Prep 601: Breaking Down B2B Data Silos: Transform Marketing, Sales & Customer Success into a Revenue

Don't break down silos, just unify data, and turn every customer interaction into a growth opportunity.

Fragmentation in B2B is Inevitable—But Can It Be a Strategic Advantage?

For CIOs and data strategy leaders, managing multiple marketing, sales, and customer success operations can seem inefficient, with separate Marketo, CRM platforms (Salesforce, Microsoft Dynamics), and customer success platforms (ServiceNow, Gainsight, Zendesk) often viewed as unnecessary complexity. The common instinct is to consolidate, but in large enterprises, fragmentation is unavoidable. I’ve seen this firsthand—attempting to force uniformity across business units can slow down operations rather than streamline them. The real challenge is not eliminating fragmentation but managing it effectively.

Here’s why:

• Independent Business Units – Different teams have unique sales, customer success, and engagement strategies. While they should align with the company’s broader goals, they also need the flexibility to operate independently.

• Diverse Sales & Success Models – Many companies run direct sales, channel sales, and partner-driven revenue models, each requiring distinct lead, customer health, and account management structures. A single system often cannot accommodate all these needs.

• Global Expansion & Compliance – Regulations like GDPR and CCPA require localized data storage and processing. For geographically distributed companies, maintaining separate CRM, customer success, and marketing automation environments isn’t a choice—it’s a necessity.

• Mergers & Acquisitions – Acquired companies bring their own CRM, marketing automation, and customer success platforms, and immediate system consolidation is rarely feasible. To keep the business running, multiple systems must operate in parallel until optimization is possible.

What I have come to realize is that fragmentation isn’t a problem to be solved—it’s an advantage when managed correctly. Instead of trying to force consolidation, flip the problem around. Fragmentation provides agility, decentralization, and focus. Marketing, sales, and customer success teams can move fast and adapt while you, as the data strategy leader, ensure that data remains unified, providing a common view across all instances. The next time your team is out on the road, you’re not fighting fragmentation—you’re making it work for you.

Managing Fragmentation with a Strategic Data Layer

One tendency I’ve seen in these situations is the urge to solve for immediate needs, such as lead routing or contact consolidation, without considering the long-term impact. The assumption is that as long as data flows smoothly between marketing, sales, and customer success systems, everything will work out. However, the reality is that no matter what middleware technology you use—whether traditional ETL or APIs for integration—you are leaving significant opportunities on the table.

Since multiple applications are involved, the common approach is to introduce middleware architectures that facilitate point-to-point transfers between systems. While this ensures basic connectivity, it fails to create a unified data foundation that provides intelligence, automation, and long-term adaptability. Instead of just moving data between systems, the focus should be on building a strategic data layer that harmonizes fragmented systems, enables advanced insights, and gives teams a competitive edge. By taking this approach, businesses gain long-term agility to support new use cases, adapt to evolving customer engagement models, and ensure that data is not just transferred—but leveraged as a core asset for growth.Let me highlight each of tyhe problems below and how you need to start thinking about it.

Just Accurate Lead Routing Across Marketo & CRM?

If you want to architect proper lead routing, the first step is ensuring that inbound leads are assigned to the right Marketo instance based on geography, product interest, or business unit. This allows sales teams to engage with the right prospects efficiently.

This isn’t a hard problem to solve. All you need is an enterprise-grade distributed system that provides full auditability and traceability of leads. No matter what data pipelines you use, the key is ensuring that every lead is captured, tracked, and routed with complete visibility.

Wait. Not so fast.

When a lead submits a form, it’s not just a contact entry—it’s a signal of interest. But before routing it to the right Marketo instance, there’s an opportunity to enrich and validate the data using your first-party assets.

• Context & Intent Detection – The moment a lead submits a form, chances are they have been browsing your website. Their behavior—pages visited, time spent, and actions taken—can provide critical context about their intent. Instead of treating the form submission in isolation, you should be pulling in this behavioral data to enrich the lead profile before routing.

• Recognizing Returning Leads – What if this isn’t a net-new lead but someone you’ve engaged with before? Maybe they attended a webinar, downloaded a whitepaper, or even made a purchase. Instead of treating them as a fresh lead, it’s valuable to retrieve their historical interactions across marketing and sales touchpoints to determine the right engagement strategy.

