B2B analytics
This guide provides a comprehensive implementation reference for B2B account-level analytics using Customer Journey Analytics (CJA) B2B Edition and Real-Time Customer Data Platform (RT-CDP) B2B Edition. It is designed for solution architects, marketing technologists, and implementation engineers who need to incorporate B2B account-level information into cross-channel customer journey analysis.
It covers all viable approaches for account-centric analytics, from flat account structures to complex global account hierarchies, with guidance on when to choose each option. The plan addresses B2B data connections, account data view configuration, workspace analysis, and dashboard publishing.
B2B Analytics extends standard CJA capabilities with account-based connections, B2B-specific containers (Account, Global Account, Opportunity, Buying Group), and account-level reporting. This capability enables organizations to analyze marketing and sales engagement at the account level, track opportunity progression, measure buying group completeness, and attribute revenue to marketing touchpoints across extended B2B sales cycles.
Use case overview
B2B organizations face a fundamental analytics challenge: their customers are not individual people but accounts composed of multiple stakeholders, buying groups, and opportunities. Standard person-based analytics cannot answer questions like “Which accounts are most engaged?”, “How complete are our buying groups?”, or “Which marketing touchpoints drive opportunity progression?”
B2B Analytics addresses this by leveraging CJA B2B Edition to create account-centric analytical views that combine person-level behavioral data with account, opportunity, and buying group dimensions. RT-CDP B2B Edition provides the underlying account profile unification and B2B identity resolution that feeds the analytics layer. Together, these solutions enable organizations to build cross-channel journey analysis at the account level, correlate marketing engagement with pipeline progression, and deliver actionable insights to both marketing and sales teams.
The target audience includes B2B marketing operations teams, demand generation leaders, revenue operations analysts, and sales leadership who need visibility into account-level engagement and pipeline health.
Key business objectives
The following business objectives are supported by this use case pattern.
Improve analytics & reporting
Enhance reporting capabilities for faster, more actionable marketing insights through unified dashboards and self-service tools. B2B Analytics enables organizations to consolidate account-level engagement data from multiple sources into a single analytical environment, providing cross-channel visibility into how marketing programs influence pipeline and revenue.
KPIs: Efficiency, Productivity
Learn more about improving analytics & reporting
Enable data-driven decision making
Empower teams with self-service analytics, real-time customer insights, and AI-powered predictions to guide strategy. Account-level analytics equip marketing and sales teams with the data needed to prioritize accounts, optimize engagement strategies, and align on pipeline opportunities.
KPIs: Efficiency, Productivity
Learn more about enabling data-driven decision making
Improve lead qualification & conversion
Increase lead quality and accelerate pipeline progression through scoring, nurturing, and personalized follow-up. CJA B2B Edition provides extended 13-month account lookback windows specifically designed for B2B sales cycles, enabling accurate multi-touch attribution across the full account journey.
KPIs: Efficiency, Incremental Revenue
Example tactical use cases
The following scenarios illustrate how this pattern can be applied in practice.
- Account engagement scoring analysis – Measure and rank accounts by their aggregate engagement across web, email, events, and content interactions to identify high-intent accounts for sales follow-up
- Buying group completeness tracking – Analyze buying group composition across accounts to identify gaps in role coverage and prioritize lead acquisition for incomplete buying groups
- Opportunity pipeline correlation – Correlate marketing engagement data with opportunity stage progression to understand which campaigns and touchpoints drive pipeline advancement
- Multi-touch B2B attribution – Apply attribution models with 13-month lookback windows to credit marketing touchpoints across the full B2B buying journey from first touch to closed-won
- Account journey mapping – Visualize the cross-channel account journey from initial awareness through opportunity creation and close, identifying common paths and friction points
- Campaign influence on pipeline – Measure how specific campaigns influence account pipeline creation, opportunity advancement, and revenue generation
- Buying group engagement progression – Track how buying group engagement scores evolve over time and correlate engagement thresholds with opportunity outcomes
- Account-based content performance – Analyze which content assets and topics resonate with specific account segments, industries, or buying group roles
- Sales and marketing alignment dashboards – Build shared dashboards that provide both marketing and sales teams with a unified view of account engagement, pipeline health, and revenue attribution
- Account segmentation for activation – Create B2B segments based on account-level analytics (for example, “highly engaged accounts without open opportunities”) and publish them for downstream activation
Key performance indicators
The following KPIs help measure the success of this use case pattern.
