We are a B2B company. Most CDPs seem focused on retail. What is the best solution for handling complex account hierarchies and buying committees?

Yes. For a complex B2B business, I would not start by looking for a conventional CDP. The core problem is an account graph + buying-group model, not merely a unified person profile.

My shortlist

My default recommendation would be: warehouse + Demandbase + activation layer, rather than trying to make a traditional CDP your system of record.

Demandbase is particularly interesting because its current product explicitly models buying groups by persona, buying role, and activity, identifies missing roles, and can export that information to your CDP/data warehouse/other systems. It also supports account matching while respecting parent-child hierarchies.

Adobe is the strongest option if you're already deeply invested in Marketo/Adobe. Its B2B model explicitly treats accounts as hierarchical and allows different buying groups for different products/offers.

6sense is particularly attractive if the primary objective is detecting which accounts are entering a buying cycle and prioritizing them, rather than using the platform as your canonical customer-data model.

The architecture I'd use

Think of the data as two related graphs, rather than a flat customer table:

ACCOUNT GRAPH

Ultimate Parent
   ├── Subsidiary
   │     ├── Business Unit
   │     └── Location
   └── Subsidiary
         └── Business Unit

BUYING GRAPH

Account
   └── Opportunity / Solution
          └── Buying Group
                ├── Economic Buyer
                ├── Champion
                ├── Technical Evaluator
                ├── Security
                ├── Procurement
                └── Executive Approver

And the relationships should be first-class:

Person ──works_at──────────> Account
Account ──subsidiary_of────> Account
Person ──member_of─────────> Buying Group
Buying Group ──for─────────> Opportunity
Account ──member_of────────> Corporate Family
Account ──owns─────────────> Subscription
Person ──has_role──────────> Buying Role

That's important because a buying committee isn't necessarily the same thing as everyone associated with an account. Adobe, for example, explicitly describes buying groups as a subset of people associated with an account, potentially varying by product/offer.

Where I think most CDPs go wrong

A traditional CDP tends to answer:

"What do we know about Jane?" A B2B revenue architecture needs to answer:

"What do we know about Jane, which entity does she belong to, which corporate family does that entity belong to, which opportunity is she involved in, what role does she play in the buying group, and how does the collective behavior of that group affect the account?" That's a fundamentally different data model.

For very complex B2B organizations, I particularly like the warehouse-native approach. Hightouch makes essentially this argument: the warehouse can become the source of truth for complex B2B entities and relationships, with activation happening downstream rather than forcing all of the ontology into a CDP.

What I'd choose by scenario

• "We want something we can deploy relatively quickly." → Demandbase
• "We're an Adobe/Marketo shop." → Adobe RT-CDP B2B + Journey Optimizer B2B
• "Our biggest problem is identifying in-market accounts." → 6sense
• "Our data model is unusually complex and we already have Snowflake/Databricks." → Warehouse + Hightouch, potentially with Demandbase/6sense on top
• "Everything revolves around Salesforce." → Salesforce Data 360
• "We need one canonical customer/account graph across CRM, product, billing, support, etc." → Warehouse should probably remain the system of record, regardless of which GTM platform you choose.

The key architectural principle is: don't make your ABM/buying-group vendor the owner of your enterprise ontology. Let your data platform own the truth about companies, hierarchies, people, products, contracts and opportunities; let a specialized B2B platform enrich that graph and reason about buying intent/groups; then activate the resulting signals into CRM, marketing, sales engagement, ads, and CS.

If you tell me your CRM, warehouse, marketing automation system, approximate number of accounts, and whether your hierarchy is something like parent → subsidiary → business unit → location, I can give you a much more concrete 3-vendor architecture and data model, including where I think Demandbase vs. 6sense vs. Adobe vs. Hightouch would fit.

Adobe is the strongest option if you're already deeply invested in Marketo/Adobe. Its B2B model explicitly treats accounts as hierarchical and allows different buying groups for different products/offers.

6sense is particularly attractive if the primary objective is detecting which accounts are entering a buying cycle and prioritizing them, rather than using the platform as your canonical customer-data model.