• Data Quality & Resolution – Leads don’t always fill out forms accurately—typos, missing fields, and inconsistent inputs are common. But if this lead has engaged with you before, you likely already have clean, validated data from past interactions. Instead of allowing bad data to propagate, you should apply automated data correction techniques based on previously verified entries, ensuring better lead quality before it moves through the pipeline.

Same Contact, Multiple Records? Fixing Identity, Sync, and Duplication Across Systems

But wait—what if multiple contact records for the same person exist across Marketo, CRM and the customer success systems? These systems are supposed to stay in sync, but in reality, discrepancies happen. Here are some aspects you need to account for in the data layer:

• Cross-System Identity Resolution – A single person may have multiple records due to different data sources, form submissions, or manual entries. Before routing a lead, it’s critical to match it against existing contacts in both Marketo and CRM system using deterministic (email, phone number) and probabilistic (name variations, company affiliation) identity resolution techniques.

• De-Duplication – When a lead enters the system, it shouldn’t be treated as a new entry until checked against existing records. If a match is found, the system should merge relevant data, and add/update missing fields.

• Syncing & Conflict Resolution – Even if Marketo and CRM systems are integrated, data drift is inevitable. One system may have updated contact details, while the other still holds outdated information. A rules-based approach should determine the source of truth—whether it’s the most recent update, the most reliable data source, or a blend of both.

• Lead Assignment with Awareness – If duplicate leads are not properly managed, sales teams may unknowingly engage with the same prospect from different instances, resulting in redundant outreach, conflicting messaging, and a poor customer experience. Even if the business allows multiple teams to contact the same prospect, there must be visibility into these interactions to prevent misalignment. A well-structured lead management system should provide real-time awareness of ongoing engagements across instances, enabling teams to coordinate outreach efforts effectively. This requires cross-instance data synchronization, shared activity logs, and clear ownership tracking to ensure that every engagement is informed, intentional, and aligned with the overall customer journey.

Don't Forget Customer Success: Turn Your Data into Retention & Growth

While marketing and sales teams rely on unified data for lead generation and conversion, customer success teams play a crucial role in retention, expansion, and long-term customer engagement. However, fragmentation across customer success platforms (ServiceNow, Gainsight, Zendesk), CRM systems, and marketing automation tools leads to gaps in customer visibility, missed renewal opportunities, and inconsistent customer experiences.

What if we integrate customer success into the data strategy, connect post-sale engagement with marketing and sales efforts using this data foundation. Opportunities abound:

1. Customer Health Scoring & Retention Alerts – Health scores based on product usage, engagement signals, and support interactions help predict churn and trigger proactive outreach by marketing.

2. Seamless Handoff to Customer Success – Synchronizing opportunity details, buying group engagement, and product interests ensures CSMs have a full view of customer expectations from day one.

3. Expansion & Upsell Opportunities – A unified account view helps identify cross-sell and upsell potential, enabling targeted account-based re-engagement campaigns to drive growth.

Reporting, But Different: Unlocking Cross-Product Insights at the Account Level

You don’t just need reporting—you need cross-product insights to understand how marketing and sales efforts translate into revenue at an account level.

• Consolidated Account-Level View – Your customers don’t just interact with one product or one sales unit. To get a true picture of engagement, you need a unified view that shows which products are selling in which accounts, which marketing efforts are influencing pipeline growth, and how different business units contribute to revenue. This requires aggregating data across multiple CRM and marketing automation systems while preserving account hierarchies.

• Marketing Impact Measurement – Without cross-instance visibility, it’s difficult to attribute marketing efforts accurately. You need to correlate campaign data from different Marketo instances with closed deals in the CRM system, ensuring you can measure ROI at both the product and account levels.

• Data Normalization Across Instances – Different teams may track leads and sales in slightly different ways—some may use different lifecycle stages, custom fields, or campaign structures. Before cross-product analysis can be effective, data must be standardized to allow apples-to-apples comparisons.

Can Marketing and Sales Work Better Together in an Account-Based World?

