Date-based Segmentation

Overview

In marketing automation, dates are everywhere — birthdays, first purchases, renewal reminders, anniversaries. Yet Drip users couldn’t build segments using that data. They could automate around it in Workflows, but not group, export, or retarget subscribers who shared a common date.

This gap limited customers in two important ways: they couldn’t perform one-time bulk actions (like exporting everyone with a renewal next week), and they couldn’t easily validate their automation logic. For a platform built on precision and flexibility, that felt like a big blind spot.

As Principal Product Designer, I partnered closely with product and engineering to define how Drip could support date-based segmentation — a foundational step toward more dynamic, context-aware automation.

Understanding the Problem

The challenge wasn’t just adding date fields to the segment builder. Dates introduce nuance: absolute versus recurring, relative timeframes, timezone handling, even import formats.

Customers expected the same flexibility they had seen in competitors like Klaviyo or Mailchimp — “birthdays in the next 10 days,” “renewals before next month,” “purchases not in the last 90 days” — and they wanted those filters to behave predictably across the app.

Early research and customer sessions helped define what “predictably” meant. Most users didn’t want complexity; they wanted clarity. Phrases like “not in the last” or “before or on” often caused hesitation. My goal became making that logic feel conversational, not computational.

Design Approach

I started by mapping all existing segment operators and exploring how date logic could fit naturally within that structure. Consistency mattered — both for usability and technical feasibility.

In Figma, I built prototypes that introduced new operators like in the next / in the last / not in the next / not in the last, along with a contextual calendar picker that let users toggle between absolute and recurring dates. These patterns reused familiar controls from the rest of the segment builder, reducing learning friction.

Alongside design work, I collaborated with engineering to ensure parsing rules and data formatting aligned with Drip’s standards. We explored how to handle timezone logic (account owner vs. subscriber), and I worked directly in Storybook to test early interactive prototypes.

Throughout, I used AI tools for quick exploration — generating operator grammar examples, validating copy tone, and creating lightweight interaction prototypes for internal reviews. It sped up iteration and gave teams faster clarity during decision points.

Collaboration and Decision-Making

This project was as much about partnership as it was about pixels.

I worked with product leadership to define what belonged in V1 versus later releases, balancing ambition with feasibility. Engineering contributed heavily to operator behavior and timezone logic; customer success helped frame messaging for migrations and imports.

We also ran early validation calls with existing customers — including Golden Key Partnership and Wonder Math — whose automation strategies relied on date logic. Their feedback clarified both terminology and the real-world use cases we needed to serve.

Outcome

The result was a new segmentation layer that brought date awareness to Drip’s entire ecosystem. Users could now:

  • Build and save segments using date-based custom fields

  • Create segments for absolute or recurring dates (e.g., birthdays)

  • Use relative operators like “in the next 30 days”

  • Export those segments or use them across Workflows, Forms, and Campaigns

We launched this alongside new Date-based Delay and Goal nodes, creating a unified “date logic” release that extended across the platform. It not only unlocked new use cases for power users but also gave newer customers a clearer, more intuitive entry point to automation.

Reflection

This project reminded me that clarity isn’t just visual — it’s linguistic.

Operator grammar, phrasing, and even the order of options all shape how confidently a user can make a decision. I also learned how valuable it is to design for partnership: involving engineering early on shaped smarter constraints, and customer input helped simplify what could have easily become too technical.

Most of all, it reinforced my belief that good design is about shared understanding. Sometimes that means slowing down, asking better questions, and being open to being wrong — even (especially) when you’ve been working on the product for years.

Assets

  • Figma: Date-based Segmentation

  • Prototype: v0 interactive demo

  • Stakeholder review: BuildBetter Pandas Leads call

  • Customer interviews: Golden Key Partnership, Wonder Math

  • Internal documentation: Operator matrix, timezone specs, grammar patterns