Abstract: Gone are the days when companies used to sell mere products and services to their consumers. Today, the industry is after creating meaningful, personalized, and emotional experiences for consumers. Modern organizations want to meet their customers where they are and how they are. To accomplish this, it has become imperative for the company to build a unified 360-degree profile for every customer so that they can construct truly memorable experiences. At the same time, organizations must ensure that customer data is protected at every cost and is used only for the consented purposes. This article explores the transformative capabilities of Salesforce Data Cloud, which integrates and unifies data from diverse sources to create detailed customer profiles. Leveraging the Customer 360 data model, Salesforce Data Cloud ensures accurate and harmonized data while supporting advanced features like identity resolution, segmentation, and real-time data activation. These capabilities enable businesses to deliver highly personalized customer experiences, automate workflows, and comply with data protection regulations such as GDPR, CCPA, PIPA, and PIPEDA. By addressing challenges such as data integration and consent management, the article highlights the platform's potential to revolutionize customer insights. Additionally, future trends, including the integration of artificial intelligence and real-time analytics, are discussed as critical advancements in optimizing customer profiling and engagement. Through practical applications and insights, this article underscores Salesforce Data Cloud's pivotal role in creating unified, actionable customer profiles that drive strategic growth.
Keywords: Salesforce Data Cloud, Customer 360 data model, customer profiles, data unification, data integration, identity resolution, data harmonization, segmentation, real-time data activation, personalized customer experiences, data-driven decision-making, AI-powered analytics, Einstein Copilot, consent management, GDPR compliance, CCPA compliance, PIPA Compliance, PIPEDA compliance, marketing automation, omni-channel strategies, data collection, behavioral insights, data accuracy, customer engagement, CRM systems, data privacy, predictive analytics, data visualization.
Building Unified Customer Profiles with Salesforce Data Cloud is a transformative approach that leverages the robust capabilities of the platform to unify and integrate data from multiple sources, creating detailed and cohesive customer profiles. At the core of this process is Salesforce's proprietary Customer 360 data model, which is instrumental in combining data from disparate heterogeneous sources into a unified view of each customer. This integration enables organizations to not only consolidate demographic, psychographic, and behavioral information but also to provide a strategic advantage in understanding and anticipating customer needs and driving their personalized experiences.
Salesforce Data Cloud's advanced data matching and reconciliation engine ensures that customer profiles from multiple sources are unified most accurately. This harmonization allows businesses to access the most relevant and consolidated version of their customers' data without tampering with the raw data. As a result, companies can use these comprehensive profiles to tailor marketing strategies, personalize customer interactions, and enhance overall engagement by better understanding customer behaviors and preferences[4].
Beyond just data ingestion from a gamut of sources, Data Cloud offers some really advanced tools for identity resolution, segmentation, calculating key insights, data transforms, data graphs, data actions, query editor, and data activation, enabling businesses to build targeted segments (rapid, standard, and waterfall) and push those to the activation targets like Marketing and CRM platforms for enhanced data-driven decision-making and marketing communications. These capabilities are crucial for driving workflows, automation, and AI-powered analytics, including leveraging low-code tools such as Salesforce Flow and generative AI solutions like Einstein Copilot[5]. Most importantly, the platform's consent management features ensure compliance with data privacy and protection regulations such as CAN-SPAM, GDPR, PIPA, PIPEDA, CCPA, etc., maintaining customer trust by honoring their communication preferences.
Considering all the power and capabilities that are offered, a Data Cloud implementation is mostly complex and entails a significant amount of planning. Depending upon the number of data sources and their complexity, required due diligence on data analysis and data modeling must be exercised. Organizations must also spend enough time putting together their consent management policies so that the data cloud can be configured per the business requirements. As businesses continue to seek innovative ways to understand and engage customers, the integration of AI and real-time data processing in Salesforce Data Cloud is poised to enhance further the creation and utilization of comprehensive customer profiles[5].
Salesforce Data Cloud
Salesforce Data Cloud is a powerful, purpose-built platform designed to unify and transform disconnected data at scale into a single, trusted customer view. Integrating data from diverse, heterogeneous data sources, including CRM systems, marketing platforms, service platforms, data warehouses, websites, ERP systems, and accounting software, facilitates the creation of comprehensive customer profiles with unparalleled accuracy and accessibility. At its core is the canonical Customer 360 data model, which harmonizes and unifies data to eliminate duplicates and inconsistencies while preserving access to the original data in its native format[1].
