AI for GTM | NextRoll Andrew Pascoe

Foundation Capital hosted our first Fintech x AI PortCo Summit in June 2023 to help executives across the Foundation Capital Portfolio answer the question “What should I be doing about AI?”

Founders and leaders from over 35 Fintech companies convened to share their learnings and gather perspectives on this essential question.

Andrew’s talk turned to the use cases of AI for GTM. Andrew was the first data scientist at NextRoll and has been leading ML efforts at this marketing technology platform for over 11 years. NextRoll’s tech stack includes a variety of ML models, including generalized linear models for ad pricing, recommender systems for personalizing ads to user preferences, LLMs for tasks like text classification and information extraction, and external vendors like ChatGPT.

Andrew began with an overview of NextRoll’s flagship products, highlighting the application of ML in each. BidIQ, their ad bidding engine, uncovers statistically significant patterns undetectable by humans, allowing marketers to refine ad delivery. Dynamic Creative uses ML to test variations in ad components, such as images, CTAs, and copy, to identify what engages viewers the most. Generative Emails leverage ChatGPT to craft personalized emails using CRM data. Last up, Account News feeds RSS content into an LLM to classify and extract relevant company news for sales teams.

Andrew then shared four guiding principles for designing effective ML products:

-"Knowledge is power": Deliver insights that help clients enhance the quality of their work.
-"That which is measured improves:" Identify and surface the core information that clients care about so they can focus improvements in the right places.
-"Time is money": Optimize products to help clients achieve more in less time—for example, by automating repetitive tasks, building intuitive interfaces, and suggesting relevant shortcuts.
-"All models are wrong, but some are useful": Avoid getting bogged down trying to build the perfect AI model. It’s better to ship an MVP quickly, get user feedback, and iterate. Enabling incremental improvements through rapid experimentation beats prolonged development cycles.

In closing, Andrew underscored the ongoing need to keep humans in the loop. While AI can create helpful and grammatically correct content, it should serve as a basis for sparking human creativity and generating ideas, rather than as a completely independent, end-to-end solution.