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Stratified

by Derek WangLaunched 2015-01via Nathan Latka Podcast
See all SaaS companies using enterprise direct sales
ARR$6.0M
Growthenterprise direct sales
Pricingsubscription
The Spark

Derek Wang, a computer science professor at the University of North Carolina at Charlotte, experienced a frustration that would become the seed for Stratified. Students from non-technical backgrounds—business school students, civil engineers, mechanical engineers—would come to him asking how to leverage machine learning for real-world impact. But he had to turn them away because they lacked the coding background necessary to implement these solutions. This gap between ambition and capability drove him to imagine a different way: what if a platform could democratize advanced analytics, shortening the time it takes for non-technical experts to extract insights from data?

Building the First Version

In January 2015, Wang launched Stratified alongside two co-founders, initially growing out of government-funded research on how AI could ingest, analyze, and visualize unstructured data. The platform was built to be an extension of human intelligence—enabling users to extract hidden signals from disparate data sources and surface actionable insights without requiring deep technical expertise. Rather than focusing on consumer appeal, the founders pursued enterprise customers, recognizing that large organizations had the most pressing need to make sense of siloed data streams.

Finding the First Customers

Stratified's go-to-market strategy was deliberately enterprise-focused. When Wang returned for his second interview in early 2020, the company had grown from 30 customers (from his first interview in June 2018) to 50 logos. The average annual contract value hovered between $100,000 and $200,000, with a tiered pricing model starting at $24,000 and reaching up to six figures. The company employed only three quota-carrying sales representatives at that time, relying instead on product-led expansion within existing accounts. This lean sales approach reflected the company's philosophy: prioritize product utility and customer success, and expansion revenue will follow organically.

What Worked (and What Didn't)

Stratified's most effective growth lever proved to be land-and-expand. By February 2020, the company had pre-virus approximately 100+ licensed users across its 50 customer accounts. When COVID-19 forced organizations into digital-first operations, demand for Stratified spiked dramatically. Within two and a half weeks of full remote work, user count doubled and data volume increased fivefold. The company went from over 100 users pre-virus to over 200 users across the same 50 logos—a testament to how internal adoption accelerated during crisis. The company's net revenue retention of 120-130% and remarkably low 3.6% annual gross revenue churn demonstrated that once Stratified was embedded in an organization, it became essential. A concrete example: a major financial services company used Stratified to monitor five to six digital channels (call centers, online chat, surveys, community forums, email) in real time, allowing them to proactively adjust communication strategies as federal interest rates dropped and CD product offerings shifted.

Where They Are Now

By early 2020, Stratified had raised $12 million in initial funding and an additional $30 million in mid-2018, bringing total capital to $42 million. The company had grown to approximately 70 people in the US and 35 globally (105 total), with the majority being engineers (60-70% of headcount). Monthly net burn was approximately $700,000, providing substantial runway even amid economic uncertainty. The company's ARR reached approximately $6 million, representing healthy growth from the estimated $3.5 million run rate 18 months prior. Derek emphasized operational efficiency as a core principle, avoiding the "growth at all costs" mentality. The company was not laying off staff despite the pandemic and was actually hiring strategically. Rather than pursue aggressive capital raises in uncertain times, Stratified was exploring alternative financing instruments like venture debt and credit facilities to extend runway while maintaining its customer-first philosophy.

Why It Worked
  • By solving a pain point the founder personally experienced in his own domain, Wang built deep empathy for the actual problem, which translated into a product that genuinely solved a high-value enterprise need rather than a perceived one.
  • The decision to pursue enterprise customers from day one aligned the company's go-to-market strategy with its product's strengths, avoiding the mismatch of trying to sell complex analytics tools to price-sensitive or less-committed segments.
  • The lean three-person sales model combined with product-led expansion created a unit economics engine where customer success and organic adoption within accounts generated 120-130% net revenue retention, meaning existing customers drove growth without proportional sales costs.
  • The platform's ability to reduce friction for non-technical domain experts to extract insights from siloed data sources created defensibility in a crisis moment—when organizations went remote and digital adoption accelerated, Stratified became embedded infrastructure that was harder to displace.
How to Replicate
  • 1.Identify a specific pain point you or your team has experienced firsthand in a domain where you have deep expertise, then validate whether enterprise customers in that domain face the same friction at meaningful scale and budget.
  • 2.Build your initial product to solve the acute problem for non-technical experts in your target domain, then go directly to enterprise sales rather than attempting product-led growth or freemium strategies that dilute focus.
  • 3.Structure your go-to-market with minimal quota-carrying salespeople and instead invest heavily in customer success and product utility, measuring success by net revenue retention and expansion within existing accounts rather than new logos.
  • 4.Design your product to integrate deeply into customer workflows and aggregate data from multiple internal sources, making it increasingly difficult to remove once adopted and creating natural expansion opportunities as users discover new use cases.

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