People
A People profile in Salesforce is a unified record that represents one real-world individual, built by combining identity data from many source systems into a single canonical view.
Definition
A People profile in Salesforce is a unified record that represents one real-world individual, built by combining identity data from many source systems into a single canonical view. In Data Cloud (now Data 360), this unified individual is produced by identity resolution, which matches and merges source records from CRM, marketing, commerce, and other streams into one profile per person.
The same idea appears in Marketing Cloud Personalization, where anonymous and known activity for a visitor is merged into a Unified Customer Profile over time. Across both products, the goal is the same: stop treating a person as scattered rows and start treating them as one addressable entity.
How a unified People profile gets built
Source profiles versus unified profiles
Every record that flows into Data Cloud starts life as a source profile. A source profile is a single row from one data stream, like a Contact from Sales Cloud, a subscriber from Marketing Cloud, or a shopper from your commerce platform. Each source holds a partial picture, and the same person often appears in several of them at once. Identity resolution reads those source profiles and decides which ones describe the same human. When it finds a match, it links them under one unified profile. That unified profile is the People record your teams and automations actually use. It is not a copy of any single source. It is a calculated result that points back to all of the sources that fed it. This separation matters for trust. Because the link between source and unified data is preserved, you can always trace a unified attribute back to where it came from. If a phone number looks wrong on the People profile, you can find the exact source row and stream responsible. The unified view stays honest because nothing is thrown away during the merge.
Match rules decide who is the same person
Match rules are the part of an identity resolution ruleset that tells Data Cloud how to recognize two records as the same individual. A typical rule matches on a strong identifier such as a normalized email address, a phone number, or a loyalty ID. You can stack several rules, and a match on any one of them can join the records into a single unified profile. Good match design is a balance. Loose rules over-merge, so two different people collapse into one profile and personalization leaks across them. Strict rules under-merge, so one person stays split into several profiles and the unified view never forms. Most teams start conservative, watch the merge results, then loosen carefully. Identifiers need normalization before they can match reliably. Email casing, phone formatting, and stray whitespace all break naive comparisons. Data Cloud applies formula and normalization steps so that JANE@EXAMPLE.COM and jane@example.com resolve to the same key. Without that step, exact-match rules quietly fail and profiles fragment.
Reconciliation rules pick the winning value
Once several source profiles are matched, Data Cloud has to decide what the unified People profile should actually say. Two sources might list different first names, two different mailing addresses, or two different birth dates. Reconciliation rules resolve those conflicts and choose one value per attribute for the unified record. Reconciliation can be driven by recency, so the most recently updated value wins. It can be driven by frequency, so the value that appears in the most sources wins. It can also be driven by source priority, so a trusted system of record always outranks a noisier feed. You set this per field, because the right tie-breaker for an address is rarely the right one for a marketing opt-in. Some attributes are not single-valued at all. A person can legitimately hold several email addresses, phone numbers, and postal addresses at the same time. Those live on Contact Point objects (Contact Point Email, Phone, Address) that relate many-to-one to the individual, so the People profile keeps every valid channel rather than forcing a single winner.
The Individual DMO and Contact Points
Under the hood, a People profile is modeled on the Individual data model object, or Individual DMO. This is the standard Data Cloud object for contacts, customers, and prospects. It carries personal fields like first name, last name, birth date, gender identity, pronouns, and salutation, plus richer demographic attributes when you choose to map them. The Individual DMO rarely stands alone. It connects to Contact Point Email, Contact Point Phone, Contact Point Address, and Contact Point App through many-to-one relationships. Those objects hold the actual ways you can reach the person. Keeping channels separate from core identity is what lets one profile own several emails or several phone numbers cleanly. Unified link objects sit between source and unified data and record which source individuals rolled up into each unified individual. They are the audit trail of the merge. When you query a unified People record through the Data Cloud APIs, those links are what let you retrieve every underlying source ID across retail, loyalty, web, and mobile systems for the same person.