If we’ve brought account-based reporting and analysis into the discussion, it’s clear that marketing and sales teams can collaborate more effectively by targeting buying groups rather than treating leads in isolation. The traditional lead-based marketing approach often overlooks the reality of B2B decision-making, where multiple stakeholders influence a deal. The real opportunity lies in shifting from individual lead-centric marketing to a strategy where marketing actively supports sales by engaging the right individuals within an opportunity—not just as disconnected contacts, but as part of a structured sales process.

This shift is only possible with a centralized data layer that unifies account, contact, and opportunity data across multiple systems. With real-time identity resolution and data synchronization between Marketo, CRM, and customer success platform, marketing can map interactions at an account level, track engagement across the buying group, and enable sales teams with precise insights. Instead of generic outreach, marketing efforts can be aligned with sales opportunities, helping to accelerate deal cycles, improve personalization, and ultimately drive higher conversion rates. The key enabler here isn’t just better reporting—it’s a cohesive data strategy that bridges marketing and sales to drive opportunity-based engagement.

How Sales Can Leverage Centralized Data

If all this data is being centralized, why should only marketing benefit? Sales teams can gain just as much, if not more, from consolidated insights across accounts, buying groups, and past interactions. By analyzing this data, sales leaders can answer critical questions: Which sales reps are performing best? What are the most successful engagement strategies? How should we structure our teams for maximum efficiency? With a unified dataset, it's possible to break down performance by geographies, product suites, deal sizes, and conversion rates, identifying strengths and gaps in the current sales approach.

Beyond retrospective analysis, this data can also drive forward-looking strategy. Instead of relying on gut feel, leadership can use data-driven insights to determine territory planning, account prioritization, and quota allocation for the next year. Are certain geographies underserved? Is one product suite consistently outperforming others? Are certain industries showing higher engagement? These insights can shape sales motions, incentive structures, and resource allocation, ensuring that both marketing and sales are operating from the same intelligence and optimizing their approach in tandem.

Whatever Happened to Governance and Privacy? Balancing Flexibility with Control

And needless to say - from a privacy and governance perspective, a multi-instance architecture offers flexibility but also introduces challenges due to fragmentation. To prevent conflicts, it’s critical to establish clear data ownership policies by standardizing field mappings, maintaining consistent lead-to-account relationships, and ensuring seamless synchronization across systems. Compliance becomes even more complex with multiple Marketo and CRM instances, requiring strict adherence to regulations like GDPR and industry-specific standards without disrupting business operations.

All This Data and No AI? It's Time to Do More with First-Party Data

Traditionally, B2B has relied on low-volume, high-value first-party data, but in today's landscape, that data is gold—and it's time to extract more value from it. First, real-time first party data capture is no longer optional; it’s a necessity. You need to harness incoming data streams and activate them instantly, whether by sending enriched audience segments to LinkedIn for targeted ad campaigns or dynamically personalizing website experiences with relevant offers.

But AI in B2B isn’t new—many organizations already use lead scoring, predictive engagement models, and out-of-the-box AI capabilities. The real question is, how do you move beyond standard AI models and introduce customization without building a large-scale data science team or incurring excessive infrastructure costs? This is where modern AI-driven data platforms can help. Instead of relying solely on predefined AI models, businesses should explore low-code AI solutions, fine-tuned predictive models, and embedded intelligence that work within their existing marketing and sales ecosystems. The goal isn’t to build a full AI lab—it’s to apply AI where it moves the needle, leveraging your first-party data to drive smarter targeting, better lead prioritization, and deeper personalization at scale.

What I’ve outlined above is a roadmap from a product management perspective on how to design an architecture that maximizes distribution for flexibility while ensuring centralized visibility and control. The goal is to allow different teams and systems to operate independently while maintaining a unified data strategy that enables seamless collaboration, insights, and decision-making.

What Were We Thinking When We Started Building Adobe Experience Platform for B2B Marketing and Sales Flows?

Adobe Experience Platform (AEP) was initially built with a B2X-first approach, focusing on scalable batch data processing and real-time infrastructure to power personalization at an unprecedented scale—serving trillions of experiences across some of the world’s largest brands. The goal was to enable real-time customer profiles, intelligent segmentation, and cross-channel activation to drive seamless consumer experiences.