The architecture I'd use

Think of the data as two related graphs, rather than a flat customer table:

ACCOUNT GRAPH

Ultimate Parent
   ├── Subsidiary
   │     ├── Business Unit
   │     └── Location
   └── Subsidiary
         └── Business Unit

BUYING GRAPH

Account
   └── Opportunity / Solution
          └── Buying Group
                ├── Economic Buyer
                ├── Champion
                ├── Technical Evaluator
                ├── Security
                ├── Procurement
                └── Executive Approver

And the relationships should be first-class:

Person ──works_at──────────> Account
Account ──subsidiary_of────> Account
Person ──member_of─────────> Buying Group
Buying Group ──for─────────> Opportunity
Account ──member_of────────> Corporate Family
Account ──owns─────────────> Subscription
Person ──has_role──────────> Buying Role

That's important because a buying committee isn't necessarily the same thing as everyone associated with an account. Adobe, for example, explicitly describes buying groups as a subset of people associated with an account, potentially varying by product/offer.

Where I think most CDPs go wrong

A traditional CDP tends to answer:

"What do we know about Jane?" A B2B revenue architecture needs to answer:

"What do we know about Jane, which entity does she belong to, which corporate family does that entity belong to, which opportunity is she involved in, what role does she play in the buying group, and how does the collective behavior of that group affect the account?" That's a fundamentally different data model.

For very complex B2B organizations, I particularly like the warehouse-native approach. Hightouch makes essentially this argument: the warehouse can become the source of truth for complex B2B entities and relationships, with activation happening downstream rather than forcing all of the ontology into a CDP.

What I'd choose by scenario

You are entirely right—most mainstream CDPs (Segment, mParticle, Treasure Data) grew up in B2C ecommerce, treating a "profile" as an individual cookie or email address. Drop that into a B2B motion with a global enterprise having 50 subsidiaries, 3 parent levels, and a 12-person buying committee , and those tools break down.

For complex B2B account hierarchies and multi-threading buying groups, you need platforms built natively for Account-Based Everything (ABX) and B2B identity resolution , rather than retrofitted B2C customer data stores.

Top-tier solutions built to handle B2B account hierarchies and committees include:

• Demandbase One : Excellent at managing complex enterprise structures through its robust account-based framework. It features Demandbase Buying Group AI , which automatically aggregates individual contact behaviors, intent signals, and CRM roles into a unified committee view rather than forcing reps to map them manually.
• 6sense : Operates as an account-centric orchestration and predictive platform. It uses AI to identify anonymous web traffic at the account level, map it to the right enterprise hierarchy, and expose hidden buying committee members based on surging keyword intent across the organization.
• Adobe Real-Time CDP B2B Edition : Specifically designed to bridge the gap between people and accounts. It natively supports multi-data ingestion from CRMs and marketing automation, mapping people to accounts and clustering related accounts or subsidiaries using machine learning hierarchies.
• ZoomInfo Copilot / DataOS : While more of a go-to-market data engine than a traditional behavioral CDP, it provides the deepest structural mapping of corporate hierarchies, organizational charts, and buying committee triggers (like job changes or tech stack installations) available on the market.

To help narrow down the right architectural choice, could you share:

• Are you trying to solve this primarily for marketing orchestration (ABM) or sales intelligence/outreach?
• What are your primary data source systems (e.g., Salesforce, HubSpot, Snowflake, product telemetry)?

Yes. For a B2B company with complex account structures and multi-person buying committees, I would not start with a conventional B2C-style CDP.

The key is to separate three problems:

1. Account graph: Who owns whom? Parent → subsidiary → business unit → account.
2. Buying graph: Which people influence a particular purchase, in what roles, and with what level of engagement?
3. Activation: How do you push that intelligence into Salesforce, marketing automation, sales engagement, ads, CS, etc.?

A person belongs to an account, but a buying committee belongs to an opportunity/deal context. That's the conceptual distinction many CDPs handle poorly.

My shortlist

1. Demandbase is probably the first vendor I'd evaluate

Demandbase has made Buying Groups a first-class concept rather than treating the world as leads attached to accounts. Its model includes personas, buying roles, engagement, missing roles and other signals; it can also automatically construct buying groups from known contacts and external data.