Use case pattern
B2B analytics
Include B2B account-level information in cross-channel customer journey analysis.
Function chain: B2B Data Connection > Account Data View Configuration > Workspace Analysis > Dashboard Publishing
Applications
The following applications are used to implement this use case pattern.
- Customer Journey Analytics B2B Edition – Provides account-based connections, B2B-specific data view containers, account-level workspace analysis, buying group analysis, opportunity analysis, B2B segmentation, and B2B attribution with extended lookback windows
- Real-Time CDP B2B Edition – Provides the B2B data foundation including account profile unification, B2B identity resolution, B2B schema classes (Account, Opportunity, Buying Group), and Marketo Engage integration for ingesting B2B engagement data
Foundational functions
The following foundational capabilities must be in place for this use case pattern. For each function, the status indicates whether it is typically required, assumed to be pre-configured, or not applicable.
Supporting functions
The following capabilities augment this use case pattern but are not required for core execution.
Application functions
This plan exercises the following functions from the Application Function Catalog. Functions are mapped to implementation phases rather than numbered steps.
Customer Journey Analytics B2B Edition
Customer Journey Analytics – standard functions
Real-Time CDP B2B Edition
Prerequisites
The following items must be in place before implementation begins.
- [ ] CJA B2B Edition license is active and provisioned for the organization
- [ ] RT-CDP B2B Edition license is active with B2B schemas and account profiles configured
- [ ] B2B XDM schemas are defined (Account, Opportunity, Account Person Relation, Opportunity Person Relation, Marketing List Members)
- [ ] Marketo Engage and/or CRM source connectors are configured and actively ingesting data
- [ ] Account-level behavioral event data (web visits, email interactions, form submissions) is flowing into AEP with account association
- [ ] Person-to-account relationships are established in the identity graph
- [ ] At least 30 days of historical B2B engagement data is available for meaningful analysis
- [ ] Stakeholders have agreed on buying group role definitions and solution interest mappings
- [ ] CJA user accounts are provisioned with appropriate product profiles for B2B Edition features
- [ ] Target KPIs and reporting requirements have been defined by marketing and sales leadership
Implementation options
The following sections describe different approaches for implementing this use case pattern.
Option A: Account-centric analytics
Best for: Organizations that want to analyze all engagement and pipeline data through the lens of the account. This approach uses the Account container as the primary analytical unit, providing a top-down view of how accounts progress through the buying journey.
How it works:
Configure a CJA B2B connection with Account as the primary identifier. All behavioral events, opportunities, and buying group data roll up to the account level. The data view uses Account as the highest-level container, with Person, Session, and Event nested within it. This enables analysis like “How many accounts visited the pricing page and then created an opportunity within 30 days?”
Account-centric analytics provides the most natural view for B2B organizations where the account is the unit of purchase. Dimensions like industry, company size, account tier, and account owner can be used to break down engagement patterns and pipeline metrics. Attribution is applied at the account level with 13-month lookback windows that accommodate long B2B sales cycles.
Key considerations:
- Requires clean account-to-person mapping in the identity graph
- All person-level events must be attributable to an account
- Anonymous web traffic that cannot be associated with an account will not appear in account-level analysis
Advantages:
- Provides a true account-level view of the entire buying journey
- Enables account-based attribution that matches how B2B revenue is generated
- Supports buying group and opportunity analysis as nested containers within the account
- Aligns analytics with account-based marketing (ABM) strategy
Limitations:
- Requires robust person-to-account identity resolution
- Anonymous or unmatched engagement data is excluded from analysis
- More complex to configure than person-centric analytics
Experience League:
Option B: Global account-centric analytics
Best for: Enterprise organizations with complex account hierarchies where a parent company has multiple subsidiary accounts. This approach uses Global Account as the primary identifier, rolling up all subsidiary account activity to the parent organization level.
