Manufacturing Data to make it AI-Ready Salesforce Data Cloud transforms manufacturing’s fragmented data into actionable insights in real time by: Ingesting data from ERP systems, production lines, and quality control into a centralized platform—giving operations teams a single source of truth. Segregating data by manufacturing unit or region to allow local teams control while ensuring centralized visibility and governance. Powering real-time dashboards and automating alerts for inventory shortages or production anomalies—so teams can respond faster and more effectively. Leveraging advanced forecasting models to predict equipment failures and demand fluctuations—helping teams minimize downtime and optimize resource planning. In practice, this means your teams have consistent, unified access to real-time insights that keep production efficient, planning accurate, and operations resilient. What Enterprise Outcomes Can AI-Ready Data Drive for Manufacturing? AI-ready data delivers tangible, enterprise-level benefits, including: Greater operational efficiency through real-time visibility and automated alerts that reduce downtime and keep production flowing. Lower costs from proactive maintenance and accurate forecasting that minimize production waste and expensive equipment failures. Improved customer experience through reliable supply chains and production schedules that support consistent delivery. For example, fragmented data can cause inefficiencies in sales planning and forecasting—limiting visibility and delaying decision-making. By integrating disconnected systems and leveraging AI-ready data, manufacturing organizations can boost sales planning efficiency by up to 30% and improve forecast accuracy across the board. AI-ready data is essential for manufacturers looking to reduce downtime, lower costs through proactive maintenance, and improve forecast accuracy and customer experience. Wondering what AI-ready data can do for your Manufacturing enterprise? Get in touch to explore our offerings. AI-Ready Data in High-Tech & IoT – Driving Real-Time Insights and Scalable Innovation The Challenge In High-Tech and IoT, enterprise data flows in from everywhere—devices, sensors, apps, and user interactions—often scattered across disconnected systems. Without a unified view, your teams lack real-time visibility and are left relying on fragmented signals to answer key questions like: “Which features are actually being used—and which aren’t?”, “Where and when are our devices at risk of failure?”, “How can we tailor support based on individual user behavior?” This data fragmentation slows innovation, limits personalization, and makes it harder for your teams to respond to users
Salesforce Data Cloud Series Part 1: The Foundation for AI-Ready Data
Imagine trying to run a high-performance car on low-quality fuel—no matter how advanced your engine, it won’t perform at its best. The same is true for AI. It’s only as powerful as the data you build it on. And the reality is stark. According to Gartner Research, only 4% of organizations report their data is prepared for AI. This gap highlights that while companies are eager to deploy AI, their enterprise data often isn’t prepared to fuel it. Fragmented across CRMs, ERPs, and third-party tools, these data silos pose a critical challenge for enterprise data management, often delaying your data-to-AI pipeline and limiting strategic decisions. Fortunately, Salesforce Data Cloud is designed to solve exactly that by unifying, cleaning, and activating your data across sources, unlocking its full potential across your enterprise. In this Part 1 of a 3-part series, we begin with the enterprise data architecture—the building blocks that power your Salesforce Data Cloud. To truly make sense of the outcomes it can unlock for your enterprise, you’ll first need to understand the foundations it’s built on. The Journey from Raw Data to AI-Ready Insights From initial capture to real-time insights, enterprise data undergoes a rigorous journey to become reliable and ready to drive strategic outcomes. That journey begins with Ingestion. Salesforce Data Cloud’s architectural journey: from raw data ingestion and harmonization through unification and insight generation, preparing data for AI applications. (Source: Salesforce Developer Documentation) 1. Ingestion: Capturing Data in its Raw Form Whether it’s coming from your Sales Cloud, ERP, IoT devices, or external files, every piece of data passes through a Data Source Object (DSO). The DSO is like a diligent front desk, logging each entry exactly as it arrives. It preserves every detail before any processing or transformation begins, ensuring nothing gets lost or distorted in this first step. Once ingested, your data then moves into Data Lakes Objects (DLOs) —the structured back office. DLOs store enterprise data in efficient, industry-standard formats, making it easy to query, analyze, and enrich. After this initial structuring, your data is now ready for the next crucial step —Data Harmonization. 2. Data Harmonization: Making Data Speak the Same Language Similar information within your data might be stored or labeled differently depending on the systems they originate from. A customer field, for example, could be labeled a “Contact” in your CRM, a “Patient” in healthcare records, or a “Guest” within a booking platform. This critical step of standardizing and unifying disparate data labels and formats is called Data Harmonization. This is where your data enters Data Model Objects (DMOs). They act like a translation office for your data, unifying different labels by mapping them to a single, common object —such as “Individual” in the above example. This ensures each entity is treated as one cohesive profile no matter the data source it came from. Beyond translation, DMOs also apply standardization rules. They align date formats, normalize units like weight and accuracy, and structure names and fields consistently. This is a crucial step in ensuring your teams and AI models speak a common language, and work from a shared playbook of truth. 