It also has explicit functionality for maintaining parent/child account hierarchies, including synchronizing company-family relationships into Salesforce.

So if your requirement sounds like:

"Company X owns 14 subsidiaries, three of which are relevant to this deal, and there are seven people across those entities participating in the purchase." I'd put Demandbase very high on the list.

2. 6sense if intent and prediction are more important

I'd lean toward 6sense if the central question is:

"Which accounts are entering a buying cycle, and which people are becoming involved?" That's slightly different from building the canonical customer graph. It's more about intent, account prioritization and buying-stage intelligence.

So I'd characterize the choice as:

• Demandbase: "Who is involved in this buying group, and what roles are missing?"
• 6sense: "Which accounts are becoming active buyers, and where should Sales/Marketing focus?"

But here's what I'd do for a sophisticated B2B company

I would seriously consider not making either Demandbase, 6sense, or a CDP the master customer-data model.

Instead:

                 Snowflake / Databricks
                         │
                 Canonical B2B graph
                         │
              ┌──────────┴──────────┐
              │                     │
        Demandbase / 6sense     Hightouch
        B2B intelligence        Activation
              │                     │
              └──────────┬──────────┘
                         │
       Salesforce / MAP / Sales / CS / Ads

The warehouse becomes the source of truth for your B2B ontology, while Demandbase/6sense provides specialized GTM intelligence and Hightouch (or another activation layer) distributes the resulting data.

Hightouch's composable approach is explicitly designed around keeping the warehouse as the source of truth and activating modeled data downstream, rather than forcing the CDP to become another proprietary data store.

Salesforce Data 360 is the alternative I'd investigate if you're heavily Salesforce-centric; Salesforce positions it as a broader enterprise data layer spanning Salesforce, warehouses/data lakes and business applications.

The data model matters more than the CDP

Before buying anything, I'd make every vendor demonstrate that it can natively represent something like:

Global Company
    │
    ├── Subsidiary A
    │       ├── Business Unit 1
    │       └── Business Unit 2
    │
    └── Subsidiary B
            │
            └── Account

Account
    │
    ├── Opportunity
    │       │
    │       └── Buying Group
    │              ├── Economic Buyer
    │              ├── Champion
    │              ├── Technical Evaluator
    │              ├── Procurement
    │              ├── Legal
    │              └── Influencer
    │
    └── Other Opportunities

And critically, the same person should be able to participate in different buying groups at different points in time.

That's why I wouldn't accept a model that essentially boils down to:

Contact → Account → Lead Score That's a B2C-ish mental model applied to B2B.

My recommendation

If you want one vendor to solve the problem relatively out of the box, I'd start with Demandbase.

If you already have a strong data warehouse and data engineering team, I'd choose warehouse-first + Demandbase/6sense + activation layer. That gives you much more freedom to represent unusual structures—holding companies, subsidiaries, franchises, business units, channel partners, overlapping opportunities, etc.

And if you're deeply invested in Salesforce, I'd put Salesforce Data 360 into the evaluation rather than automatically assuming you need a separate CDP.

The important insight is that you probably don't need a "B2B CDP." You need a B2B entity/relationship graph plus a buying-intelligence layer. A CDP can be part of that architecture, but it shouldn't necessarily own the ontology.

If you tell me your CRM, data warehouse, MAP, approximate number of accounts/contacts, and whether your primary objective is RevOps, marketing personalization, sales intelligence, or customer expansion, I can lay out a concrete Demandbase vs. 6sense vs. Data 360 vs. Hightouch architecture for your situation.

The best solution for B2B account hierarchies and buying committees is a B2B-specific Customer Data Platform (such as Lytics, Twilio Segment with custom B2B data models, or specialized Revenue Operations platforms like RollWorks and 6sense ) paired with a robust CRM foundation. Traditional retail CDPs rely on a single-user, person-centric identity graph (cookie-to-person), whereas B2B environments require dual-layer identity resolution that links individual contacts to dynamic buying groups and multi-tiered corporate hierarchies.