Data Unification Layer

When we shifted our focus to B2B marketing and sales flows, it became clear that the landscape was fundamentally different from B2C. While data volume was lower, its strategic importance was higher, requiring personalization and segmentation at a deeper level within the batch data foundation. Unlike B2C, where real-time engagement is typically driven by individual user behaviors, B2B operates in a highly fragmented ecosystem of CRM, marketing automation, and sales engagement platforms, each with custom workflows, unique data models, and varying degrees of IT involvement.

To address these challenges, we needed to build a foundational data model that aligned with the core B2B entities—Persons, Accounts, and Opportunities—while allowing for real-time enrichment and account-based native segmentation. The goal wasn’t just to mirror Marketo workflows but to enhance them by introducing greater flexibility in data ingestion, attribute augmentation, and segmentation logic. By leveraging real-time data feeds and rules-based enrichment, we could surface high-value segments dynamically, enabling marketers to orchestrate personalized experiences across Marketo and other downstream systems. This approach not only preserved the structured automation that Marketo users rely on but also added a layer of intelligence and adaptability that could better support the complex, multi-stakeholder nature of B2B sales cycles.

Platform with Customization

B2B workflows are highly customized based on industry, go-to-market strategy, and sales motion (direct, channel, or partner-driven). Unlike B2C, where automation is largely marketer-driven, B2B relies heavily on IT teams to manage integrations, data flows, and governance across multiple instances of Marketo, CRM, and other customer success tools. This means that any scalable solution must offer deep configurability, flexible routing logic, and support for both real-time and batch orchestration to accommodate the diverse operational models that enterprises rely on.

Account-Based Marketing as a First Class Feature

One of the key insights we’ve gained from working with Marketo customers is that the real opportunity lies in constructing a holistic view of leads within an account, rather than treating them in isolation. B2B decision-making is driven by buying groups, not individual leads, making it critical to map, segment, and engage accounts cohesively.

More importantly, the goal isn’t just to understand individual accounts, but to identify and replicate marketing, sales, and customer success strategies for similar accounts following recognizable patterns. To enable this, we had to augment the data foundation with account-based audiences, ensuring that segmentation, activation, and reporting natively support account-level engagement rather than relying on lead-based approximations.

Buying Groups and Their Collective Power

In B2B, buying groups—rather than individual leads—drive decision-making, which adds complexity to data consolidation and activation. Different teams across marketing, sales, and customer success may engage with the same account but operate in separate systems, each with its own source of truth. This fragmentation makes data unification, identity resolution, and synchronization across multiple platforms a critical challenge.

Marketo is a Rockstar with Adobe Experience Platform

A key insight was that nearly all of our B2B customers rely on Marketo, and marketers appreciate the workflow-driven automation it provides. Rather than replicating Marketo’s architecture, the more effective approach was to augment it, not just for Marketo and CRM users, but also for businesses operating across multiple, disconnected systems that often fall out of sync. We built a mechanism to connect to multiple Marketo instances and have that data ingest into our system. On the outbound side, we scaled up the activation to support augmented/complex audiences to Marketo and to other enterprise systems such as warehouses and Linkedin.

This meant that Adobe Experience Platform (AEP) needed a highly customizable batch processing layer capable of integrating AI/ML-driven data enrichment, cross-system identity resolution, and advanced reporting. At the same time, real-time data ingestion was crucial—not just for immediate activation, but also to derive enriched attributes from behavioral insights, helping sales and marketing teams personalize engagement more effectively.

Additionally, maintaining a historical view of data across multiple Marketo instances is going to be essential for enterprises that operate in multi-instance, multi-region, or multi-product setups. The scale and complexity of this challenge made it clear: we needed a data foundation layer that could unify and synchronize data across marketing and sales systems. This approach ensures that teams using Marketo, CRM platforms, and other tools can continue working within their preferred systems while leveraging a single, reliable source of truth to drive smarter engagement and better decision-making.

Consolidated Reporting and AI/ML

Additionally, our platform had to support comprehensive reporting, advanced analytics, and seamless integration with external BI tools. This includes the ability to generate custom reports and build AI/ML models for lead scoring, attribution, lead-to-account matching, and intent analysis—all within a single, unified data foundation. More importantly, our approach was to reduce dependence on expensive AI/ML infrastructure and large data science teams, making advanced intelligence more accessible and cost-effective.

And Once Again – Real-Time Activation Comes Along for the Ride?