Data Cloud must not be misconstrued as a CRM, MDM, or a customer-facing system. It sits behind the scenes, constantly ingesting data from all the transactional systems (ERP, websites, orders, CRM, marketing, etc.) to construct a point-in-time snapshot of an individual or customer.
The unification process encompasses diverse datasets, such as telemetry data, web engagement data, and more, providing a holistic and actionable customer view. This enables sales, service, and marketing teams to deliver personalized customer experiences, trigger data-driven workflows, and safely implement AI across all Salesforce applications.
Data Cloud excels in key capabilities like identity resolution, segmentation, audience building, and data activation. Businesses can build precise segments, enrich data, and seamlessly feed insights back into consuming systems, such as Salesforce apps, to optimize operations and customer interactions[3]. This functionality supports workflows, automations, AI-driven analytics, and low-code tools like Flow and generative AI solutions, including Einstein Copilot and Prompt Builder[5]
Consent management is another salient feature that is integrated into Data Cloud, ensuring compliance with regulations like GDPR, CCPA, PIPA, PIPEDA, etc. Embedding customer permissions and preferences into the data model safeguards trust and adheres to global data privacy standards and immediate action on data subject access requests (DSAR).
By turning disconnected data into a cohesive and accessible model, Salesforce Data Cloud empowers organizations to understand customer behaviors, build effective strategies, and drive personalized, data-informed actions across every business function.
Why a Unified Customer Profile Is Imperative
Data Siloes have never ceased to exist, and they never will. As organizations explore more and more creative ways to engage with their customers, it will produce new types of data sources. At the same time, there has never been a stronger need to have a unified view of customers from the customer success teams. Spray and Pray marketing tactics do not work anymore. Your customers expect personalized, curated experiences from great brands. Building comprehensive customer profiles is the first crucial step in developing a deep and thorough understanding of your customer base. A comprehensive customer profile serves as a strategic compass that guides decision-making by providing a detailed and well-rounded snapshot of demographic, psychographic, and behavioral information about your customers[4]. This information is gathered through meticulous data collection and harmonization, enabling businesses to tailor their marketing efforts more effectively.
Salesforce Data Cloud plays a pivotal role in this process by ingesting data from all possible data sources, such as websites, commerce portals, internal systems, data warehouses, excel sheets, POS terminals, external data lakes, emails, images, or PDFs to create a unified customer profile[]]. This unified profile is not just a collection of data but a dynamic tool that allows businesses to monitor customer interactions across different platforms in real time. For instance, it helps answer questions like what products a customer has browsed online, which ones they added to the cart, and what offers or promotions they looked at and pop up some real-time messages on the phone or email driving their behavior. By doing so, businesses can better understand what their customers need today and anticipate future needs. Moreover, real-time dashboards, constructed using tools like Salesforce Tableau, allow businesses to track key performance indicators (KPIs), customer behavior, and campaign performance, providing a continuous and comprehensive overview of customer interactions[7].
This real-time insight is essential for serving your customers at the right time and at the right place with the right piece of information. By leveraging this powerful combination of unified data and real-time insights, businesses can trigger real-time messages (e.g., SMS promotions, push messages on mobile apps, etc.). They can also do trend analysis of purchasing behavior across brands, promotions, time periods, and other dimensions.
Salesforce Data Cloud Funnel
The diagram below depicts how Salesforce Data Cloud processes data coming from multiple sources through its different stages and ultimately producing actionable activation targets for Sales, Service, Marketing, Customer Ops, and Customer success teams.
The process of building a robust customer data platform involves five key steps:
- Ingest, where data from diverse sources like CRM, marketing platforms, and external data lakes is collected through batch or streaming pipelines.
- Data Transform, where you can create custom rules to transform your data based on some unique business requirements (e.g., handling multiple email addresses)
- Harmonize, which transforms and governs the data to ensure accuracy and consistency.
- Unify, where disparate datasets are reconciled into a single, trusted customer view.