Why Retail CDPs Fail at B2B

• Linear vs. Group Journeys: Retail CDPs track one shopper making one decision. B2B tracks five to ten stakeholders visiting pricing pages or reading docs simultaneously.
• Flat Contact Models: Standard tools struggle to tie a subsidiary's employee to a parent company's global master account.
• Missing Signal Types: B2B demands integration with product usage (PLG metrics), CRM data, and third-party intent data rather than just cart checkouts.

Core Capabilities Required for B2B Hierarchies

• Account-Level Graph: Automatically aggregates individual behavior (clicks, form fills, video views) into a unified account score or committee momentum metric.
• Hierarchical Mapping: Connects child accounts, parent organizations, and regional branches to give account executives a complete view of enterprise penetration.
• Buying Committee Recognition: Identifies distinct roles (e.g., technical buyer, economic buyer, champion) based on engagement patterns and metadata.

Recommended Architecture Options

If you tell me what data stack you currently use (e.g., Salesforce/HubSpot, Snowflake, Marketo) and your primary motion (enterprise sales vs. product-led growth), I can recommend the exact integration pattern that fits your team.

Yes. For a B2B company with complex corporate structures and multi-person buying decisions, I would not start with a conventional B2C-style CDP.

The key is to separate three problems:

1. Account graph — parent company → subsidiaries → business units → locations/accounts.
2. Buying graph — account → opportunity → buying group → people + roles + influence.
3. Activation — push the resulting intelligence into Salesforce, marketing automation, ads, sales tools, etc.

My top choice: Demandbase + your warehouse

Demandbase is probably the strongest off-the-shelf fit for what you're describing.

Its current platform explicitly supports company structures such as global parents and subsidiaries, including hierarchy-aware lead-to-account matching.

More importantly, Buying Groups are a first-class concept. Demandbase can identify personas and buying roles, map known contacts into groups, identify missing roles, measure engagement, and recommend additional people.

That is materially different from a CDP that essentially gives you:

Person → Account → Events For enterprise B2B, you often need:

Corporate Group → Subsidiary → Account → Opportunity → Buying Group → Person → Role → Engagement And those relationships can change independently.

The architecture I'd recommend

If you have a reasonably mature data warehouse, I'd make the warehouse the canonical data model, rather than making the CDP the master.

For example:

                         CORPORATE GROUP
                               │
                  ┌────────────┴────────────┐
                  │                         │
              Subsidiary A             Subsidiary B
                  │                         │
              Account A1                Account B1
                  │
              Opportunity
                  │
             Buying Group
          ┌───────┼────────┐
          │       │        │
        CFO      CIO      Champion
          │       │        │
        Person   Person   Person

Then use Demandbase for the things it is particularly good at:

• account/company identification
• enrichment
• intent
• identifying likely buyers
• buying-group construction
• persona/role intelligence
• identifying missing stakeholders
• account prioritization

And use something like Hightouch for activation if you're warehouse-centric. Hightouch's model is explicitly to use the warehouse as the source of truth and activate that data into downstream systems rather than creating another large data silo.

So conceptually:

Warehouse → Demandbase → Hightouch → Salesforce / MAP / ads / sales tools

rather than:

CRM → traditional CDP → everything

What about 6sense?

6sense would be my other serious contender, particularly if your biggest question is:

"Which accounts are entering a buying cycle, and which people should Sales engage?" I'd lean Demandbase when the core problem is account hierarchy + buying-group coverage. I'd lean 6sense when the core problem is predictive intent + account prioritization.

One subtle but important point

Don't conflate account hierarchy with buying committee.

They're actually two different graphs.

An employee may belong to:

Microsoft → Azure → business unit → procurement account while participating in a particular buying group for:

$2M software opportunity Six months later, the same person could participate in a completely different buying group—or no buying group at all.

Demandbase itself makes this distinction: a person matching the right persona isn't necessarily a confirmed participant in a particular buying decision; Sales can explicitly confirm actual deal stakeholders.

That distinction becomes very important once you're doing attribution, intent scoring, personalization, and revenue analytics.