How it works:
Configure the CJA B2B connection with Global Account as the primary identifier instead of Account. This aggregates engagement data from all subsidiary accounts under their parent organization. For example, if “Acme Corp” has regional subsidiaries “Acme EMEA” and “Acme APAC,” a Global Account connection consolidates all engagement from all three entities into a single analytical view.
The data view includes Global Account as the top-level container, with Account, Person, Session, and Event as nested containers. This enables analysis at both the global and subsidiary account levels within the same workspace project. Attribution lookback windows apply at the global account level, capturing all touchpoints across the entire corporate hierarchy.
Key considerations:
- Requires account hierarchy data with parent-child relationships defined in the B2B data model
- Global Account ID must be populated and accurate across all account records
- Subsidiary accounts must be correctly mapped to their parent
Advantages:
- Provides consolidated visibility across complex enterprise account structures
- Prevents fragmented analysis when a single enterprise customer has multiple account records
- Enables comparison between regional subsidiaries within a single global account
- Supports enterprise-level pipeline and revenue reporting
Limitations:
- Requires accurate account hierarchy data, which many organizations struggle to maintain
- May obscure subsidiary-level patterns when viewed only at the global level
- Additional data modeling effort to establish and maintain hierarchy relationships
Experience League:
Option C: Hybrid person + account analytics
Best for: Organizations transitioning from person-based analytics to account-based analytics, or those that need both person-level and account-level views. This approach creates two data views from the same connection – one person-centric and one account-centric.
How it works:
Configure a single CJA B2B connection that includes all B2B datasets (event, account, opportunity, buying group, person-account relations). Then create two data views: one using Person as the primary container (similar to standard CJA) and one using Account as the primary container. Analysts can switch between data views depending on the question being asked.
The person-centric data view provides traditional individual-level journey analysis (for example, “What is the conversion path for leads who become opportunities?”), while the account-centric data view provides organization-level analysis (for example, “Which accounts have the highest engagement-to-pipeline conversion rate?”). Both views use the same underlying data, ensuring consistency.
Key considerations:
- Requires two data views with potentially different dimension and metric configurations
- Analysts need training on when to use each view
- Computed metrics may need to be created separately for each data view
Advantages:
- Provides flexibility to analyze data at both the person and account level
- Easier adoption path for teams accustomed to person-level analytics
- Enables comparison between person-level and account-level metrics
- Supports both lead-based and account-based marketing strategies
Limitations:
- Two data views to maintain and keep in sync
- Potential confusion for analysts about which view to use
- Computed metrics and filters may need duplication across views
Experience League:
Option comparison
Choose the right option
- Choose Option A if your organization has a flat account structure (no parent-child hierarchies), your ABM strategy operates at the individual account level, and you want the simplest path to account-level analytics.
- Choose Option B if your target accounts are large enterprises with subsidiary accounts across regions or divisions, and you need consolidated reporting at the corporate parent level. This option requires high-quality account hierarchy data.
- Choose Option C if your organization is transitioning from lead-based to account-based marketing, your analysts need both person-level funnel analysis and account-level engagement analysis, or you have a mix of B2B and B2C business lines.
Implementation phases
The following phases outline the recommended implementation sequence.
Phase 1: B2B data connection
Application function: CJA B2B: Account-Based Connection, CJA: Data Connection
Configure the CJA connection that binds your AEP B2B datasets to CJA for analysis. This connection defines which datasets flow into CJA, the primary identifier type (Account or Global Account), and how historical and streaming data are ingested. The connection is the foundation of all subsequent analysis.
