2 min - May 26, 2021

The SaaS Metrics that Matter Most with Sanjana Basu, Radical Ventures

Radical Ventures is an Artificial Intelligence-focused VC fund created by AI founders for AI founders. Radical Ventures’ founders also founded Layer 6 — one of Canada’s largest AI exits — and the Vector Institute for AI, and helped co-author the Pan Canadian AI Strategy, the world’s first AI national AI strategy. Radical Ventures’ team has deep technical expertise, business experience and relationships across the AI world, enabling them to invest in AI world-leaders. Radical Ventures’ Impact Team provides specialized support to help AI companies achieve global success.

Radical Ventures portfolio company PocketHealth is a fast growing, patient-centric medical image and patient data access and sharing software platform. The PocketHealth team is removing hard costs and friction for hospitals and healthcare providers while fundamentally empowering patients with a simple-to-use platform to participate in their care.

Radical Ventures investor Sanjana Basu emphasizes that the key metrics founders should focus on will vary depending on their specific context. She advises founders to be obsessed with their customers and have a disciplined approach to track the metrics that enable companies to make the necessary changes to drive customer value.

As her top SaaS metric picks in general, Sanjana focuses on acquisition, retention and growth metrics.




Sanjana also looks at Net Burn and wants to see how cash is being used to create value for customers and stakeholders.

In the context of AI, Sanjana calls out three notable nuances:

Revenue Type – While it can be tempting to prioritize short term AI services revenue from building customized solutions, a scalable AI product is where the value is generated. In the words of Radical Ventures Co-founder and Managing Partner Jordan Jacobs, “Global AI companies should be more SaaS than Service.”

Data Costs – Sanjana looks to see how AI companies are finding innovative ways to reduce the costs of data acquisition, data storage, data annotation, and data labelling while ensuring high quality data in the context of the exact problem they’re trying to solve.

Algorithmic Performance – Algorithmic performance benchmarks for AI companies vary depending on the type of problem being solved. Founders need to know what a good benchmark is for their context and use cases.

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