Checkr CPO on Building Scalable Products for Large Enterprises | Ilan Frank | E256
Feb 12, 2025
auto_awesome
Ilan Frank is the Chief Product Officer at Checkr, a pioneer in the background check industry, known for his expertise in building scalable SaaS products for large enterprises. He shares insights on transitioning from SMBs to large-scale operations and the significance of balancing user delight with enterprise needs. Ilan discusses Checkr's innovative use of AI, which dramatically speeds up background checks, and the importance of product-market fit. He also highlights the evolving role of product managers in navigating complex enterprise challenges.
Successfully transitioning from SMBs to large enterprises requires a tailored product approach that meets unique enterprise client demands.
Leveraging AI and machine learning significantly enhances the efficiency of background checks, enabling faster processing and improving user experience.
Deep dives
The Transition to Enterprise Success
Transitioning from serving small to medium businesses (SMBs) to larger enterprises involves significant challenges and a focused strategy. A prime example comes from the experience of Checker, which initially tried to move upmarket too quickly without sufficient preparation, resulting in the need to pivot back to SMBs to solidify their product-market fit. The key to successfully navigating this transition lies in understanding the distinct needs of enterprise clients and the organization’s readiness to meet those demands. This journey emphasizes the necessity for tailored product development and the importance of crafting features that resonate with both choosers and users in enterprise environments.
Product Readiness for Enterprise
A product must demonstrate market fit and reliability before targeting enterprise clients to avoid failure. This includes understanding the unique demands of enterprise customers, such as enhanced security and administrative capabilities, that go beyond basic user needs. For instance, in previous roles at companies like Slack, a clear understanding of what enterprise users valued helped in creating solutions that addressed organizational alignment and improved user experience. This preparation ensures that a product does not just meet initial requirements but is also strategically positioned for growth within the enterprise sector.
Navigating User and Chooser Needs
In enterprise product management, balancing the needs of users and choosers is essential for creating a successful product roadmap. Features often need to address the concerns of those with budget authority while still providing an optimal experience for the end users. A helpful approach includes categorizing features into 'metrics movers,' 'customer asks,' and 'delighters' to ensure that the roadmap simultaneously pushes the company's strategy forward and enhances user satisfaction. Such an approach fosters loyalty by addressing hidden user needs that may not be immediately apparent but significantly contribute to the overall success of the product.
Leveraging AI for Efficiency
Artificial intelligence plays a crucial role in enhancing the efficiency and accuracy of background checks at Checker, enabling faster processing times compared to industry standards. The integration of machine learning allows Checker to automate much of the data collection and verification process, achieving turnaround times of 15 minutes or less for 85% of checks. Utilizing AI to adapt to variations in county-level legal language also improves comprehensibility for end users, streamlining the adjudication process. By continuing to innovate with AI, Checker can better serve enterprise clients while ensuring a delightful experience for both applicants and employers.
In this episode, Carlos Gonzalez de Villaumbrosia interviews Ilan Frank, Chief Product Officer at Checkr, the leading background check company serving over 100,000 customers.
Checkr has revolutionized the background check industry, processing 85% of checks in 15 minutes or less, compared to the industry standard of 1 to 3 days. This remarkable efficiency is achieved through the power of artificial intelligence and machine learning.
With $679 million in funding and a $5 billion valuation, Checkr serves major companies like Uber, Warby Parker, and Instacart. Ilan is spearheading the core business while driving product expansion into larger enterprises and new markets.
In this episode, we'll explore Ilan's extensive experience in building SaaS products for large enterprises, including his work at successful companies like Slack, Airtable, and Grammarly. We'll discuss the challenges and strategies involved in transitioning from SMB to enterprise success.
What you'll learn: - Ilan's journey to becoming CPO at Checkr and his insights on the background check industry's digital transformation. - The challenges and strategies involved in creating enterprise-ready products and organizations. - How to balance user delight with complex enterprise client needs. - The future of background check technology, including AI-powered innovations and international expansion.
Key Takeaways: - Enterprise Readiness: Ilan emphasizes the importance of both product and organizational readiness when transitioning to serve enterprise clients. - Product Management Approach: Ilan shares his "Three Buckets" strategy for prioritizing features: metrics movers, customer asks, and delighters. - Data-Driven Innovation: Ilan highlights the use of AI and machine learning to process vast amounts of data quickly and accurately, revolutionizing the background check industry. - Enterprise Product Development: Ilan discusses the importance of understanding both the "chooser" and the "user" in enterprise software, and how to create value for both.
Social Links: - Follow our Podcast on Tik Tok here - Follow Product School on LinkedIn here - Join Product School's free events here - Find out more about Product School here