Yes. But it’s not just about activation—it’s also about real-time segmentation and personalization, both on your website and across external streaming systems like LinkedIn. With a unified data foundation, you can dynamically segment audiences based on real-time intent signals, ensuring that personalized experiences are delivered at the right moment across multiple engagement channels.

Privacy and Governance

The system has been designed with a privacy-first mindset, ensuring compliance with GDPR, data hygiene, and governance requirements across multiple systems. This includes automated deletion handling for GDPR compliance, data retention policies, and consent management to enforce user preferences. Additionally, field-level access controls provide granular restrictions on data export, segmentation, and usage. To address regional data privacy concerns, sandboxing capabilities allow organizations to isolate datasets based on geographic requirements, ensuring compliance while maintaining operational flexibility.

Use Case Guide & Capability Matrix

The solution we will leverage in our use case and capability guide will be the following:

• Adobe Real-Time Customer Data Platform (B2B Edition) referred to as B2B CDP below.

• Adobe Journey Optimizer (B2B Edition) referred to as AJO B2B below.

• Adobe Target

• Data Distiller

• Adobe Experience Platform referred to as AEP below.

• Marketo

At the highest level, Adobe Experience Platform (AEP) Data Lake serves as the centralized repository where Marketo, CRM systems, and customer success platforms ingest data. Data Distiller acts as the processing engine, responsible for cleaning, unifying, resolving identities, and generating datasets that populate core B2B CDP tables (Accounts, Persons, Opportunities).

Merge Policy within B2B CDP determines the golden dataset, prioritizing data for account-based segmentation based on downstream activation needs. Adobe Experience Platform Data Collection captures real-time data, routing it via Edge Network to B2B CDP, enabling real-time personalization and activation to various destinations.

Account-based audiences within B2B CDP are leveraged to orchestrate buying group journeys using Adobe Journey Optimizer (AJO), ensuring coordinated engagement across marketing, sales, and customer success teams. Since the data is processed and available on the Edge, real-time personalization is also possible through Adobe Target, allowing businesses to deliver tailored experiences dynamically based on buying group behavior and intent signals.

Beyond data transformation, Data Distiller provides reporting and an AI/ML engine, allowing businesses to extend and customize out-of-the-box (OOTB) reporting and AI/ML models, ensuring tailored insights and predictive capabilities that enhance B2B marketing, sales, and customer success strategies.

