Revenue Lifecycle Management (RLM)

Revenue Lifecycle Management (RLM): A New Era of Salesforce Monetization

Salesforce is evolving beyond traditional CPQ with the introduction of Revenue Lifecycle Management (RLM) a unified approach to manage the entire quote-to-cash journey on the Salesforce platform.

1. Enter Revenue Lifecycle Management (RLM)

Revenue Lifecycle Management (RLM) is Salesforce’s next-generation solution that streamlines the complete revenue process—from product setup and pricing to quoting, contracting, and orchestration.

RLM became Generally Available (GA) on 13 February 2024, with a press release on 17 June 2024 highlighting adoption across Professional Services and Software industries. Built natively on the Salesforce Platform, RLM brings together CPQ, pricing, contracts, and orchestration into a single, cohesive architecture.

2. Key Features of RLM

RLM combines multiple revenue-critical capabilities into one unified framework:

• Configure, Price, Quote (CPQ) for fast and accurate quoting
• Product Catalog Management (PCM) and Salesforce Pricing for centralized product and price control
• Dynamic Revenue Orchestrator (DRO) for decomposition and orchestration of complex transactions
• Business Rules Engine (BRE) to enforce pricing and eligibility rules
• Salesforce Contracts for contract creation and lifecycle management
• Native support across Sales Cloud, Service Cloud, and OmniStudio

Together, these features help organizations manage revenue with greater speed, consistency, and automation.

3. RLM vs Industries CPQ

RLM and Industries CPQ share several overlapping capabilities, but they target different business needs.

RLM is designed for modern, unified revenue management, focusing on standardization, native platform integration, and end-to-end monetization.

Industries CPQ is built for highly complex, industry-specific scenarios, such as telecom or utilities, using deep industry data models and MACD-driven order lifecycles.

For example:

RLM uses Product Catalog Management and Salesforce Pricing, while Industries CPQ relies on Enterprise Product Catalog (EPC).

RLM supports Amend, Renew, Cancel (ARC), whereas Industries CPQ handles Move, Add, Change, Disconnect (MACD).

Both support CPQ, but Industries CPQ offers deeper industry customization, while RLM prioritizes simplicity and platform consistency.

Final Thoughts

Revenue Lifecycle Management marks a strategic shift for Salesforce bringing revenue operations closer to the core platform. While Industries CPQ remains the best choice for complex, industry-driven models, RLM is ideal for organizations looking for a modern, scalable, and native revenue solution.

Industries CPQ vs Salesforce CPQ: Understanding the Difference

Industries CPQ vs Salesforce CPQ: Key Differences Explained

Salesforce provides two CPQ solutions Industries CPQ (formerly Vlocity CPQ) and Salesforce CPQ (formerly SteelBrick) each built for distinct business needs. While both support pricing and quoting, they serve very different sales complexity levels.

Origins at a Glance

Industries CPQ (Vlocity CPQ) was founded in 2014 to deliver industry-specific CRM solutions for highly complex sectors like telecom, insurance, healthcare, energy, and utilities. Salesforce acquired Vlocity in 2020 and rebranded it as Salesforce Industries, making it a core part of its vertical cloud strategy.

Salesforce CPQ (SteelBrick) was founded in 2009 with a focus on simplifying the CPQ process for sales teams. Salesforce acquired it in 2015/2016 and positioned it as a Sales Cloud add-on for faster and more accurate quoting.

Core Focus

Industries CPQ is designed for complex, industry-driven sales models. It supports deep product hierarchies, complex rules, long order lifecycles, and industry-specific data models. It is part of an end-to-end industry solution, not just a quoting tool.

Salesforce CPQ targets standard sales organizations, helping teams create quotes faster with accurate pricing, discounts, and configurations. It excels in improving sales efficiency for typical B2B use cases.

Functional Scope

Industries CPQ includes advanced capabilities such as MACDs, Order Management, Contract Lifecycle Management, OmniStudio, document generation, and rich APIs—making it ideal for regulated and complex industries.

Salesforce CPQ focuses on core CPQ features like product configuration, contract amendments, document generation, and basic APIs, prioritizing speed and simplicity.

Integration Approach

Industries CPQ is deeply integrated into Salesforce as part of Salesforce Industries, providing vertical-specific data models and guided processes.

