Email List Segmentation: Simple Rules, Big Lift

I resisted segmentation for a long time because the tooling looked heavy. Turns out you do not need a fancy ESP. Two boolean fields on the subscriber table and a slightly smarter send function covers 90% of the benefit.

The Minimum Schema

subscribers:
  email
  interests: {technical: bool, business: bool}
  created_at
  last_opened_at

Two booleans. Interests get set at signup via a two-checkbox form. Boring technology, big result.

The Sends That Changed

My technical deep dives go only to interests.technical. My business essays go only to interests.business. Subscribers get roughly half as many emails, but each one is relevant. Open rates went from 22% to 41% in six weeks. Unsubscribe rate dropped by a third.

The logic is boring:

def audience_for(post):
    if post.category == 'technical':
        return subscribers.filter(interests__technical=True)
    if post.category == 'business':
        return subscribers.filter(interests__business=True)
    return subscribers.all()  # general content, everyone

Activity Segments Come Next

Second boolean: has the subscriber opened anything in 90 days? If not, they move to a low-frequency list. One email a month instead of weekly. Some come back. Some do not. Either way the list stays healthier and sender reputation improves.

What Not To Do At My Scale

I do not run A/B tests, complex drip sequences, or behavior-triggered automations. Those need volume I do not have. At 2,000 subscribers, clean segmentation and consistent cadence beat fancy tooling by a mile.

See the AWS SES list management best practices for the plumbing side. Two booleans. That is the unlock.