- Analyze & Predict stage, which leverages AI-driven insights to uncover patterns and behaviors, enabling precise segmentation and predictions.
- Act stage where actionable insights power workflows, automations, and customer engagements, driving business value across sales, service, and marketing teams.
Data Ingestion
Data Ingestion is the first and most fundamental step of building comprehensive customer profiles within Salesforce Data Cloud. This platform is designed to aggregate data from a variety of sources, such as internal applications, CRM systems, marketing platforms, service platforms, data warehouses, websites, ERP systems, Accounting software, emails, images, and PDFs, thereby creating a unified customer profile[5]. The process involves ingesting demographic, psychographic, and behavioral information, which provides insights into customers' preferences, needs, and behaviors[4]. Each data source is ingested in the form of Data Streams and is stored in Data Lake objects (DLOs). This is where the data stays in its raw ingested form.
To support data ingestion, Salesforce Data Cloud has a vast library of connectors (e.g., Salesforce CRM, Marketing Cloud, Snowflake, AWS, REST API, Commerce Cloud, File Upload, and many more), which allows for real-time or batch data import from a gamut of business applications or systems. In this stage, you also specify the Data Space in which you want to house this data source. This flexible approach enables businesses to gather diverse data types for different business units or brands and allows them to be in their own private spaces while still leveraging the environment.
Salesforce Data Cloud's capabilities extend from data collection and transformation to identity resolution and segmentation, which are crucial for effective audience building and data activation[3]. Data Cloud utilizes its proprietary Customer 360 canonical data model as its foundation to store the unified customer profile data, enabling the creation of a cohesive view of customer data across different channels and systems. The overall process ensures that once the data is ingested, it is harmonized and unified, resulting in an actionable data set that creates segments and custom data views for various business purposes.
Data Transform
This stage allows the addition of custom data transformations (e.g., filters, joining, formulas, merging, custom fields, etc.) to the ingested data to meet some unique business requirements and to make it compatible with the data model. A common example here could be handling multiple email addresses coming for a single customer in different fields (e.g., email 1, email 2, email 3). You can use data transform to transform these three fields into three separate rows so you can map them accurately with the Contact Point Email DMO (Data Model Object) in the Customer 360 canonical model. A best practice here is to create custom interim DLOs from the ingested Data Streams. This gives full control and flexibility on the ingested data before it can be mapped with the DMOs.
Data Harmonization
Although the data has been ingested into Data Cloud, it is not consumable yet. It's sitting siloed in individual DLOs. Data Harmonization is the step where all these data streams from multiple sources start to come together (after they are ingested and transformed). This is where you establish relationships, identify field-level data, and determine the DMOs to which each DLO will be mapped. A best practice here is first to put together a detailed data dictionary that describes every table and field of all the data sources. You must consider how each of the data sources will associate with the Individual Object. For example, if you are bringing inpatient visits to understand the treatment discipline better, ensure that its attributes are mapped to the Individual object. That is the object that ultimately turns into a Unified Individual and represents a single customer profile. If you use one of the available standard data kits (e.g., Sales Cloud or Service Cloud), then most of the mapping is done automatically except for any custom fields. In addition to fields, you can create custom DMOs if none of the existing objects fit the business needs. Another crucial step in the system design is categorizing the DSOs and DLOs as Profile, Engagement, or another type. Accurate category assignment is critical; otherwise, it may result in incorrect mapping, improper DMO selection, and various data integrity issues. A DMO inherits its category from the first DLO it is mapped to. While this complex data mapping creates a data labyrinth behind the scenes for business applications and users, it presents a clean and meaningful 360-degree view of your customers.
Unify the Profiles
Harmonization is appealing, but its true magic only materializes once the profiles are unified. Unified profiles are highly coveted by organizations because they provide actionable insights into their customers. After all the disparate data sources are meticulously mapped and harmonized into the standard Customer 360 model of Data Cloud, they are ready to be processed by Salesforce Data Cloud's unification engine.
Up to this point, multiple identities still exist within the mapped data. For instance, if the same customer is sourced from both your CRM and POS systems, they will appear as two separate profiles. To resolve this, identity resolution rules must be defined. These rules enable Data Cloud to identify matching profiles, reconcile them, and place the consolidated data into the Unified Individual object.
