Improving PE Deal Sourcing with Automation Platform

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A leading Private Equity and Venture Capital firm, managing over $20 billion, faced significant challenges with their existing deal sourcing platform. They engaged Blue Orange Digital to conduct an end-to-end data audit, aiming to evolve the platform into a scalable, unified sourcing tool equipped with consistent architecture and advanced data capabilities. The goal was to integrate data from four newly acquired CRM/ERP systems, each previously operating independently, which led to obscured visibility and coordination within the firm’s sales processes.


The firm encountered obstacles including:

  • Data Silos: After acquiring four companies, the firm was burdened by disparate data sets that made it difficult to track and analyze sales effectively.
  • Inefficient Sales Tracking: There was a significant gap in understanding the efficacy of upper funnel engagement and attribution across the sales cycle, leading to low conversion rates.
  • Technical Limitations: The firm’s internal team lacked the resources to overhaul their system, necessitating external expertise to build a scalable and unified data infrastructure.


Blue Orange Digital proposed a comprehensive overhaul of the firm’s data architecture and processes through the development of an automated deal-sourcing platform. This platform was designed to leverage advanced data science techniques and predictive analytics to streamline the firm’s investment processes. The solution included:

  1. Data Transformation: Implementing a scalable architecture that could handle all data-driven operational growth confidently.
  2. Data Visualization and Integration: Enhancing sales modeling and oversight with real-time, full-funnel dashboards to provide actionable insights.
  3. Predictive Analytics and Automation: Utilizing machine learning to improve lead segmentation and increase top-of-funnel conversion rates.
  4. Portfolio Company Performance Enhancement: Offering real-time data science as a service to portfolio companies, thereby improving their performance and the firm’s investment returns.


The project involved several key activities:

  • Scrape and Collect: A bot was coded to collect data from both public (e.g., stocks, news, social media) and private sources, compiling this information into a unified source for real-time trend analysis and association identification.
  • Track and Quantify Non-Financial Data: Notifications for significant market movements were set up, and systems were trained to identify non-financial data points like growth in web traffic and social media engagement, which are indicative of a company’s market traction.
  • Score Prospects: Predictive lead scoring models were developed to rank companies based on data-driven insights, optimizing the firm’s focus on the most promising opportunities.
  • Advanced Filtering and Peer Analysis: Tailored outreach efforts were enabled by narrowing down potential deals through data analytics, including sentiment analysis and business model comparisons.


The implementation of the automated deal-sourcing platform transformed the firm’s approach to private equity investments:

  • Enhanced Decision-Making: The firm now benefits from a comprehensive view of potential investments, backed by data-driven insights that enhance decision accuracy.
  • Increased Efficiency: Automated processes and predictive analytics significantly reduced the time and effort required to identify and evaluate potential deals.
  • Improved Investment Returns: The ability to quickly and accurately assess investment opportunities led to better allocation of resources and higher returns on investments.

This strategic enhancement of the firm’s deal-sourcing capabilities through advanced data analytics and machine learning has set a new standard for efficiency and effectiveness in the private equity sector.