Clover Customers

This project ran in two phases.

Phase 1 started without a product owner, without a research function, and without an engineering team. That was an intentional strategic decision by our design director: advance the project through discovery and design while engineering was committed to other priorities. I operated as lead designer, surrogate researcher, and de facto product owner for the first phase.

Phase 2 began when a new product owner joined and ran fresh discovery across Customers, Loyalty, and Marketing together. What she found changed the scope: the loyalty and messaging tools weren’t just underused; they were on legacy systems that needed migration, and the loyalty program was out of compliance. The brief expanded from surfacing existing tools to actually fixing them. Engineering was in the room from day one of Phase 2.

I completed the dashboard and customer detail work toward handoff, and I directed a direct report through the loyalty, feedback, and messaging redesigns.

Growth design was central to both phases. The brief was specifically about retention, upgrade conversion, and making a subscription feel worth paying for, not just usability improvement.

Clover’s merchant base skews toward small business owners who don’t have time for analytics. They need the product to surface the right information at the right moment and make the next action obvious. The Customers section wasn’t doing that. Loyalty, feedback, and messaging tools existed but lived in silos, and engagement with all three was low for a paid subscription.

The ask was a dashboard. The real problem was coherence, not features. The dashboard is a desktop-primary tool; merchants managing customer relationships are usually at a workstation. The loyalty tools lived inside the Clover mobile app, so that surface was mobile-first.

Discovery pulled from three parallel streams: support channel feedback, in-app behavioral data, and competitive analysis.

The finding was consistent across all three: merchants wanted to act on their data, not just see it. The tools to act were already there. The path to them wasn’t.

Three pillars anchored every design decision in Phase 1:

Integrated
The dashboard should be a starting point for action, not a reporting surface. Loyalty, feedback, and messaging should be reachable from within a customer’s record, not buried in separate navigation.

Customer-empowered
End customers needed real control over what merchants knew about them, per merchant, per channel, per message type.

Merchant-empowered
The base tier had to justify its cost before asking anyone to upgrade. The upgrade had to offer something unavailable elsewhere: merchant performance benchmarked against similar nearby businesses.

Proposed details: enhanced tier vs. basic tier.

Phase 1 had no research function. I structured AI prompts to model merchant and payment processor perspectives, giving me directional signal quickly enough to move design decisions forward. Every output was pressure-tested against support channel data and competitive analysis before it influenced anything. It wasn’t ground truth. It was a starting point I could validate.

The design vision for the Customers area included an insight layer that would move beyond charts: not just “here’s what happened” but “here’s what to do about it.” At the time, AI was the mechanism that made delivering that guidance at scale realistic, and it would have been a genuine market differentiator. Legal and security cut it during review. Data leaving the system was a risk they weren’t willing to carry, and in-house models were cost-prohibitive. We replaced it with comparative analytics and smart aggregation, which delivered similar value without the dependency.

That cut was consequential, not just a feature removal. It changed what the product could claim. Knowing the difference between a feature and a strategic capability is part of the job.

When the new product owner ran fresh discovery, she found that the loyalty program wasn’t just underperforming: it was out of compliance with messaging consent standards and behind competitors on features customers expected. Enrollment flows were inconsistent across platforms, creating measurable drop-off. The messaging control model was too blunt: one toggle for all message types, all merchants, both channels.

The fix required two things: streamline enrollment across all surfaces to reduce friction, and give customers granular control. Separate opt-in for operational messages (receipts, invoices) and marketing messages (promos, rewards), per channel (email and SMS), per individual merchant. That’s a more complex system. It’s also a fundamentally more honest one.

Those flows ran inside the Clover mobile app, so the granular opt-in model my direct report designed had to work on a small screen first: touch targets, form layout and confirmation states were validated on mobile before the desktop treatment was considered complete.

I stayed on the dashboard and customer details, completing that work toward engineering handoff. The loyalty, feedback, and messaging redesigns went to my direct report. He didn’t start from a blank canvas. He started from the IA, the design direction, the interaction model, the validated data architecture, and a constraints doc capturing every Phase 1 decision and the reasoning behind it.

Weekly design reviews. Problem-solving when he hit walls. Walking through edge cases and engineering alignment together. Present enough that he wasn’t blocked, hands-off enough that the work was his.

Phase 2 ran as a fully collaborative model from the start. Feasibility checked before direction locked. Data structures validated before designs were finalized. Edge cases surfaced before handoff. That’s my default model when I have the full team available.

The loyalty updates launched first. Three months post-launch, loyalty transactions were up 29.3% a month and new loyalty members up 28.3%.

Those numbers came from two specific fixes: reducing enrollment friction across platforms, and giving customers meaningful control over what they received and from whom. The dashboard and customer details are in active development. Feedback and messaging tools are queued behind them. The unified data model, IA, and interaction patterns established in Phase 1 carry forward into all of it.

Lead UXDUser research (surrogate methods)Functional & content reqsInformation architectureUser flowsUI designResponsive designMobile-first (Loyalty)Stakeholder alignmentGrowth design

Three things I’d carry into any similar engagement: