106. Growing a Self-Funded Company

Host Greg Nokes is a distinguished technical architect with Heroku. His guests are Alli McGee, a product manager, and Lewis Buckley, a senior application engineer, from BiggerPockets. BiggerPockets was founded 16 years ago to educate non-professionals about real estate investing. As a self-funded company, it’s critical for BiggerPockets to create products that customers will pay for. One way they achieve this product/market fit is by building cross-functional teams that are user-focused. All product teams have a project manager, tech lead, and designer that work closely together. This design-led approach allows teams to collaborate with representation from users, technology, and design. As the PM on one of these teams, Alli lives at the intersection of what can we do for business, what can we do from a technology perspective, and what can we do for the user. She advocates for the customer, bringing knowledge of what customers want, what problems they are facing, and how they have interacted with prototypes in usability studies. Alli also advocates for the business to be sure products make money. Finally, Alli advocates for developers to make sure the project is technically feasible and won’t cause technical debt. Another way BiggerPockets creates market fit is by creating Minimum Lovable Products—the smallest cheapest thing they can build that people love. With their current product, BP Insights, Alli and Lewis used this strategy to create the first iteration of a product that provides insight into local real estate markets. They then tested the product with users, iterated, and slowly built out a more fully formed offering. For their tech stack, BiggerPockets is built on a Ruby on Rails monolith. While some in the industry say Ruby on Rails’ time is over, Lewis argues that it has been a great choice, as using a well-known stack has allowed them to worry less about the technology and focus more on building value for users. The BP Insights product was built on this monolith using a massive data set of nearly every property in the US. BiggerPockets imported the data to an Amazon S3 bucket and eventually copied the data to Amazon Redshift for querying. Links from this episode BiggerPockets