Decision: Primary identifier type
Determine whether the connection should use Account ID or Global Account ID as the primary identifier.
Decision: Dataset selection and type designation
Determine which B2B datasets should be included and how each should be typed.
Decision: Backfill range
Determine how much historical data should be imported into the connection.
Configure the B2B data connection
UI navigation: Customer Journey Analytics > Connections > Create new connection
Key configuration details:
- Select the AEP sandbox containing B2B datasets
- Set the average number of daily events for capacity planning
- Add each dataset and designate its type (event, lookup, or profile)
- Configure the person ID field to use Account ID or Global Account ID for B2B connections
- Enable streaming for datasets that require near-real-time updates
- Enable “Import all existing data” for historical backfill
Where options diverge:
For Option A (Account-Centric):
Set the primary identifier to Account ID. Add account record, opportunity, buying group, and person-account relation datasets. Configure person-level event datasets with the Account ID field for cross-dataset joining.
For Option B (Global Account-Centric):
Set the primary identifier to Global Account ID. Ensure account hierarchy data includes the Global Account ID field. All datasets must include or be joinable to the Global Account ID for proper roll-up.
For Option C (Hybrid):
Create a single connection with all B2B datasets. Use Account ID as the primary identifier. The person-centric view will be created in Phase 2 by using a different data view configuration on the same connection.
Experience League documentation:
Phase 2: Account data view configuration
Application function: CJA B2B: B2B Data View Configuration, CJA: Data View Configuration
Configure the data view that defines how connection data appears in analysis. For B2B analytics, this includes configuring B2B-specific containers (Account, Opportunity, Buying Group), mapping B2B schema fields to dimensions and metrics, setting attribution models with B2B-appropriate lookback windows, and creating derived fields for B2B business logic.
Decision: Container configuration
Determine which B2B containers should be enabled and how they should be named.
Decision: Attribution model for B2B metrics
Determine which attribution model should be the default for conversion metrics.
Decision: Session definition for B2B
Determine how sessions should be defined for B2B engagement.
Configure the account data view
UI navigation: Customer Journey Analytics > Data views > Create new data view
Key configuration details:
- Select the connection created in Phase 1
- Configure time zone and calendar type appropriate for the organization
- Rename containers to B2B-relevant terminology (for example, Account/Engagement/Touchpoint)
- Map B2B schema fields to dimensions: account name, account ID, industry, company size, account tier, account owner, opportunity stage, opportunity value, buying group role, solution interest
- Map engagement metrics: events (occurrences), people, sessions, page views, form submissions, email opens, email clicks
- Configure persistence for key dimensions (for example, account industry persists at the account level)
- Set attribution to linear with 13-month lookback as the default for conversion metrics
- Create derived fields for marketing channel classification, engagement scoring tiers, and opportunity stage grouping
Where options diverge:
For Option A (Account-Centric):
Configure a single data view with Account as the top-level container. Include Opportunity and Buying Group containers if pipeline and buying group analysis is needed.
For Option B (Global Account-Centric):
Configure Global Account as the top-level container. Include Account as a sub-container to enable both global and subsidiary analysis.
For Option C (Hybrid):
Create two data views from the same connection. Data View 1 uses Person as the primary container (standard CJA behavior). Data View 2 uses Account as the primary container with B2B containers. Map identical metrics to both views where applicable.
Experience League documentation:
Phase 3: Workspace analysis
Application function: CJA B2B: Account-Level Workspace Analysis, Buying Group Analysis, Opportunity Analysis, B2B Segmentation, B2B Attribution, CJA: Workspace Analysis, Computed Metric Creation, Guided Analysis
Build workspace projects that deliver the analytical insights defined in the KPIs. This phase includes building freeform tables with B2B dimensions and metrics, creating computed metrics for B2B KPIs, configuring B2B-specific visualizations (account-level flow, opportunity funnel, buying group engagement), creating filters/segments using B2B containers, and applying B2B attribution models.