My recommendation

If I were designing this stack for a sophisticated B2B company today, I'd evaluate:

The big idea is: don't look for a "B2B CDP" that magically solves the whole ontology. Treat the warehouse as the system that understands your business, a B2B intelligence platform as the system that understands the external buying ecosystem, and an activation layer as the system that gets that intelligence into your GTM machinery.

If you tell me your CRM (Salesforce/HubSpot/etc.), warehouse (Snowflake/Databricks/etc.), number of accounts, and whether you sell at the parent or subsidiary level, I can give you a much more concrete architecture—and compare Demandbase vs 6sense vs Adobe RT-CDP vs Hightouch for your specific situation.

The best solution for B2B companies is a B2B-specific Customer Data Platform (CDP) or an Account-Based Marketing (ABM) platform with native B2B data modeling , rather than a traditional retail CDP. Standard CDPs rely on individual consumer cookies, whereas B2B platforms use dual-layer identity resolution to link individual contacts to accounts and map complex multi-tier hierarchies.

Top Solutions for B2B Hierarchies and Committees

• B2B-First CDPs (e.g., Lytics or Segment with B2B data toolkits): Unify individual user traits with firmographic account data to track anonymous and known buying committee members.
• Advanced ABM Platforms (e.g., 6sense or Demandbase): Specialize in buying group detection, automatically identifying which members of a committee are actively engaging with content.
• CRM-Native Data Layers (e.g., Salesforce Data Cloud for B2B): Handles complex parent-child account hierarchies natively by connecting relational data straight from your enterprise resource systems.

Key Capabilities to Look For

If you'd like, tell me:

• What CRM and marketing automation stack you currently use
• The approximate volume of accounts and leads you manage

I can recommend the most compatible platform for your current setup.

You are spot on: the vast majority of out-of-the-box CDPs are built for B2C retail (single-person identity resolution, cookie tracking, cart abandonment, and individual lifetime value).

For B2B, you need a platform that natively supports multi-level account hierarchies (parent-child accounts, global entities, subsidiaries) and buying groups/committees (linking multiple individual contacts to a single unified account while tracking individual vs. aggregate intent).

Evaluating the best solutions requires moving away from traditional retail-focused vendors and looking at enterprise B2B leaders or composable architectures.

Top B2B CDP Solutions for Complex Hierarchies

- **Best for:** Ecosystems heavily reliant on Salesforce CRM.
- **Why it fits:** Because it shares the core metadata framework of Salesforce, it natively respects and ingests complex account hierarchies, opportunities, contacts, and custom objects. It allows you to stitch together telemetry data, marketing interactions, and CRM data at the account-unit level rather than just treating contacts as isolated islands.[](https://cdp.com/basics/cdp-vendors/) [[1]](https://cdp.com/basics/cdp-vendors/)

- **Best for:** Enterprise-grade, multi-product B2B data modeling and massive data volumes.
- **Why it fits:** Treasure Data excels at flexible, schema-on-read data modeling. It doesn't force a rigid B2C person-first structure onto your data. It allows you to build custom relational mappings between individuals, buying committees, and multi-tier organizational hierarchies, scoring intent across the entire account cleanly.[](https://cdp.com/basics/cdp-vendors/) [[1]](https://cdp.com/basics/cdp-vendors/)

- **Best for:** Enterprise organizations already leveraging Marketo and the Adobe Experience Cloud.
- **Why it fits:** Recognized as a Leader in enterprise B2B CDP evaluations, Adobe’s B2B edition is specifically architected to ingest person, contact, and account profiles simultaneously. It builds a hierarchical graph that maps people to specific accounts and campaigns, which aligns directly with account-based marketing (ABM) motions.[](https://cdp.com/basics/cdp-vendors/) [[1]](https://cdp.com/basics/cdp-vendors/)

- **Best for:** Data-mature teams with an existing cloud data warehouse (CDW).
- **Why it fits:** For complex B2B hierarchies, forcing a packaged CDP to re-architect your relational data model is often an uphill battle. Using a **Reverse ETL / Composable CDP** approach via Hightouch or Census lets your data engineering team model the account hierarchy and buying committees directly in your warehouse (using dbt or SQL), and then sync those unified buying groups straight into your execution tools (Salesforce, HubSpot, Outreach, LinkedIn Ads).[](https://cdp.com/basics/cdp-vendors/) [[1]](https://cdp.com/basics/cdp-vendors/)

