Common Plus
Karan Chodron had already built and sold a business before Common Plus. His previous company, Drop Thought, collected and analyzed feedback for large enterprises using machine learning, and it was acquired after raising over $7 million in venture funding. From that experience he saw an opening in online content. Outside of a few companies like Netflix, he argued, most platforms do a poor job recommending content, and viewers struggle to find what they want in an ocean of material.
Common Plus applies machine learning analytics to content personalization. The team is small, five people at the time of the interview, and was built around a deliberately lean, highly qualified group that included Karan's former head of technology and a data scientist with a Stanford postdoc background. The company stayed bootstrapped. Karan explained that the business was designed so that client revenue would fund the team from the start.
The first customer came through a referral. TVF (The Viral Fever) gave Common Plus its entire content library and access to its user base. Common Plus builds user clusters from behavior on the platform and uses them to improve conversions, engagement, and retention through targeted content, emails, and push notifications. Karan said customer acquisition cost on the platform had dropped to under 50 cents per customer.
The company's pricing is aimed at large content providers, because the data science needs large volumes of users and content to work well, which rules out most SMBs. Karan's plan is to price on performance, charging based on measured improvements in acquisition costs, retention, and engagement. He also estimated that sales and marketing spend would run 50 to 70 percent of revenue early on, with a target of about half of first-year contract value spent on acquiring each customer.
At the time of the interview, Common Plus had one major customer and was not raising money, because the client funded operations. Karan's stated focus was proving value so the client would be excited about results. Competitors named include RichRelevance and Gravity R&D, which mostly target e-commerce, while Common Plus targets content.
- •Founding with a proven prior exit and a machine learning background gave the founder credibility to land a large enterprise client quickly.
- •Targeting a niche where incumbents focus on e-commerce left the content vertical underserved, which made a referral-led enterprise sale easier to win.
- •Structuring the business so that the first client's revenue funded the team let the company stay bootstrapped and avoid fundraising while proving value.
- •Selling only to large providers, because the product needs big data volumes, meant each customer was worth enough to justify high-touch enterprise sales.
- 1.Start with a warm referral from an existing relationship or advisor, and secure a large anchor client before building a full sales engine.
- 2.Pick a vertical where incumbent tools serve other industries poorly, and tailor the product to that vertical's specific data and users.
- 3.Price high-value annual contracts for enterprises, and consider moving to performance-based pricing tied to measurable customer outcomes.
- 4.Set a clear customer acquisition budget relative to contract value, for example spending about half of first-year contract value per customer, before scaling sales.
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