Last year Substack introduced a new recommendation feature which I want to highlight here. In case you’re unfamiliar with Substack, it’s a platform that lets writers and podcasters publish directly to their audience and get paid through subscriptions.

The principle behind Substack’s recommendation feature is as simple as it’s powerful: a writer can select several publications to recommend to new subscribers, and the recommended writers will be notified and prompted to recommend back.

Substack’s approach to cross-promotion creates a loop where Susbstack readers discover new content, and for writers to promote other writers. Apart from recommending another writer when a new reader subscribes to their publication, they can also add a personal blurb to explain why they endorse the publication.

If you are a reader on Substack, you’ll see the recommended publications after you’ve subscribed to a specific publication. You can subscribe to these recommended publications there and then or do so later.

For readers, recommended publications appear after they subscribe to a specific publication. The content is not algorithmic, and the writer is in control of which publications they recommend. The recommendations are controlled fully by the writer and there’s no algorithmic element to it. The recommended writer will receive an email with details of how the endorsement has impacted their subscriber numbers. They’re in turn encouraged to reciprocate the recommendation, and recommend other writers.

Main learning point: Credit to Substack’s product people like Dayne and Gabriel for launching this new recommendation feature in Beta last year. Substack’s recommendation feature triggers a nice discovery loop, which is then perpetuated in a simple but powerful way.