Problem
The client’s primary sales platform was Salesforce, but as an enterprise their customer and sales data was distributed across multiple systems including Snowflake, Oracle, a separate billing system, and others. This fragmentation made it difficult to bring insights together into a single view of each prospect. They needed a way to unify data from all these systems and generate a custom lead score inside Salesforce so that sales teams could prioritize leads more effectively and align outreach with real conversion potential.
Approach
We engaged both technical and business stakeholders to fully understand the requirements and finalize a single data hub where all lead-related data could be consolidated. This enterprise data platform served as the foundation for combining inputs from Salesforce, Snowflake, Oracle, billing, and other systems. After unifying the data, we profiled and explored it to identify the most predictive signals and designed a machine learning model, with gradient boosting ranking chosen for its balance of accuracy, interpretability, and performance at scale. We benchmarked the model against Salesforce’s auto-generated lead score and found it delivered a 4x improvement in predictive accuracy.
Solution
Beyond training the model, we engineered a production-ready system capable of running at enterprise scale. We implemented a managed feature store to ensure parity between offline experimentation and online inference, and set up pipelines for automated data ingestion and model retraining. Model serving was deployed on Kubernetes to handle high availability and low-latency scoring, with storage managed in cloud object buckets. We integrated observability through centralized logging, metrics, and alerts, ensuring continuous monitoring and reliability. Finally, we brought all of this directly into Salesforce by using custom Apex logic: when a lead was created or updated, a Queueable Apex process triggered an external call to score the lead, and the resulting score was written back into a custom Salesforce field. This seamless integration allowed sales teams to act immediately on high-quality leads with complete confidence in the score.
Impact
- Achieved a 4x improvement in lead scoring accuracy compared to Salesforce’s default scoring, enabling sales teams to focus efforts on the most promising prospects.
- Improved conversion rates and sales efficiency by unifying fragmented enterprise data into a single actionable score within Salesforce.
- Increased adoption and trust in the scoring process across sales and marketing teams.
- Delivered a fully scalable enterprise-grade ML deployment with automated pipelines, monitoring, and retraining, reducing manual intervention and future maintenance overhead.