Decision: Analysis workspace structure
Determine how the workspace project should be organized.
Decision: B2B computed metrics
Determine which calculated metrics are needed for B2B KPIs.
Decision: B2B filter/segment scope
Determine at which container level filters should be created.
Build the workspace analysis
UI navigation: Customer Journey Analytics > Workspace > Projects > Create project
Key configuration details:
- Select the B2B data view created in Phase 2
- Build freeform tables with account-level dimensions (account name, industry, tier) broken down by engagement metrics
- Create opportunity funnel visualizations showing opportunity progression through stages
- Build buying group composition tables showing role fill rates and engagement per role
- Configure B2B attribution panels comparing attribution models (linear, U-shaped, time decay) with 13-month lookback
- Create account flow visualizations showing common paths through the buying journey
- Build cohort tables analyzing account retention and re-engagement over time
- Apply B2B filters to segment analysis by account tier, industry, or engagement level
- Create annotations for significant events (campaign launches, product releases, pricing changes)
Experience League documentation:
Phase 4: Dashboard publishing
Application function: CJA: Dashboard & Scorecard Publishing, CJA: Audience Publishing
Create shareable dashboards and mobile scorecards that deliver B2B analytics insights to stakeholders. This phase also covers publishing CJA-defined B2B audiences back to AEP for activation in downstream use cases such as B2B audience activation.
Decision: Dashboard delivery method
Determine how B2B analytics insights should be delivered to stakeholders.
Decision: Audience publishing from CJA
Determine whether B2B segments should be published back to AEP for activation.
Publish dashboards and audiences
UI navigation: Customer Journey Analytics > Projects > Share (for Workspace), Projects > Create > Mobile scorecard (for scorecards), Components > Audiences > Publish (for audience publishing)
Key configuration details:
- Build executive dashboards with summary numbers for key B2B KPIs (total engaged accounts, pipeline value, buying group completeness)
- Configure comparison periods (month-over-month, quarter-over-quarter) for trend indicators
- Create mobile scorecards with tiles for account engagement, pipeline health, and attribution metrics
- Add filters for executives to toggle views by region, industry, or account tier
- Configure scheduled project delivery for weekly executive reports
- For audience publishing: select the B2B filter, configure the identity namespace (Account ID), and set the refresh cadence
Experience League documentation:
Implementation considerations
The following sections cover guardrails, common pitfalls, best practices, and trade-off decisions to keep in mind during implementation.
Guardrails and limits
- CJA connections can include datasets from only one AEP sandbox – CJA guardrails
- Maximum of 5,000 dimensions and 5,000 metrics per data view
- Maximum of 100 derived fields per data view
- B2B attribution supports lookback windows up to 13 months for account-level analysis
- Maximum of 75 published audiences per CJA customer across all sandboxes
- Audience publishing minimum refresh cadence is every 4 hours
- Streaming latency from AEP to CJA is typically under 90 minutes
- Freeform tables support up to 10 dimension breakdowns deep
- Mobile scorecards support up to 16 tiles per scorecard
- Workspace projects support up to 40 panels per project
- Backfill for large B2B datasets (billions of records) may take days to complete
Common pitfalls
- Incomplete person-to-account mapping: If person-level events cannot be associated with an account, they will not appear in account-level analysis. Ensure all event datasets include a field that can be joined to the Account ID, either directly or through a person-account relation lookup dataset. Audit the percentage of events with missing account association before building analysis.
- Incorrect dataset type designation: B2B lookup datasets (opportunity, buying group, person-account relations) must be correctly designated as lookup or profile type in the CJA connection. Mistyping a lookup dataset as an event dataset will produce incorrect results because CJA will attempt to treat each record as a timestamped event.
- Attribution window too short for B2B: Using default 30-day attribution windows will miss early-stage touchpoints in B2B journeys that typically span 6-18 months. Always configure 13-month lookback windows for B2B attribution metrics.
