Case Study: Supercharging Sales Intelligence at Snowflake Pre-IPO

Abstract
I played a key role in transforming Snowflake's sales operations just ahead of its IPO by cleaning up the CRM, eliminating duplicates, and streamlining vendor tools. I also developed Snowflake's first account propensity score model to automate lead prioritization. By collaborating with sales leadership to align the model with their strategic segmentation, we boosted data integrity, sharpened sales efforts, and played a critical role in supporting the company’s record-breaking IPO.

Background
As Snowflake geared up for its IPO, its sales operations were bogged down by a messy CRM—duplicate accounts, redundant vendor tools, and no clear system to prioritize leads. As the first data scientist on the go-to-market team, I was brought in to clean up the data chaos and create a streamlined, data-driven process to support the sales team in targeting high-value opportunities effectively.

Objectives
Here’s what I set out to accomplish:

• CRM Cleanup & Vendor Streamlining: Reduce operational waste by cleaning up the CRM and simplifying the vendor toolkit.

• Account Prioritization: Build a machine learning model (account propensity score) to rank accounts based on their likelihood to convert.

• Sales Alignment: Align these solutions with sales strategies, ensuring that every lead was evaluated with precision to support a successful IPO.

Deliverables

• CRM Cleanup & Vendor Optimization:
  Led a full audit of the CRM, eliminating duplicate accounts and unnecessary vendor add-ons. Streamlined the vendor ecosystem, narrowing down to two key partners for a more efficient toolkit.

• Machine Learning Model Development:
  Built Snowflake’s first account propensity score model, using historical sales data to automatically prioritize high-value leads. Worked with sales leadership to fine-tune the model, ensuring it aligned with their operational strategies (e.g., hunter-farmer segmentation, enterprise-SMB focus).

• Process Implementation:
  Led a focused, one-week cleanup effort alongside the sales operations team, establishing a data management process that set Snowflake up for ongoing success.

Outcomes

• Enhanced Data Integrity:
  The CRM cleanup significantly improved data quality, reducing wasted time and enabling the sales team to target leads more effectively.

• Optimized Sales Prioritization:
  The account propensity score model automated lead evaluation, letting the sales team focus on the opportunities most likely to convert, driving smarter sales decisions.

• Strategic Impact:
  The data-driven transformation helped Snowflake’s sales teams work smarter, contributing directly to the company’s record-breaking IPO. This project showed the true value of systematic, high-quality data analysis and how it can drive results at every level of an organization.