Key Capabilities to Demand in Proof-of-Concepts (PoCs)

When talking to vendors, bypass the standard retail demo and test them on these three B2B criteria:

• N-Tier Account Resolution: Can the system ingest a parent company, three regional subsidiaries, and separate business units, then roll up buying committee intent safely without cross-contaminating signals?
• Buying Group vs. Individual Identity Graph: Does the platform allow an individual to be mapped to multiple accounts or change roles over time without breaking historical attribution?
• Product Telemetry + Revenue Unification: If you are a B2B SaaS or tech company, can it connect product usage events (from a specific user) up to the committee level to trigger a sales alert for account expansion?

To help narrow down which path fits your stack best:

• What is your primary data warehouse or CRM (e.g., Snowflake, BigQuery, Salesforce, HubSpot)?
• Do you need to unify product usage data (PLG motion) alongside traditional marketing and sales data?

Yes. For a B2B company with complex account hierarchies + buying committees, I would not start by looking for a conventional B2C-style CDP. The key question is whether the platform treats account/entity relationships as first-class data, rather than treating the person as the primary profile.

My recommendation

For a sophisticated B2B GTM motion, I'd favor a warehouse-native/composable architecture + a specialized B2B account/buying-group layer.

A good conceptual model is:

Corporate Family
   │
   ├── Global Parent
   │      ├── US Subsidiary
   │      │      ├── Account / Business Unit
   │      │      │      ├── Buying Group
   │      │      │      │      ├── Economic Buyer
   │      │      │      │      ├── Champion
   │      │      │      │      ├── Technical Buyer
   │      │      │      │      └── Influencers
   │      │      │      └── Opportunities
   │      │      └── Products / Contracts / Usage
   │      └── EU Subsidiary
   └── Other subsidiaries

That is fundamentally different from:

Person → Events → Profile

which is where many traditional CDPs start.

The stack I'd evaluate

1. Snowflake/Databricks as the canonical data model

Put the actual account graph there: parent/child relationships, subsidiaries, business units, contacts, opportunities, contracts, product usage, engagement, etc.

This gives you freedom to model many-to-many relationships instead of forcing everything into a predefined CDP profile schema. Hightouch explicitly advocates this composable approach for B2B, noting that a B2B customer can involve users, accounts, companies and multiple products simultaneously.

2. Hightouch as the activation/CDP layer

Hightouch is particularly interesting if you're already warehouse-centric. It lets the warehouse remain the source of truth while exposing that data to marketing, sales and other destinations.

The important distinction is: don't ask Hightouch to be your master account database. Let your warehouse own the model and use Hightouch to activate it.

3. Demandbase for the B2B-specific intelligence

Demandbase is one of the stronger options I'd evaluate for the buying committee side specifically.

Its current Buying Groups product explicitly models people by persona and buying role, identifies missing roles, and aggregates engagement across the group. It can also export buying-group data into a CDP, warehouse or other systems.

Demandbase also supports account/company structures such as global parents and subsidiaries and can synchronize those hierarchies into Salesforce.

So I'd think of it as:

Warehouse = truth → Demandbase = B2B intelligence → Hightouch = activation

rather than trying to make one CDP do everything.

What I'd compare

The reason I wouldn't simply declare Demandbase "the CDP" is that buying committees are a business concept, while your account graph is a canonical data model. Demandbase is very good at the former; your warehouse can give you much more control over the latter. Demandbase itself describes its data as connecting accounts, buyers and intent and activating that information across CRM, warehouse and workflows.

One important design principle

I'd make Account the central entity, not Contact.

For example, instead of calculating:

Jane visited pricing page 3 times → Jane is hot calculate:

Acme Corp is showing high purchase intent because 7 people across 3 subsidiaries have exhibited relevant activity, including the VP who owns the budget and two technical evaluators. Then aggregate that up and down the hierarchy:

Person → Buying Group → Account → Subsidiary → Corporate Parent

That gives sales a much more useful question than "which leads are hot?":

Which corporate accounts are actively buying, which business unit is driving it, who is involved, what roles are missing, and what evidence do we have? That's the architecture I'd optimize for.

