Building a Single Customer View with Salesforce Data Cloud

The "single customer view" has been a marketing aspiration for decades. In practice, it has been constrained by the fragmented state of customer data: CRM systems, marketing platforms, product databases, support tools, and website analytics all hold different pieces of the picture, rarely in a consistent format, rarely updated simultaneously.

Salesforce Data Cloud makes a genuine single customer view achievable for the first time without a custom data engineering project. This article explains what a single customer view in Data Cloud actually consists of, how to build it, and how Marketing Cloud Next uses it.

What a Single Customer View Is (and Is Not)

A single customer view is not a reporting dashboard. It is not a spreadsheet merge. It is an operational architecture — a live, unified data layer where every profile is continuously updated from all connected sources, deduplicated to a single person record, and available for use by any connected system in real time.

For B2B marketing, a genuine single customer view means:

• One profile per real person, regardless of how many CRM records, form submissions, or tool-specific IDs exist for them
• All engagement history from all channels (email, web, events, product) attached to that one profile
• Account and relationship context (which company, which deals, which products they use) visible on the same profile
• Consent status per channel, current and accurate
• Enriched attributes (scores, tiers, segment memberships) computed and maintained automatically

The Data Sources That Build the View

A B2B single customer view in Data Cloud typically requires data from several sources:

Salesforce CRM (Contact and Lead): The primary source for person identity data — name, email, phone, job title, company. This is the data your sales team maintains and considers authoritative.

Salesforce CRM (Account): Company-level data — industry, size, contract status, revenue, region. In B2B, account context is often more important for segmentation than individual demographics.

Salesforce CRM (Opportunity and Opportunity Contact Role): Deal history and pipeline. Which contacts are associated with open deals? Which accounts have closed-won revenue? This is the commercial context that separates high-value prospects from the general database.

Website Engagement (from MCN tracking script): Behavioural intent signals — which pages did this person visit, which content did they download, how recently did they engage? This is the layer that tells you where a person is in their buying journey.

Marketing Cloud Next campaign history: Which campaigns were they sent? Which did they engage with? This provides the outbound marketing context.

Product usage data (if SaaS): What features do they use, how active are they, what is their health score? For customer expansion campaigns, this is often the most actionable data available.

Third-party enrichment (optional): Company data from providers like Clearbit or ZoomInfo can fill gaps in CRM data — adding missing industry classifications, revenue estimates, or technographic data.

[Screenshot: Data source consolidation diagram showing inputs to Data Cloud]

A data flow diagram showing five data source categories feeding into Salesforce Data Cloud: Salesforce CRM (Contact, Account, Opportunity), Website Engagement (web tracking), Product Database (usage data), Event Platform (attendance data), and Third-Party Enrichment (company data) — all converging into the Unified Individual layer

id: scv-data-sources-diagram

The Identity Resolution Step

Consolidating data from multiple sources creates an immediate problem: the same person exists under different IDs in each system. Your CRM has them as Contact ID 003ABC. Your website analytics has them as Session ID XYZ123. Your product database has them as User ID u_456. Your event tool has them by email address only.

Data Cloud's identity resolution matches these fragmented records to a single Unified Individual using matching rules you define. The most common matching approach for B2B:

1. Email match (exact): If two records from different sources share the same email address, they belong to the same person with high confidence.
2. Name + Company match (fuzzy): If two records have similar names at the same company, they probably belong to the same person.
3. Phone match (exact): Supplements email matching for records without email.

The resolution process runs continuously. When a new form submission arrives with an email address that matches an existing Unified Individual, the new data is merged automatically — enriching the existing profile rather than creating a duplicate.

Profile Enrichment with Calculated Insights

After the base profile is assembled from source data, Calculated Insights add computed attributes that are not available in any single source system.

Calculated Insights are SQL-like computations run on your Data Cloud data to produce new attributes on the Unified Individual. Examples:

Recency, Frequency, Monetary (RFM) scoring: Calculate a combined score for each contact based on when they last engaged (Recency), how often they engage (Frequency), and the commercial value of their account (Monetary). This single score enables powerful audience tiering.

Days Since Last Engagement: A simple date-difference calculation. More useful as a segment condition than raw engagement timestamps.