3. Identity Resolution: One Truth for Every Customer The third phase, Identity resolution, is where Salesforce Data Cloud’s capabilities truly come to the forefront. This is the stage where records that represent the same person, like “John Smith” in your CRM, “J. Smith” in your email list, and “Johnny S.” are recognized and merged into a singular, accurate profile. It deploys several matching techniques to achieve this: Fuzzy Matching finds close matches that aren’t exact (e.g., “Jon Smith” vs. “John Smith”) Exact Matching finds matching through precise identifiers (e.g., the same customer ID) Normalized Matching finds matches after standardization (e.g., phone numbers ‘(123)-456-7890’ and ‘123-456-7890’ would standardize to ‘1234567890’, detecting that these belong to the same person despite different formatting rules). Here’s a real-world example: A hospital sees a patient in both its CRM and EHR data, with minor differences in their name or contact information. Identity resolution assigns rules like “most recent update” to select the latest record as the most accurate, while “source priority” rules choose data from more reliable systems to create a singular, trusted profile for every user. 4. Dynamic Segmentation: Turning Data into Meaningful Groups Dynamic Segmentation in action: Leveraging a unified view of customer data (as seen in the dashboard) to target specific segments for tailored outreach and enhanced personalization. With this unified data at your fingertips, you now have a complete, accurate view of every customer, partner, or asset across your organization. You could group individuals or entities by their behaviors, demographics, or any attribute that matters to your business. This process, known as dynamic segmentation, allows you to tailor your outreach, spot emerging trends, and target specific customer needs with precision. For example, a manufacturer might segment customers by industry, purchase history, or service needs, enabling sales teams to craft personalized campaigns that resonate with specific customer pain-points and challenges. Or, a healthcare provider could group patients by risk factors, ensuring timely interventions and better care. The possibilities are as diverse as your data—and with every segment, you unlock new opportunities to engage, support, and build lasting customer relationships. 5. Data Actions: Automating Insights into Action Segmentation is just the beginning. The real magic of your Salesforce Data Cloud starts to unfold when your unified data starts to drive action automatically through Data Actions. This feature enables you to set up sophisticated cloud workflow automations that respond to real-time changes within your data. For instance, if a high-value customer shows signs of inactivity, a Data Action can trigger precise Salesforce automation, like a re-engagement campaign or alert a sales rep to reach out to the customer. Similarly, a sudden spike in support requests from a particular customer segment could escalate the issue to your service team automatically. This level of automation turns insights into action, so your teams are always a step ahead. Data Actions make your data work for you—reducing
Salesforce CPQ for Medical Manufacturers
Medical equipment manufacturing sales is a tooth and nail business. What we often hear from our clients is the need to be flexible when it comes to pricing. That translates to the ability to support different pricing strategies for different buying groups. Recently, we delivered a transformative pricing project with a global medical equipment manufacturer. There were three key components to this transformation project: Agility Transparency Workforce enablement The overall challenge was simplifying the processes involved in delivering quotes to clients. The legacy process required coordination across multiple teams and systems. Getting a simple quote to the client required coordinations between three different teams using multiple systems. Our objective was to consolidate these disparate processes under one roof: Salesforce CPQ. The aim was to not just streamline operations but to empower Sales and Marketing with self-service pricing capabilities. Given that medical manufacturers operate in a highly regulated market, it can take years—from initial concept—to putting out a saleable product. Therefore, go-to-market pricing strategies are often kept simple using a combination of cost- and reference-based pricing. After the product is launched, the next big game is the ‘buying group’. The top three buying groups in the industry are group purchasing organizations (GPOs), aggregation groups (Agg Groups) and integrated delivery networks (IDNs—often also referred to as health systems). The size and volume of business these groups deal with often varies geographically. For instance GPOs are large, powerful groups in the US, but not so in Canada. Here is an example of how a hospital is aligned to an IDN, GPO and Agg Group: Salesforce’ CPQ offers a wide array of pricing-related functionalities out of the box. Examples include product and pricing rules, product bundling, and so on. At N28, the art and science of configuring Salesforce CPQ for simple quoting has been done and dusted many times over for various clients. The complexity in this instance was due to the intricate web of buying groups and their overlapping pricing structures. A single hospital was accessing pricing from the IDN it belonged to and also from all the GPOs and aggregation groups it was associated with. The N28 team implemented a tailored Salesforce CPQ system that enabled multiple tiers of pricing access for a single SKU. This includes: Tiered pricing for influential buying groups Bundled packages for value-based pricing, and… Support for distributor/reseller models. Here is a custom pricing access view component we developed to simplify the complexity of the underlying business model. N28 equips medical manufacturers with the tools to master the complexities of pricing strategies. Our expertise in Salesforce CPQ implementation ensures that clients can seamlessly adapt to the diverse demands of various buying groups while optimizing internal processes. The result? Enhanced efficiency and profitability in an ever-evolving and competitive market landscape. Get In Touch