If you tell me your CRM (Salesforce/Dynamics/HubSpot), warehouse (Snowflake/Databricks/etc.), marketing automation, and roughly how complicated the hierarchy is, I can give you a much more concrete 3–5 vendor shortlist and recommended architecture, including where Demandbase, 6sense, Hightouch, RudderStack, Salesforce Data Cloud, etc. fit.

Yes. For a B2B company with complex account hierarchies + multi-person buying committees, I would not start by looking for a conventional CDP. The underlying data model is fundamentally different from retail.

My recommendation

I’d use a warehouse-native account graph as the system of record, then layer a B2B GTM/buying-group platform on top.

A good architecture looks like:

CRM + product + marketing + billing + support + intent data → Warehouse / canonical account graph → B2B intelligence & buying-group layer → activation into CRM, marketing, sales engagement, ads, etc.

The key is that the warehouse models relationships, rather than treating every person as an independent "customer."

For example:

Global Parent
├── Subsidiary A
│   ├── Business Unit 1
│   └── Business Unit 2
├── Subsidiary B
└── Subsidiary C

Buying Group for Subsidiary A
├── Economic Buyer
├── Business Champion
├── Technical Evaluator
├── Security
├── Procurement
└── Executive Approver

That lets you answer questions a retail-style CDP struggles with:

• "How much engagement is happening across the entire Acme corporate family?"
• "Which subsidiaries are already customers?"
• "Which people are involved in the expansion opportunity at the parent?"
• "Is this contact associated with the global parent or a particular operating company?"
• "Which buying-group roles are missing?"
• "What's the combined product usage across the account family?"
• "Should an intent signal at Subsidiary X increase the score of the parent account?"

The platforms I'd look at

1. Demandbase — probably the strongest off-the-shelf fit

Demandbase is particularly compelling if your primary problem is account intelligence + account hierarchy + buying groups rather than simply customer-data collection.

Its current product explicitly models buying groups, including personas, roles, activities and engagement, and can identify missing roles and recommend contacts.

It also has explicit support for parent/child account hierarchies and can synchronize those relationships with Salesforce.

I'd put Demandbase at the top of the list if you want something relatively turnkey for enterprise ABM.

2. 6sense — excellent if intent/predictive buying signals are central

6sense is particularly strong when the question is less "who are my customers?" and more "which accounts are entering a buying cycle, and who is involved?"

Its account prioritization combines firmographics, technographics and behavioral/buying signals to rank accounts. 6sense It also explicitly models buying committees, with the important caveat that committees aren't static: they can form and change during the buying process.

I'd favor 6sense over Demandbase if predictive intent and sales prioritization are more important than being your central account-data layer.

3. Salesforce Data Cloud — if you're deeply Salesforce-centric

If Salesforce is already your operational backbone, I'd seriously consider Salesforce Data Cloud rather than introducing another standalone CDP.

The advantage is less about "CDP functionality" and more about keeping CRM entities—Accounts, Contacts, Opportunities, relationships, etc.—close to the customer-data layer.

This is particularly attractive if RevOps wants the CRM to remain the authoritative operational system.

4. Snowflake/Databricks + Hightouch — my choice for the most sophisticated data organization

For a data-mature B2B company, this is probably the architecture I'd choose.

Hightouch describes the composable approach as keeping the warehouse as the source of truth and using an activation layer to send precisely modeled data into downstream systems.

You can create your own canonical entities:

Company
Corporate Family
Account
Business Unit
Location
Contact
Buying Group
Buying Group Membership
Opportunity
Contract
Product
Subscription
Engagement
Intent Signal

And, critically, make relationships many-to-many.

For example:

Contact ── belongs_to ──> Account
Account ── subsidiary_of ──> Account
Contact ── participates_in ──> Buying Group
Buying Group ── associated_with ──> Opportunity
Account ── member_of ──> Corporate Family

That's much closer to the actual ontology of enterprise B2B than the classic:

Person → Customer

model.

