Algorithmic Approaches to B2B Contacts in B2B CDP and Data Distiller - Unifying and Standardizing Across Sales Orgs

The case study is about a financial company that is facing significant challenges with contact records across various systems, particularly multiple instances of Salesforce, Adobe CDP, and Marketo. The challenges arise from data fragmentation, lack of standardization, duplication, and governance issues, impacting both marketing and sales teams. Here’s an in-depth look at each challenge and how it affects operations, followed by potential solutions for addressing them using SQL on a contact list dataset:

1. Fragmented Data Across Multiple Salesforce Instances: Contacts are stored in multiple Salesforce instances. Some users only have access to a subset of instances, resulting in incomplete data visibility. This limits the ability of users (especially in sales and customer service) to see the full history or context of a contact, impacting customer interactions and leading to missed opportunities.
2. Duplicate Contacts: Duplicate contacts exist within and across multiple instances, creating conflicting data points. Duplicates lead to confusion as different versions of the same contact may contain conflicting details, such as job titles, roles, and companies, which affect marketing and sales targeting.
3. Data Footprint in Adobe CDP: Only a small subset of users has access to the digital footprint data captured in Adobe CDP. Sales and customer-facing teams lack critical insights derived from digital interactions, reducing their ability to personalize engagements effectively.
4. Multiple Email Addresses Per User: Adobe CDP has identified multiple email addresses for some users, merging them into a single user record, but this data is not synchronized back to sales systems. Inconsistent email addresses create a fragmented view in sales systems, which may lead to duplicated outreach or incomplete activity history.
5. Disconnected Marketing Technology Stacks: Multiple marketing stacks (e.g., associated with different instances) are not integrated, preventing a cohesive enterprise-wide campaign view. Marketing messages may become redundant or disconnected as customers are targeted by individual product campaigns instead of a unified brand campaign.
6. Lack of Governance for Contact Creation: Users can create new contacts in core Salesforce, as long as the email is different, even if the contact exists in another instance.This results in scattered records for the same contact across systems, making it challenging to maintain an accurate, centralized customer profile.
7. Multiple Roles for Contacts: Contacts may hold multiple roles across organizations, which impacts the type of messaging they should receive. Inconsistent role-based communication strategies can cause confusion, as the same contact might be messaged for multiple roles within different campaigns, leading to mixed messaging.
8. Tracking Contact Promotions and Role Changes: There’s no enterprise-wide tracking of contacts' role changes or promotions. When a contact transitions to a new role (e.g., promotion or job change), users may inadvertently lose the contact's activity history, limiting the personalization potential for future interactions.
9. Difficulty with Attribution in Marketing: Contacts spread across multiple Salesforce instances make it hard to track and attribute marketing activities accurately. Inaccurate attribution data impacts budget allocation decisions, making it challenging for marketing teams to understand the effectiveness of their campaigns or optimize spending.

Dataset Strategy for Supporting Sales Team-Specific Rules and Marketing-Level Cohesion

When working with diverse sales teams, each with its unique business rules and priorities, a robust dataset strategy must balance the need for individualized algorithms with the overarching goal of enabling marketing to look across all sales organizations cohesively. This strategy ensures that the data remains harmonized at the schema level but allows for flexibility in processing and prioritizing information to suit both localized needs and enterprise-wide insights. Here’s how such a strategy can be designed:

Custom Algorithms for Each Sales Organization

Each sales team operates with specific business rules and requirements. To support this, we implement custom algorithms for processing the harmonized dataset for each sales organization. These algorithms allow for:

• Tailoring how data is aggregated, deduplicated, and prioritized based on the sales team's operational focus.
• Generating attributes unique to that sales organization, such as region-specific metrics or custom scoring models for leads.
• Aligning with local sales strategies while ensuring the outputs conform to a standard schema for cross-organizational interoperability.

For example, Sales Org A might prioritize customer engagement metrics, while Sales Org B focuses on product affinity scores. These differences are captured in their respective datasets.

Harmonization with a Single Schema

Although the datasets for each sales organization are processed with different algorithms, the outputs adhere to a common schema. This standardized schema ensures that attributes across datasets are aligned and comparable. For instance:

• Attributes like email, first_name, and purchase_history remain consistent across all datasets.
• Unique business rules are applied at the processing level but do not compromise the schema's integrity.

This harmonization enables the Profile Store to ingest and manage all datasets seamlessly while retaining each sales organization's specific details.

Ingesting Datasets into the Profile Store

The datasets for each sales organization are ingested into the Profile Store, which serves as the central repository for customer data. Each dataset is preserved as an independent layer within the Profile Store, ensuring that:

• Marketing and sales teams can reference the datasets individually or collectively.
• Data lineage is maintained, enabling traceability of attributes back to their originating sales organization.

Dynamic Data Selection Using Merge Policies

The Profile Store enables dynamic selection of datasets through merge policies, which define how datasets are prioritized and combined:

• Merge Policies by Sales Organization: Individual sales teams can specify merge policies that prioritize their dataset when creating audiences or running segmentation.
• Cross-Organization Merge Policies: Marketing can define merge policies that aggregate and harmonize datasets using Data Distiller across sales organizations, providing a unified view of customer data.

For example, one merge policy might prioritize the most recent dataset from a specific sales org, while another aggregates the highest-value attributes across all sales orgs.

Flexible Segmentation and Personalization Contexts

Merge policies allow for dynamic person audience creation at a granular level. This ensures:

• Sales-Specific Views: Sales teams can work within their own datasets, creating audiences and personalization rules that align with their business priorities.
• Marketing-Level Insights: Marketing can look across all sales organizations by selecting merge policies that harmonize datasets, enabling segmentation and personalization at the enterprise level.

By switching merge policies, teams can seamlessly transition between localized and global perspectives without altering the underlying datasets.

Profile Snapshots for Merge Policies

To ensure operational flexibility and avoid conflicts between teams, Profile Snapshots are created for each merge policy. These snapshots capture:

• The state of the unified profile dataset under a specific merge policy at a given time.
• A static, reproducible dataset for segmentation, analysis, or experimentation without disrupting live datasets.

For example, marketing can generate a snapshot using a cross-organization merge policy for a campaign, while a sales org uses a snapshot of its own dataset for a regional initiative.

A Unified Yet Flexible Dataset Strategy

This strategy allows each sales organization to operate within its unique business rules while maintaining a harmonized data foundation. The use of custom algorithms ensures localized relevance, while the standardized schema and Profile Store enable enterprise-wide cohesion. Merge policies and Profile Snapshots provide the flexibility needed for segmentation and personalization at both the sales and marketing levels. This approach empowers the organization to balance tailored sales strategies with holistic marketing insights, ensuring consistent data integrity, adaptability, and alignment across the business.

Link to the full tutorial here