Identifying New vs Existing Customers
The organization operates across transportation-related services supported by finance and technology capabilities.
Across its business, maintaining a clear understanding of existing customer relationships is important for sales operations. Sales teams need to know whether an incoming lead represents a genuinely new opportunity or an organization that already has a relationship with the business.
That customer identity also provides a foundation for broader analytics and future data initiatives.
The enterprise's existing Salesforce matching process was no longer providing a reliable way to identify existing customer relationships.
When a new lead entered Salesforce, sales representatives could not consistently determine whether the organization was truly new or already part of the customer base.
That created several operational challenges.
Duplicate Customer Records
Incoming leads could create additional customer records instead of connecting back to an existing account.
Over time, this made it more difficult to maintain a consistent picture of the customer base.
Duplicate Sales Outreach
When teams could not easily identify existing customer relationships, multiple sales representatives could potentially contact the same organization.
This created unnecessary duplication across sales activity.
Inconsistent Pipeline Stages
Separate records representing the same organization could appear at different stages in the sales pipeline.
That made it harder for teams to understand the real status of the customer relationship.
Repeated Paperwork and Rework
Existing customers could be treated as completely new prospects.
As a result, teams could repeat processes and paperwork that had already been completed for the organization.
The enterprise needed a more reliable way to match incoming Salesforce records against its existing customer base.
A Core Customer Master in Databricks
LakeFusion established a core customer master in Databricks that enables incoming Salesforce leads and contacts to be compared against a trusted customer foundation.
The system returns a match value indicating whether an incoming lead should connect to an existing customer.
Salesforce remains the primary source, while additional sales and marketing channel data can also flow into Databricks to support the broader customer view.
Rules-Based and AI-Based Matching
LakeFusion combined deterministic matching with AI-based matching rather than relying on one technique for every record.
DOT numbers provide a strong rules-based identifier across the customer dataset.
When a reliable DOT number is available, it can provide a clear deterministic matching signal. AI-based matching is then available for cases that require additional resolution.
This approach allows the organization to use strong existing identifiers wherever possible while reserving more advanced matching for ambiguous records.
How LakeFusion Implemented the Customer Master
The implementation combined data assessment, matching refinement, internal enablement, and Databricks centralization.
Data Profiling and Assessment
LakeFusion began by analyzing the existing Salesforce customer data.
The assessment identified duplicate records and inconsistently staged accounts, providing the foundation for a matching strategy designed around the organization's actual customer data rather than theoretical matching rules.
Match Tuning
Rules-based logic anchored by DOT numbers was combined with AI-based matching.
The matching process was refined to improve how incoming leads were compared against the existing customer master.
Team Enablement
LakeFusion worked closely with the enterprise's technical champion and internal team so they could develop the skills needed to operate and expand the platform themselves.
This helped move long-term platform ownership into the customer's own environment rather than leaving it dependent on an external delivery team.
Databricks Centralization
Customer information was brought together in Databricks.
That means the mastered customer data can support more than the immediate Salesforce matching requirement. The same customer foundation can also support broader analytics, direct querying, natural-language querying, and future transformation initiatives.
Business Value Delivered
The most important outcome is straightforward: sales operations now have a stronger way to determine whether an incoming lead represents a new or existing customer.
That improvement creates value across several areas.
Less Duplicate Outreach
Better customer matching helps reduce situations where multiple sales representatives contact the same organization because the customer relationship was not recognized.
It also reduces unnecessary processes for customers the business already knows.
Clearer Customer Visibility
Centralizing customer information in Databricks creates a more consistent view of the customer base.
Teams no longer need to depend entirely on the previous Salesforce matching process to understand whether records belong to existing relationships.
Broader Analytics Opportunities
The customer master also creates a reusable data foundation for wider analytics and direct querying in Databricks.
Trusted customer identities can support additional analysis without rebuilding the customer view for every new use case.
Foundation for Future Initiatives
The initial sales-matching requirement creates a foundation that can support future transformation initiatives across the business.
Instead of solving duplicate lead matching as an isolated problem, the enterprise now has mastered customer data that can support additional operational, analytical, and AI-driven use cases.
What Made the Initiative Successful
Several factors helped strengthen adoption and create a more sustainable customer-mastering program.
Strong Internal Champions
Support from both leadership and technical stakeholders helped keep the initiative moving while creating internal ownership of the platform.
Existing Identifiers Improved Matching
The organization's existing use of DOT numbers gave the matching strategy a strong deterministic anchor alongside AI-based matching.
This allowed LakeFusion to use reliable business identifiers where available rather than applying AI unnecessarily to every record.
Client-Led Platform Ownership
The enterprise's technical champion actively developed expertise in the platform and assumed responsibility for expanding it within the company's own environment.
This created a stronger foundation for continued adoption and future use cases.
Designed Beyond One Use Case
Bringing customer information together in Databricks meant the project did not have to remain limited to Salesforce lead matching.
The same mastered customer foundation can support analytics, natural-language querying, and future enterprise data initiatives.
From Duplicate Leads to a Trusted Customer Foundation
The enterprise began with a clear sales-operations challenge: incoming Salesforce leads could not be reliably checked against existing customers.
LakeFusion addressed that challenge by building a core customer master in Databricks and combining rules-based matching with AI-based entity resolution.
Today, sales operations have a more reliable way to distinguish new prospects from existing customers, helping reduce duplicate outreach, repeated processes, and unnecessary rework.
At the same time, centralized customer data provides a stronger foundation for analytics and future transformation initiatives across the business.
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