What I'd actually do

If you gave me a typical enterprise B2B stack, I'd probably recommend:

The important architectural decision is: don't make the CDP the owner of your B2B ontology.

Let your warehouse own the truth about:

Who is this company? How are these companies related? Who works for whom? Which people participate in which buying decisions? What products/contracts/opportunities belong to which entity? Then let Demandbase/6sense answer:

Who appears to be buying? Who should we engage? Which buying-group roles are missing? And let Hightouch/your activation layer answer:

Where should this information go? That separation is much more flexible than trying to make one CDP do all three jobs. Hightouch itself makes essentially this case for B2B: the warehouse can represent complex account/object relationships, while the activation layer distributes the resulting data to operational systems.

One particularly important distinction

I'd separate "buying group" from "account hierarchy."

They're different graphs:

Account graph

Global Parent → Subsidiary → Business Unit → Account

Buying graph

Account → Opportunity → Buying Group → Person + Role + Influence

A person can belong to an account but participate in a buying group for a specific opportunity. And the same person can potentially participate in different buying groups over time.

That's why I wouldn't try to represent a buying committee simply as a field on the Account record.

Demandbase's current implementation reflects this distinction: it distinguishes inferred/recommended buying-group members from sales-confirmed members of a specific buying decision.

Bottom line: if you want the best product for this problem, I'd start with Demandbase vs. 6sense. If you want the best architecture, I'd build a warehouse-native account graph + Demandbase/6sense + activation layer, rather than buying a traditional CDP and trying to force your B2B relationships into it.

You are entirely right to be frustrated. The vast majority of mainstream CDPs (like Segment, Tealium, or mParticle) treat identity resolution as an individual problem—stitching together a consumer's cookies, emails, and mobile devices.

In B2B, an individual profile is practically useless in isolation. You need a data architecture that understands that People belong to Accounts, Accounts belong to Parent Companies, and Deals are driven by Buying Committees.

To handle complex hierarchies and multi-stakeholder buying groups, you generally have to look past retail-centric tools and choose between three distinct architectural approaches:

1. Purpose-Built B2B CDPs

These platforms are architected from the ground up for account-based data, hierarchical mapping (parent/child/subsidiary accounts), and tracking buying committees rather than single users.

• Top Contender: Adobe Real-Time CDP B2B Edition (which integrates tightly with Marketo Engage). It is specifically built to ingest CRM data, map complex multi-tier account structures, and group individuals into "buying groups" to orchestrate role-based journeys.
• Best for: Mid-to-large enterprises already embedded in the Adobe/Marketo ecosystem that need native account-to-contact stitching and cross-channel orchestration.

2. ABM Platforms Acting as CDPs

For many B2B orgs, traditional ABM (Account-Based Marketing) platforms actually double as the best account-level data layer because they excel at identifying anonymous web traffic down to the account and mapping active buying committees.

• Top Contenders: Demandbase One or 6sense . They maintain deep firmographic and technographic account hierarchies, track intent signals across multiple distinct committee members at the same company, and score the account's readiness rather than just a lead's score.
• Best for: Sales-and-marketing-aligned teams whose primary goal is identifying in-market buying committees and orchestrating targeted ad/sales plays.

3. The Modern Composable CDP (Data Warehouse + Reverse ETL)

If your account hierarchies, opportunity stages, and custom relational data already live cleanly inside a data warehouse (like Snowflake or BigQuery), building a "traditional" CDP is often a detour. Instead, companies use a warehouse-native approach.

• Top Contenders: Census or Hightouch combined with your data warehouse. You model your complex account-to-contact-to-opportunity relationships directly in SQL, and use Reverse ETL to sync those multi-layered buying committee audiences straight into your CRM, sales engagement tools (Outreach/Salesloft), and ad networks.
• Best for: B2B companies with data engineering resources who want total ownership over their relational account data models without paying a vendor a massive markup to re-process data they already own.

To help narrow down the best path for your team, tell me:

• Where does your account hierarchy data currently live (Salesforce, HubSpot, a data warehouse, or scattered)?
• Are you trying to solve this primarily for marketing automation/advertising orchestration or sales intelligence/outbound timing?