Salesforce CPQ integrates seamlessly with Sales Cloud, working with standard Salesforce objects without significantly altering the core data model.

Choosing the Right CPQ

Choose Industries CPQ if you deal with complex products, long lifecycles, and industry-specific processes.

Choose Salesforce CPQ if your goal is quick implementation, standard quoting, and improved sales productivity.

Architecting Salesforce Integrations Interview Questions

Q1. Scenario: Your organization needs to integrate Salesforce with multiple external systems (ERP, billing, and analytics). As an architect, how do you decide which Salesforce integration option to use?

Answer:

I start by evaluating data volume, real-time vs async needs, ownership of data, and latency tolerance.

For real-time transactional access, REST APIs are preferred.

For large data loads (>2000 records), Bulk API 2.0 is ideal due to async processing.

If data must not be stored in Salesforce and must always be fresh, I use Salesforce Connect with External Objects.

For event-driven integration, Streaming APIs (Platform Events / CDC) are chosen.

This architectural decision ensures scalability, performance, and data consistency.

Q2. Scenario: A business wants near real-time updates in an external system whenever a Salesforce record changes. What integration pattern would you suggest?

Answer:

I would recommend an event-driven architecture using Streaming APIs, preferably Change Data Capture (CDC). CDC automatically publishes events when records change, avoiding custom triggers. This approach improves scalability.

Q3. Scenario: Multiple external applications need to access Salesforce APIs securely. How do you design the authentication model?

Answer:

I design a Connected App per trust boundary, not per application blindly. Each connected app has:

- Least-privilege OAuth scopes

- Defined OAuth flow (JWT or Web Server or as per scenario details available)

- IP relaxation policies only if required

This isolates security risks and allows fine-grained access control.

Q4. Scenario: Why would you avoid sharing a single Connected App across multiple integrations?

Answer:

Sharing a connected app increases risk if credentials are compromised. From an architectural standpoint, separate connected apps allow:

- Independent rotation of secrets

- Flow-specific security settings

📘[Explore the course]

https://www.sfdc-lightning.com/p/integration-topics.html

Add Apex Merge Fields to a Field Generation Prompt Template

Salesforce allows you to make AI responses smarter by using Apex merge fields in a Field Generation Prompt Template. This helps Einstein Generative AI use real-time data from your org when creating AI-generated content.

Let’s understand this with a simple example.

Business Use Case

Imagine your sales team wants to see a summary of all open cases for a customer before making a sales call. Instead of checking multiple records, Salesforce AI can automatically generate this summary and store it on the Account record.

To do this, we use:

A Field Generation Prompt Template

An Apex class to fetch open case data

Using Apex with Prompt Templates

An Apex class can be written to:

Accept an Account as input

Find all open Cases related to that Account

Prepare the case details as text

Send this text to the AI model as part of the prompt

This Apex class is exposed to Prompt Builder using the @InvocableMethod annotation. Once exposed, it can be selected as a resource inside a prompt template.

public class OpenCasesPrompt {
    @InvocableMethod(label='Open Cases'
        description='Find Cases for an Account'
        CapabilityType='PromptTemplateType://einstein_gpt__fieldCompletion')
    public static List<Response> getCasesPrompt(List<Request> requests) {
        // Validate the expected number of requests as an input
        if (requests.size() != 1)
          throw new ListException('The requests list must contain one entry only');
        Account a = requests[0].RelatedEntity;
        ID searchAcctId = a.Id;
        List<Case> cases =
            [SELECT Id, Subject, Description
             FROM Case
             WHERE AccountId = :searchAcctId AND Status != 'Closed'
             WITH USER_MODE];
        string responseData = null;
        if(cases.isEmpty()) {
            responseData = 'There are no open cases.';
        } else {
            for(Case c : cases) {  
                responseData =
                   (responseData != null) ? responseData + '\n' : '';           
                responseData += String.format('Case details: {0}, {1}.',
                    new List<Object>{c.Subject, c.Description});
            }
        }
       
        List<Response> responses = new List<Response>();
        Response res = new Response();
        res.Prompt = responseData;
        responses.add(res);
        return responses;
    }
    
    public class Request {
        @InvocableVariable(required=true)
        public Account RelatedEntity;
    }
    
    public class Response {
        @InvocableVariable
        public String Prompt;
    }
}

Key parts explained:

@InvocableMethod

Required to expose the method to Prompt Builder

label

This is the name you see in the UI (“Open Cases”)

description

Explains what the method does

CapabilityType

Tells Salesforce this method is used for Field Generation Prompt Templates

Method signature rules:

Must be static

Must accept a List of Request

Must return a List of Response

Request Inner Class (Input)

public class Request {

@InvocableVariable(required=true)

public Account RelatedEntity;

}

What this does:

Defines what input the method receives

In this case, it receives an Account record

Important points:

@InvocableVariable exposes the variable to Salesforce

RelatedEntity is automatically populated when the prompt runs

This allows the method to know which Account to process

Response Inner Class (Output)

public class Response {

@InvocableVariable

public String Prompt;

}

What this does:

Defines what data is sent back to Prompt Builder

The Prompt field holds the final text

Einstein uses this text as input context for the LLM

Creating the Field Generation Prompt Template

After the Apex class is ready, follow these steps in Prompt Builder:

Create a custom field on the Account object

Example: Open Case Summary (Text Area – Long)

Open Prompt Builder and create a new prompt template

Prompt Template Type: Field Generation

Object: Account

Object Field: Open Case Summary

Paste a sample “Summarize open cases” prompt into the workspace

Insert resources into the prompt:

Add Account ID as a merge field

Add the Apex resource that fetches open case details

The Apex class provides live case data, and Einstein Generative AI uses it to generate a clear summary.

Final Result

When the prompt runs:

Salesforce calls the Apex class

Open case details are added to the prompt

Einstein Generative AI creates a summary

The summary is saved in the Account field

This gives users up-to-date, AI-generated insights without manual effort.

Einstein Trust Layer and Its Features

As organizations begin using generative AI, trust becomes the most important factor. At Salesforce, trust is the number one value. Einstein Generative AI is built to help businesses innovate while keeping their data secure and their AI responses reliable and safe.

Your Data Stays Secure

With Einstein Generative AI, your data remains private and protected. Salesforce has strong agreements with large language model providers like OpenAI. These agreements ensure that customer data is not stored, reused, or trained on by the AI models. This allows organizations to take advantage of generative AI without worrying about data security.

Einstein Trust Layer: Designed for Trust

As Salesforce adds more generative AI features, keeping customer data safe becomes very important. The Einstein Trust Layer is built to protect data privacy, improve AI accuracy, and make sure AI is used in a responsible way across Salesforce.

The Einstein Trust Layer is a set of features, rules, and processes that work in the background whenever Einstein Generative AI is used.

How Data Flows Through the Einstein Trust Layer?

When AI is used in Salesforce, data follows a secure path:

A prompt is sent from a CRM app (like Sales or Service Cloud).

The prompt passes through the Einstein Trust Layer.

The prompt is sent to the LLM (Large Language Model).

The LLM creates a response.

The response comes back through the Einstein Trust Layer.

The final response is shown in the CRM app.

This full process helps keep data secure at every step.

Prompt Journey

To get an AI response, Salesforce sends a prompt to the LLM.

Prompts can be created using Prompt Builder and can be called from Flow or Apex.

Before the prompt reaches the LLM, the Einstein Trust Layer checks and prepares the data.

The Einstein Trust Layer Features.

Secure Data Retrieval and Grounding

For better and more accurate answers, the AI needs context from Salesforce data. This is called grounding.

Grounding means adding CRM data to the prompt, such as:

Record fields

Related lists

Flow data

Apex data

Security is always respected:

Only data that the current user has access to is used

Salesforce role and field-level security rules are followed

Grounding happens at run time, based on user permissions

This ensures users see only what they are allowed to see.

Data Masking for the LLM

To protect sensitive information, the Einstein Trust Layer uses data masking.

Sensitive data is detected in two ways:

Pattern-based detection (like emails, phone numbers, names)

Field-based detection using Salesforce data classification and Shield encryption

Once detected:

Sensitive data is replaced with placeholder text

The real data is never sent to the LLM

After the response is generated, the data is safely restored

This prevents private data from being exposed to external AI models.

Prompt Defense

To help decrease the likelihood of the LLM generating something unintended or harmful, Prompt Builder and Prompt Template Connect API use system policies. 

These policies:

Guide the LLM on how to behave

Prevent harmful or unwanted responses

Protect against prompt injection and jailbreaking attacks

Stop the AI from answering questions it does not have data for