Identity resolution rulesets comprise two key components:
- Matching rules specify how to compare field values to identify matching records.
- Reconciliation rules determine how to select and retain the most accurate values from multiple data sources for each field.
Once these rules are executed, the data is organized into new objects: Unified Profile, Unified Contact Point, and Unified Link Objects. This process is facilitated by the creation of link tables that act as connectors between source objects and unified profile objects.
A critical requirement for ensuring that identity resolution functions as expected is the accurate mapping of data during the Harmonize stage. After executing these rules, you can review the Consolidation Rate in the Identity Resolution summary to assess its effectiveness. To further improve the consolidation rate, additional matching rules can be introduced.
This comprehensive process transforms a complex data landscape into a unified, actionable view, empowering organizations to make more informed decisions and gain deeper customer insights.
Act on the Unified Profiles
Harmonized unified customer profiles play a pivotal role in enhancing marketing efforts by enabling businesses to gain a deeper understanding of their customers' behaviors and needs. These profiles serve as strategic guides that inform decision-making processes, allowing for more effective marketing campaigns and personalized customer experiences[4]. Salesforce Data Cloud enables the delivery of these unified profiles across a suite of customer-facing systems like Marketing Cloud for running personalized campaigns, Service Cloud for service agents, Agent Force for AI chatbots, Sales Cloud for sales development reps, and many more. This approach results in more accurate and actionable insights, facilitating the delivery of targeted marketing strategies that resonate with individual customers.
Salesforce Data Cloud provides businesses with robust tools to develop targeted segments for various needs, such as a continuously refreshed segment of all the customers who have placed their first orders. Another example could be a segment of all the lapsed customers who have not had any transactions in the last 18 months. Another could be a segment of patients who have come for vaccines but not for chronic checks. This list will be endless, depending on the business. By leveraging detailed demographic, psychographic, and behavioral information, businesses can tailor their customer success efforts to meet the specific preferences and needs of different customer segments, ultimately enhancing customer satisfaction and loyalty[4]. You can create different types of segments, including Standard, Real-Time, Waterfall, etc. Once you have created Segments, now it's time to push them to business-facing applications like Sales apps, Serviceappsp, Marketing Cloud, Google Ads, LinkedIn, Meta Audiences, and many more. These applications are called Activation Targets, where the custom segments come into action. Marketing Cloud is a fantastic use case here. Data Cloud Segments can be fed directly to Marketing Cloud journeys in the form of their entry source data extensions.
In addition to enhancing marketing strategies, customer profiles also aid in ensuring data accuracy and consistency. For example, consultants can use these profiles to verify and save critical information, such as address details from customer orders, thereby ensuring that the unified profile remains up-to-date and reliable. This attention to detail in data management supports the creation of personalized marketing experiences that reflect the most current customer information, further contributing to the effectiveness of marketing efforts[4].
Data Analysis
So far, it has been all about ingesting, transforming, unifying, and activating the data. It's all behind-the-scenes stuff—configuration, mapping, modeling, resolving, etc. What about visualizing, pattern recognition, predictive insights, hidden trends, etc.? Data Analysis is all about that. Data Cloud seamlessly integrates with powerful data analysis tools where you can surface all kinds of intelligent data insights. Data Cloud provides standard reporting capabilities on DMOs (e.g., Unified Individual, Contact Point Objects) for any basic business needs. Salesforce Einstein adds an intelligent layer to this data analysis by automating the creation of predictive models. These models can provide insights into customer behavior, such as predicting customer churn or recommending products, which enhances the depth and accuracy of the analysis[7].
Tableau and CRM Analytics can take this analysis to the next level, where you can build actionable reports, dashboards, and contextual insights, create interactive views on your DLOs and DMOs, and calculate insights.
Salesforce Data Cloud unlocks the power of generative AI, which, when combined with the company's data, can deliver secure and relevant outcomes. This integration is enhanced further with the option to incorporate external predictive models, providing businesses with enriched insights and more effective workflows[9]. Through these advanced data analysis processes, Salesforce Data Cloud enables businesses to not only understand their customers better but also to personalize their engagement and optimize their marketing strategies.
Why Salesforce Data Cloud?
