Turning a black box into a business growth engine.

01 — Strategic Context

The opportunity hiding
inside a cost center

The Rewards Rule Engine powered our entire loyalty ecosystem — automating how incentives were triggered, stacked, and distributed. It worked. But it was a black box: opaque to stakeholders, inaccessible to growth teams, and entirely dependent on engineering for even the simplest campaign changes.

For a tool designed to reward users, the experience of creating a rule felt like punishment. The system was built for engineers, not for the growth teams who actually needed to move fast.

— Pooja Kudesia, UX Lead

This wasn't just a UX problem — it was a business velocity problem. Every campaign change required developer time. Every logic conflict surfaced after launch. Every new reward type needed months of backend rearchitecting. I reframed the brief: not "redesign the interface" but redesign the system so non-technical teams could own the entire campaign lifecycle.

02 — Problem Framing

Three compounding friction points

Before scoping a solution, I led a cross-functional diagnostic to understand the true cost of the status quo.

04 — My Leadership Process

I didn't just redesign screens.
I architected a workflow.

My approach was diagnostic before it was generative. I built shared understanding across stakeholders before a single wireframe was drawn.

1. 1

   Cross-functional audit & stakeholder alignment

   I initiated a structured audit involving Marketing, Product, and Data Engineering — creating a shared language for the problem before any solutions were proposed. This prevented the redesign from being perceived as a UI facelift and positioned design as a strategic driver of operational change.

2. 2

   Deep-dive user research with 10+ power users

   I personally led interviews asking participants to walk me through their last 3 campaign attempts — mapping friction, workarounds, and failure points in real time rather than asking them what they wanted.

   💡 Key finding: 60% of rule failures were not bugs — they were logic conflicts caused by a UI with no guardrails.
3. 3

   12 months of quantitative campaign data analysis

   Rather than designing for edge cases, we analysed historical data to identify the most frequent rule combinations. This gave us a principled basis for prioritisation: build the modular builder around the 20% of patterns covering 80% of real-world use cases.

   📊 Prioritisation insight: 80% of use cases covered with zero custom coding in the new system.
4. 4

   Iterative concept testing & logic validation design

   I designed and facilitated multiple rounds of concept testing — specifically to stress-test the logic-building experience. We tested whether non-technical users could correctly configure complex rule stacks without introducing conflicts. The fail-safe validation system emerged directly from observing users in early prototypes.

5. 5

   Engineering partnership for scalable architecture

   I embedded with Data Engineering throughout the build — not to dictate implementation, but to ensure the modular design system was technically sustainable. The resulting component architecture enabled future reward types to be added without redesign.

   ⚡ Result: Unlimited reward type scalability built into the architecture from day one.

05 — Strategic Objectives

Design as business strategy

I reframed the project around three strategic objectives aligned to business KPIs — not just usability metrics. This framing secured cross-functional buy-in and executive sponsorship.