Account Total Pipeline Value: Sum of open Opportunity amounts across all contacts at the same Account. Enables prioritising outreach to accounts with the highest pipeline concentration.

Feature Adoption Index: For SaaS companies with product usage data, a computed score aggregating which features are used and how frequently. Identifies expansion candidates and churn risks.

Content Interest Category: Based on which page categories a contact has visited most frequently, classify their primary product interest area. Powers personalisation that matches content to demonstrated interest.

[Screenshot: Calculated Insights configuration for custom profile enrichment]

The Calculated Insights editor showing a SQL expression computing a Recency Score (0-100) from the days since last email engagement — the preview panel shows the score distribution across the current Unified Individual population

id: scv-calculated-insights

The Unified Profile in Practice

Once identity resolution has run and calculated insights are populated, the Unified Individual profile becomes a rich, operational record.

A well-built B2B single customer view profile contains:

Identity: Name, primary email, secondary emails, phone, job title, company name, LinkedIn URL (if enriched)

Company context: Industry, employee count, revenue tier, technology stack, contract status, assigned sales rep, territory segment

Engagement summary: Engagement Score, last email open date, last web visit date, content interest category, event attendance history

Commercial context: Open Opportunities with stages, closed-won revenue, product usage tier, customer health score

Consent: Email opted-in Y/N, SMS opted-in Y/N, WhatsApp opted-in Y/N, per-channel last updated date

Segment memberships: Current membership in all active Data 360 Segments — updated in near-real-time as conditions change

[Screenshot: Unified Individual profile showing aggregated data from multiple sources]

A Unified Individual profile showing data assembled from four sources: CRM Contact (name, title, company), Website Engagement (last visit: 3 days ago, 8 sessions in last 30 days), Product Usage (feature adoption: 73%, login: daily), and Calculated Insights (Engagement Score: 84, RFM Tier: High Value)

id: scv-unified-individual-profile

How Marketing Cloud Next Uses the Unified View

The single customer view in Data Cloud is not just a data asset — it is the live operational layer that Marketing Cloud Next acts on.

Segmentation: Data 360 Segments evaluate the Unified Individual and all connected objects. A segment targeting "high-value accounts with VP-level contacts showing product intent" requires the Account data, the job title, and the website engagement data to all be present and correctly linked. The richer the Unified Individual, the more precisely you can segment.

Personalisation: Personalisation Points pull values directly from Unified Individual fields. A personalised email that says "Hi [First Name], we saw you're exploring [Last Page Category]" is only possible because the Unified Individual has both the name and the web engagement data in one place.

Journey decisioning: Flow Builder branch conditions evaluate Unified Individual attributes in real time. A branch condition that routes a contact to a different email path based on their Engagement Score tier works because the score is live on the Unified Individual, not a snapshot from a previous batch sync.

Attribution: Opportunity Influence links campaign touches (from Campaign Member data) through the Unified Individual to Salesforce Opportunities (via Opportunity Contact Role). The chain only works when all the connections in the data model are correctly configured.

[Screenshot: Segment using unified customer view attributes]

A Data 360 Segment with four conditions spanning the full unified view: Unified Individual Engagement Score > 60, Account Industry = Financial Services, Website Engagement (Pricing page visit in last 14 days), Product Usage (Feature Adoption Index > 50) — a segment only possible with a multi-source unified view

id: scv-segment-using-unified-view

Summary

A single customer view in Salesforce Data Cloud is built from four components: consolidated data from all relevant sources, identity resolution that merges fragmented records into Unified Individuals, calculated insights that enrich profiles with computed attributes, and correctly configured relationships that enable cross-object queries.

The payoff for Marketing Cloud Next is direct: better segments, more precise personalisation, more actionable journey decisions, and cleaner pipeline attribution. The investment is in the data architecture work — getting the sources connected, the model designed, and the identity resolution calibrated correctly.

That investment, done once, enables every marketing programme built on the platform to operate with significantly more precision than any disconnected stack could provide.

Want help building a single customer view for your Marketing Cloud Next programme? Pardive's data architecture service covers source integration, model design, identity resolution, and calculated insight configuration. Book a free architecture consultation.