From Salesforce CDP to Salesforce Data Cloud: How Salesforce Powers Personalization?

 

Salesforce has rebranded its CDP product several times. While some changes were due to the names not resonating well, many name changes signified significant product advancements.

1. Customer 360 Audiences: Salesforce's initial CDP offering, launched in 2020, marks its entry into the CDP market.

2. Salesforce CDP: In 2021, the product was renamed to align with the growing CDP market terminology.

3. Marketing Cloud Customer Data Platform: In 2022, Salesforce simplified its marketing product names and rebranded its CDP accordingly.

4. Salesforce Genie: Later in 2022, during Dreamforce, Salesforce introduced Genie, highlighting an expansion in use cases beyond marketing to include sales, customer service, and more.

5. Data Cloud: In 2023, the name Genie was replaced with Data Cloud, reflecting its broader capabilities and role in powering Salesforce's AI innovation.

Data Cloud and Salesforce CDP are related products, designed to work in tandem. Initially focused on marketing, they evolved to encompass broader use cases. Since both products still coexist, let's explore how they differ in delivering personalized customer service.

Don't forget to check out: 3 Ways Salesforce Data Cloud is Transforming Healthcare

What is Data Cloud in Salesforce?

Salesforce Data Cloud is a platform that integrates and manages customer data from multiple sources, providing a unified view of customers to enhance personalization and insights across marketing, sales, and service activities. In essence, this platform is the basis for a data-driven, customer-centric approach, allowing your organization to make more informed decisions.

It is essentially a data management solution that focuses on:

• Data integration from multiple sources, including CRM systems, external databases, and third-party applications
• Data storage for large datasets
• Data processing tools for processing and analyzing data, including engagement and instore transaction data, to generate insights
• Data security to ensure that data is securely stored and managed in compliance with regulatory standards insights
• Data security to ensure that data is securely stored and managed in compliance with regulatory standards

Obviously, Data Cloud seamlessly integrates with existing Salesforce objects, reducing the work required to map systems and maintain data quality. With Salesforce Industries, organizations can tap into data models and processes designed for their industry needs. This includes accommodating additional objects, regulatory compliance, and diverse applications within verticals.

Another advantage is Prompt Builder. With harmonized info across the Salesforce ecosystem, users can create prompts with generative AI for various use cases, such as generating emails and fields. To ensure privacy when handling sensitive data, Einstein Trust Layer, built-in secure AI architecture, establishes a 'trust boundary' with features like zero data retention and feedback storage, ensuring data security and privacy.

However, as with any software, Data Cloud has its limitations adopters need to be aware of:

• The segmentation tool can be tricky to navigate
• The system lacks the ability to ingest and activate real-time data
• Updates are constrained to a once-per-hour schedule
• Cannot be used independently from the Salesforce core
• The activation process takes place on a distinct, separate platform
• Content creation happens within a different user interface or platform altogether
• The built-in analytics features are somewhat limited in their capabilities

Most Salesforce CDP use cases can be roughly summarized in three points:

• Personalization

Leverage unified customer profiles to deliver tailored offers. When a customer visits your website, browses a product, and leaves, a Customer Data Platform allows you to connect their actions to create personalized outreach via email or push notification.

• Suppression

You can use data to optimize marketing spend. A CDP that unifies marketing and purchase history lets you suppress customers who have already purchased. Redirect those marketing resources towards acquiring new customers, maximizing the impact of your addressable spending.

• Insights

Data becomes cross-functional. Imagine an outdoor retailer where call center reps can access a customer's marketing interactions, purchase history, and website browsing data. This holistic view transforms a $15/hour rep into a $100,000/year salesperson, delivering hyper-personalized service.

Data Cloud for Personalization: Main Use Cases

You can use Salesforce Data Cloud without Salesforce Customer Data Platform (CDP) and vice versa, but their combined use enhances functionality. Salesforce Data Cloud provides the infrastructure for managing, processing, and analyzing large volumes of data from multiple sources.

Salesforce Customer Data Platform can operate without the underlying Data Cloud infrastructure. In this case, CDP would still unify customer data from various touchpoints to create comprehensive customer profiles and enable personalized marketing strategies.

Let's explore a few scenarios relevant to most businesses.

Target Use Cases

Salesforce CDP is primarily for marketing and focuses on audience segmentation and campaign activation across multiple channels. It is ideal for marketers who need to create detailed audience segments and launch personalized campaigns using tools like Einstein optimization.

Broader in scope, Data Cloud spans the entire Salesforce ecosystem, including sales, service, marketing, commerce, and more. It integrates various information streams to drive actions, interactions, and insights across the Customer 360 platform.

Enhanced Segmentation & Segment Activation

Salesforce CDP allows marketers to build and activate complex audience segments. It supports non-technical users with features like the Audience Builder, which suggests values and allows for easy replication and sharing of segments. These segments can be seamlessly integrated into Journey Builder for campaign execution.

Salesforce Data Cloud, in addition, provides extensive activation use cases beyond marketing: it supports information absorption, Flow automation triggers, and Einstein Next Best Action. It can access info from multiple sources using native connectors and third-party plugins, enabling segment activation across websites, emails, chat, paid media, and alerts via Slack.

Underlying Architecture

Directly linked to the Marketing Cloud product suite, Salesforce CDP focuses on integrating customer data for marketing purposes. However, custom integrations may be required to connect with other Salesforce tools, such as Marketing Cloud components.

Data Cloud works with all Salesforce Customer 360 products, providing out-of-the-box connectivity. It ensures data flows efficiently across the entire Salesforce platform.

AI Capabilities

Salesforce CDP primarily utilizes Salesforce's built-in AI capabilities, while Data Cloud offers open data capabilities. It allows organizations to integrate external AI platforms with Salesforce's Einstein AI. This extensibility supports advanced AI models and predictions.

Analytics and Data Migration Integrations

Salesforce CDP integrates with MuleSoft to connect external apps and sources and Tableau to visualize and explore customer data. It enriches profiles with calculated insights, such as customer lifetime value and engagement scores.

Data Cloud builds on these integrations with enhanced functionality, supporting more visualization and actionable insights across the entire Salesforce platform.