People in Marketing Cloud Personalization
Outside Data Cloud, the People idea shows up strongly in Marketing Cloud Personalization (formerly Interaction Studio). There, every visitor gets a profile ID the moment they interact, even before you know who they are. Anonymous browsers carry only that profile ID. Named users add accepted identities such as an email address or phone number on top of it. The interesting work happens when an anonymous visitor identifies themselves, perhaps by submitting a form or logging in. Personalization then attempts to merge the earlier anonymous activity with the newly known identity into one Unified Customer Profile. The browsing history that happened before sign-in is not lost. It attaches to the now-known person. This continuity is what makes real-time personalization feel coherent. The system can recommend products based on pages viewed anonymously last week, then keep refining as the same person returns across devices. The People profile becomes the running memory of an individual across every touchpoint, instead of a fresh stranger on each visit.
Why unified People profiles are worth the effort
A unified People profile is the foundation for almost everything downstream. Segments built on a unified individual count each person once, so audience sizes are honest and suppression actually works. Calculated insights that compute lifetime value or engagement scores produce one number per human, not one per fragment. Activation depends on it too. When you send an audience to an ad platform, an email tool, or an agent, you want to reach the person, not five partial versions of them. Because the unified profile gathers every Contact Point, activation can choose the right channel and respect consent at the individual level rather than guessing. Profile quality is therefore a design decision, not an afterthought. The accuracy of your match rules, the sanity of your reconciliation order, and the cleanliness of your incoming identifiers all decide whether the People profile can be trusted. A unified profile also updates continuously as new source data arrives or rules change, so the work of tuning identity resolution pays off on every record, every day.
Configure identity resolution to build People profiles
Unified People profiles are produced by an identity resolution ruleset in Data Cloud. You do not create a People record by hand. You define the rules, then Data Cloud builds and maintains the unified profiles for you. Here is the shape of that configuration.
- Map data to the Individual DMO
Bring your CRM, marketing, and commerce sources in as data streams, then map their person fields to the Individual data model object and the related Contact Point objects. Clean mapping here is what makes later matching possible.
- Create an identity resolution ruleset
In Data Cloud setup, create a ruleset targeting the Individual DMO. The ruleset is the container that holds all of your match and reconciliation logic for People profiles.
- Add match rules
Define how two records count as the same individual, for example an exact match on normalized email or on phone number. Add normalization steps so casing and formatting differences do not break matches.
- Set reconciliation rules
For each attribute, choose how conflicts resolve: most recent value, most frequent value, or a preferred source. Tune address and contact fields separately from name and demographic fields.
- Run, review, and refine
Process the ruleset, then inspect the unified individuals it produces. Watch for over-merging and under-merging, adjust the rules, and reprocess until the profiles look right.
The identifiers that link records, such as email, phone, or a loyalty key. Looser criteria merge more aggressively; stricter criteria keep records separate.
Per-field tie-breaker for choosing a winning value: recency, frequency, or source priority.
Formula and standardization applied before comparison so values like email casing and phone formatting line up.
Whether multi-valued channels (email, phone, address) are kept as separate Contact Point records rather than collapsed to one.
- Over-merging is worse than under-merging. Two real people fused into one profile leaks personalization and consent across them, which is hard to unwind.
- Identifiers must be normalized before matching. Without it, JANE@EXAMPLE.COM and jane@example.com look like two people and profiles fragment.
- A unified individual ID can change as rules and data evolve. Do not treat it as a permanent external key for integrations.
- Reconciliation is per field, not global. A single recency rule for the whole profile usually produces strange results on at least one attribute.
Prefer this walkthrough as its own page? How to People in Salesforce, step by step
Trust & references
Cross-checked against the following references.
Straight from the source - Salesforce's reference material on People.
Hands-on resources to go deeper on People.
About the Author
Dipojjal Chakrabarti is a B2C Solution Architect with 29 Salesforce certifications and over 13 years in the Salesforce ecosystem. He runs salesforcedictionary.com to help admins, developers, architects, and cert/interview candidates sharpen their fundamentals. More about Dipojjal.
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Q1. What is a People profile in Salesforce Data Cloud?
Q2. How does Data Cloud build a single People profile per real-world individual?
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