Use Case	Description	What to Use
Lead Capture, Processing, Routing & Synchronization	Leads are routed dynamically based on predefined logic to ensure they are assigned to the appropriate Marketo instance. Subsequently, contact records are enriched, deduplicated, and synchronized to maintain consistency across systems	Capture, normalize, and sync real-time lead interactions using AEP Data Collection, Standardize and orchestrate using Data Distiller. Use B2B CDP Activation to route and send to Marketo instances. Every lead is logged and tracked from web capture to activation, ensuring complete auditability and compliance.
Data Quality & Resolution	Leads often submit forms with typos, missing fields, or inconsistent data. However, if they have engaged previously, their validated data can be leveraged to enhance accuracy. Instead of allowing bad data to propagate, automated data correction and enrichment techniques should be applied to ensure high-quality lead data before it moves through the pipeline.	Utilize Data Distiller for automated data validation, deduplication, and correction based on historical records. Use B2B CDP identity resolution to unify and enhance lead profiles before activation. Leverage AEP Data Collection to capture and refine incoming data in real time, ensuring clean and structured records for seamless integration into Marketo and CRM systems.
Contact Standardization	Ensuring consistency in contact data across multiple systems by standardizing fields, formatting, and identifiers to improve lead-to-account matching and eliminate duplicates.	Clean, deduplicate, and enrich contact data using advanced algorithms in Data Distiller. Utilize Contact Profiles to generate standardized versions through the merge policy feature in B2B CDP, enabling seamless activation or direct export via B2B CDP segmentation for downstream marketing and sales workflows.
Data Mining for Enrichment	Analyzing lead trends, website interactions, opportunity, and deal data to uncover key attributes that best describe both the lead and the account. for deeper segmentation, improved targeting, and better personalization for marketing and sales strategies.	Data Distiller Derived Attributes
The Golden Record - Common Data Model Foundation	Standardize accounts, persons and opportunities schemas across multiple Marketo, CRM and customer success instances along with customization for each sales or marketing team.	Data Distiller, B2B CDP Account & Person Profile along with merge pollicies to create variations for different teams
Account-Based Segmentation	If sales involves multiple stakeholders, traditional lead-based marketing will isolate contacts, misaligning marketing and sales efforts leading to slower deal cycles. To improve collaboration, segmentation, shift to accounts, requiring unified account, contact, and opportunity data across systems.	Use Data Distiller to unify disparate contact records under the correct account. Leverage B2B CDP to segment audiences based on engagement, firmographics, and intent signals. Activate these segments using B2B CDP Activation to push insights into Marketo, LinkedIn, and B2B Journey Optimizer, ensuring that marketing and sales efforts are aligned for coordinated engagement.
Buying Groups	Use buying groups—rather than individual leads within an account—to drive decision-making. Marketing, sales, and customer success teams may engage with the same account but operate in separate systems with different data sources.	Use B2B CDP identity resolution to unify contacts under the correct account and Data Distiller to deduplicate and enrich records across Marketo and CRM systems. Leverage AJO B2B to orchestrate buying group journeys..
Customer Health Scoring & Retention	Health scores based on product usage, engagement signals, and support interactions help predict churn and trigger proactive outreach by marketing.	Leverage Data Distiller to generate customer health scores, enrich B2B CDP Account and Person profiles with valuable insights, or seamlessly export the scores to your existing CRM, customer success, and analytics systems for proactive engagement and retention strategies.
Automated Synchronization with Marketo, CRM & Enterprise Systems	Ensures leads are assigned correctly within Marketo and prevents duplicate records by synchronizing CRM contact merges back to the B2B CDP.	B2B CDP Activation
Real-Time Personalization,. Activation & Multi-Channel Marketing	Leverage lead data, web event data, and account data, enriched with relevant attributes, to drive targeted outreach across LinkedIn, Google Ads, and B2B Journey Optimizer for optimized marketing activation.	B2B CDP Segmentation (Account or Person) & Activation, Adobe Target offers OOTB AI/ML based recommendations, Adobe Journey Optimizer (B2B), Data Distiller. B2B Prospect Profiles lets you target prospects independently as well.
Machine Learning & Statistical Modeling	Offers feature engineering, predictive scoring, clustering, and AI-driven insights to enhance lead qualification	Leverage AI-driven predictive lead scoring, clustering, and behavioral analysis using out-of-the-box (OOTB) AI capabilities in B2B CDP and custom ML models in Data Distiller and for enhanced lead qualification and segmentation.
Sales and Marketing Collaboration on Account-Based Insights	While marketing benefits from centralized data, sales teams often operate in silos, missing out on valuable insights across accounts, buying groups, and past interactions. Also, enable marketing and sales teams to work together on account-based strategies by providing a unified view of engagement across buying groups.	Leverage Data Distiller’s reporting capabilities to build custom data models and visualize insights using built-in dashboards or by integrating with external BI tools. Additionally, seamlessly embed these insights into existing sales dashboards for a unified view of performance and engagement trends.
Expansion & Upsell Opportunities	Identify cross-sell and upsell potential, enabling targeted account-based re-engagement campaigns to drive growth.	B2B CDP Account and Person Profile. Exploratory analysis using Data Distiller.
Data Activation & Batch Exports to Enterprise Ecosystems	Facilitates structured dataset exports to external analytics platforms in Parquet or JSON formats for seamless ingestion.	Supports batch exports in custom formats for analytics integration with B2B CDP Activation and Data Distiller.
Governance and Privacy Compliance	Establishes data ownership policies, standardizes field mappings, and ensures seamless compliance with GDPR and industry regulations.	Adobe Experience Platform provides privacy, data governance, and compliance across instances by managing and enforcing data controls within the unified data layer, ensuring secure and compliant data handling.
Leveraging Centralized Data and Insights for Sales Strategy	Provides sales teams with account-level insights to optimize strategies based on lead conversion rates, geographies, and product suite performance.	Use Data Distiller, B2B CDP Insights and AJO B2B Insights to get deeper insight into buying behaviors across prosucts, georgraphies, solkutions, industries