When envisioning a system or platform capable of integrating all your data sources, traditional solutions like data lakes, data warehouses, MDM solutions, and ETL frameworks often come to mind. These require you to design the entire architecture, data model, business rules, and every intricate detail. Historically, this approach has been largely reporting-oriented, focusing on aggregated KPIs, trend analysis, and similar tasks. Additionally, developing custom segments and audiences for marketing purposes often involves writing extensive and complex SQL queries.
Salesforce Data Cloud transcends this traditional mindset by offering a robust platform that addresses these challenges far more efficiently. Its Customer 360 Canonical Data Model represents the culmination of Salesforce's years of experience with customers across various industries. The platform's customizability and extensibility are limited only by the user's imagination.
Unlike traditional systems, Salesforce Data Cloud eliminates the need for users to set up data models requiring advanced data modeling skills. Its real-time activation targets enable seamless data flow into other business applications, revolutionizing how businesses operate.
This platform has truly empowered Customer Success teams—including Sales, Service, and Marketing—by providing direct access to unified customer profiles. This access enhances their ability to deliver personalized, targeted experiences, transforming customer engagement and satisfaction.
One of the primary advantages is the platform's capability to collect, transform, and harmonize data from multiple sources and ultimately integrate it into a unified customer profile[3]. The segmentation capability is designed with an intuitive drag-and-drop interface, which takes away all the headache of writing SQLs
Additionally, Salesforce Data Cloud offers seamless integration with other Salesforce products, such as Marketing Cloud, Sales Cloud, and Service Cloud. This integration empowers marketing teams to execute highly personalized campaigns, sales teams to leverage a 360-degree customer view for informed sales strategies, and service teams to enhance service quality through comprehensive customer profiles[8]. The ability to provide real-time dashboards and visualizations further supports businesses in tracking key performance indicators and customer behavior, enabling more agile and responsive decision-making processes[7].
Furthermore, the open, extensible architecture of Data Cloud allows for zero-copy integrations with leading data platforms like Snowflake, AWS, and Databricks, offering unparalleled flexibility and control over data management[5]. This architecture ensures that data can be seamlessly connected and utilized without the need for cumbersome data movement, making it easier for organizations to manage and activate their data as needed.
Key Considerations
Although Data Cloud promises to bring the dream of having pristine, unified customer profiles to life, navigating through data intricacies has never been an easy path. The more data sources are there, the trickier the process gets. Hence, a Data Cloud implementation project does entail overcoming data challenges and keeping key considerations in mind. One of the primary challenges is mapping data elements from diverse data sources into a structured canonical data model that consists of several ever-growing predefined models for a multitude of industry use cases. Data Cloud can aggregate information from various internal applications, external data lakes, and unstructured sources like emails and PDFs, creating a unified customer profile[5]. However, achieving this integration requires meticulous data mapping and modeling exercises to ensure data accurate profile construction of an individual customer, especially when dealing with complex systems like AWS, Snowflake, or Databricks.
Another significant consideration is the management of data accuracy and consistency. Data Quality Management practices must be adopted to ensure that the collected data adheres to some basic data quality protocols. The process of harmonizing data involves applying identity resolution rules to unify profile-type data and using reconciliation rules to resolve conflicts[3]. This process ensures that organizations maintain a 360-degree view of customer profiles, which serves as a single source of truth[3]. However, organizations must remain vigilant in maintaining data integrity to prevent discrepancies that could lead to inaccurate insights. Customer profiling is generally vulnerable to assumptions. Modern AI-based tools should be used to uncover trends and patterns in the data.
Additionally, consent management is a critical consideration when building customer profiles. Organizations must comply with data protection regulations such as CAN-SPAM, GDPR, PIPA, PIPEDA, and CCPA by including consent status in their data segmentation strategies. This ensures that customer data is used in line with their given permissions, safeguarding privacy and maintaining regulatory compliance. As a Data Cloud best practice, exclusion segments can be built to filter out opted-out consumers.
Furthermore, organizations must be prepared to address the limitations of Data Cloud. While it offers capabilities for creating calculated insights and optimizing identity resolution, it does not inherently provide data governance or disaster recovery features. These aspects are typically managed by other Salesforce solutions or external platforms [12]. As a result, organizations must ensure that complementary systems are in place to support these critical functions.