- Mixing account-level and person-level metrics incorrectly: Counting “people” in an account-level analysis can be misleading. Ensure computed metrics are defined at the appropriate container level. An “account engagement rate” should use account-level events divided by accounts, not by people.
- Stale buying group data: Buying group composition and role assignments change over time. If buying group datasets are not refreshed regularly, completeness metrics will be inaccurate. Ensure the source system (Marketo Engage or AJO B2B Edition) is actively syncing buying group data.
- Overloading a single workspace project: B2B analytics spans engagement, pipeline, attribution, and buying groups. Attempting to put everything in one project leads to slow loading and confusing navigation. Use multiple focused projects or clearly labeled panels.
Best practices
- Start with Option A (Account-Centric) even if you plan to use Option B (Global Account) later. Account-centric analytics is simpler and validates your data model before adding hierarchy complexity.
- Create a dedicated “B2B Data Quality” workspace project that tracks the percentage of events with account association, the number of orphaned accounts, and buying group completeness metrics. Run this weekly to catch data issues early.
- Use derived fields to create engagement scoring tiers (High/Medium/Low) based on account-level event counts. This simplifies analysis and makes dashboards more actionable for non-technical stakeholders.
- When configuring B2B attribution, start with linear attribution as a baseline, then compare against U-shaped and time-decay models. The differences between models often reveal whether your marketing mix is weighted toward awareness or conversion activities.
- Publish a “B2B Executive Summary” mobile scorecard with no more than 8 tiles covering the KPIs that matter most to leadership. Keep it focused – executive scorecards should answer “How are we doing?” not “Why?”
- Annotate key events (product launches, major campaign launches, pricing changes, sales process changes) to provide context for data trends. B2B data often shows spikes and dips that are unexplainable without event context.
- When publishing B2B audiences from CJA, use daily refresh for standard activation segments and 4-hour refresh only for time-sensitive segments. Frequent refreshes consume processing resources.
Trade-off decisions
Data completeness vs. analytical accuracy
Including all person-level events in account analysis (even those with weak account association) improves data completeness but may reduce analytical accuracy if the account mapping is unreliable.
- Including all events favors: Comprehensive engagement measurement, higher account engagement scores, broader visibility
- Excluding unmatched events favors: Accurate account-level metrics, trustworthy attribution, reliable pipeline correlation
- Recommendation: Exclude unmatched events from account-level analysis but include them in a separate person-level data view (Option C) to understand the full picture. Prioritize improving account association data quality as a parallel workstream.
B2B attribution lookback window length
Longer lookback windows (13 months) capture more touchpoints but may include marketing activities that are no longer relevant to the current buying decision.
- Longer lookback (13 months) favors: Capturing the full B2B journey, crediting awareness-stage activities, accommodating long sales cycles
- Shorter lookback (6 months) favors: Focusing on recent engagement, reducing noise from old touchpoints, better reflection of current buying intent
- Recommendation: Use 13-month lookback for enterprise accounts with long sales cycles (12+ months). Use 6-month lookback for mid-market accounts with shorter cycles. Create separate computed metrics for each window to compare.
Single comprehensive data view vs. multiple focused data views
One data view with all B2B containers and dimensions is simpler to maintain but may overwhelm analysts with complexity. Multiple focused data views (engagement, pipeline, attribution) are easier to use but harder to maintain.
- Single view favors: Consistency, easier maintenance, cross-domain analysis within a single project
- Multiple views favors: Simplicity for analysts, faster loading times, tailored component lists per use case
- Recommendation: Start with a single comprehensive data view. If analysts report difficulty finding the right dimensions and metrics, create curated component groups within the same view before splitting into multiple views. Use workspace templates to guide analysts to the right components.
Related documentation
The following resources provide additional information for implementing this use case pattern.
CJA B2B Edition
Connections
Data views
Workspace and analysis
Components
Audiences
Dashboards and scorecards
Guided analysis
RT-CDP B2B Edition
AEP data foundation
Data governance and lifecycle
Tutorials and guides