Finally, leveraging the full potential of Salesforce Data Cloud involves not only integrating and managing data but also activating it effectively. The activation process allows organizations to build segments and choose targets for enriched data, thereby transforming fragmented data into a comprehensive picture with added insights[3]. Ensuring that these processes are well-coordinated can significantly enhance the value derived from Data Cloud.
Case Studies
Salesforce Data Cloud has been instrumental in transforming how businesses build comprehensive customer profiles and leverage these insights for effective marketing strategies.
A good example of this is a leading chain of Healthcare clinics that successfully consolidated information from multiple data systems (EMR, Marketing, CRM) to create a unified patient profile by consuming millions of records about patients' clinical encounters and medical procedures. This approach enabled them to map out the patient journey and deliver personalized experiences and targeted marketing campaigns by utilizing details of demographic, psychographic, and behavioral information gathered through advanced data collection and analysis techniques.
Another noteworthy case is a tech firm that employed the open, extensible architecture of Data Cloud to integrate seamlessly with platforms like Snowflake and Databricks. This integration facilitated the management of their data without the need for cumbersome data transfers, thus providing unparalleled flexibility and control[5]. The firm's ability to easily bring in and send out data as needed allowed them to maintain an agile data strategy and fostered improved decision-making processes.
Furthermore, Formula 1 delights fans with personalization based on location, content preferences, and favorite driver. They've created near real-time fan journeys with one-of-a-kind experiences and exclusive offers, providing meaningful interactions to turn new fans into loyal ones and fuel sustained growth worldwide. Results include 88% fan satisfaction, 86% first contact resolution, and 99.6% email delivery rate[5]
These case studies highlight the profound impact of Salesforce Data Cloud on building comprehensive customer profiles and the subsequent benefits in terms of personalization and strategic marketing initiatives.
Future Trends
As the digital landscape continues to evolve, the future of building comprehensive customer profiles with Salesforce Data Cloud is poised to embrace several transformative trends. One significant trend is the increasing importance of real-time data processing and personalization. With consumers demanding immediate responses, businesses are likely to leverage Data Cloud's capabilities to process vast amounts of data from diverse sources almost instantaneously, enabling the creation of unified customer profiles that reflect the most current information[5].
The integration of artificial intelligence (AI) will also play a pivotal role in the future of customer profiling. Tools like Einstein Copilot, which are designed to provide tailored recommendations based on customer segmentation, will become more sophisticated, allowing businesses to offer highly personalized experiences[5]. As AI technologies advance, they are expected to enhance further the ability to predict customer needs and behaviors, making marketing efforts more targeted and effective[3].
Moreover, as the demand for comprehensive customer insights grows, Salesforce Data Cloud's ability to consolidate data from multiple sources into a single, accurate customer profile will become increasingly critical. This unification process not only aids in eliminating data silos but also improves the precision of identity resolution and segmentation[3]. Consequently, companies will be better equipped to understand customer behaviors and needs, leading to more strategic decision-making and optimized marketing strategies[4].
Additionally, the shift toward omni-channel strategies is likely to accelerate. By leveraging Salesforce Data Cloud, businesses can seamlessly integrate customer interactions across various platforms, providing a holistic view of the customer journey. This approach not only enhances customer experience but also empowers sales and service teams to offer more informed and personalized engagements[8].
References
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[12] DumpsMate. (2024, November 8). Data-Cloud-Consultant Salesforce Certified Data Cloud Consultant (WI25) Questions and Answers. DumpsMate. https://www.dumpsmate.com/data-cloud-consultant-salesforce-certified-data-cloud-consultant24-question.html
[13] https://help.salesforce.com/s/articleView?id=sf.c360_a_identity_resolution.htm&type=5
About the Author
Maneesh Gupta is a seasoned Salesforce and Veeva CRM Consultant with over 18 years of expertise in architecting complex CRM and digital marketing automation platforms. Currently working as a consultant for a large pharmaceutical organization, Maneesh specializes in leveraging Salesforce platform to design and develop robust, scalable customer success systems that enable data-backed decision-making and marketing strategy for organizations. His deep understanding of CRM space, combined with a focus on compliance with global data privacy and protection regulations, positions him as a thought leader in advancing personalized customer engagement and innovative data strategies across industries.
