Rule Builder．2025

Payments Recovery Rule Creation for Non-Technical Users

Led the 0 to 1 UX for a B2B payment recovery product, turning complex backend logic into a guided rule creation experience for business teams.

Through stakeholder synthesis, workflow design, IA, prototyping, and usability testing, I helped shape a no code rule builder that made rule creation faster and more confident for first time users.

Disclaimer:
In compliance with my non-disclosure agreement, I’ve omitted any confidential information from this case study. The insights shared here are my own and don’t necessarily represent the views of Data-Realm.

Team

Director of Marketing
Principal Architect
Co-founder

DURATION

14 weeks

Deliverable

UX Strategy
Affinity Map
Workflow
Persona
Information Architecture
User Flows
Prototype
Usability Testing

Tool

Figma
ChatGPT
Preplexity
Gemini
Claude

Impact

~50% faster
Create rule
↑ confidence
Clear status + confirmations
Built UX foundation
Set up discovery-to-test workflow in a low-maturity org
Exterior of a wellness clinic with glass windows listing services like massage therapy, Pilates, acupuncture, and Chinese herbal medicine, plus a neon sign for direct billing.

TL;DR

Data-Realm needed a way for business teams to manage payment recovery rules without relying on developers.
I led the end to end UX for a 0 to 1 rule builder, translating a complex backend workflow into a structured, no code experience.
After usability testing, I redesigned the flow into a guided builder that improved time to create by about 50% and increased user confidence.

The Challenge

The team was launching a B2B web product and wanted business teams to manage complex payment recovery rules on their own.
The original framing was broad: make payment churn recovery more proactive and easier to manage.
Through discovery, I helped refine the challenge into a more specific product problem:
How might we help revenue teams edit payment-churn rules confidently without engineering support?
This meant designing for two things at once:
  • Enough flexibility for business teams to make routine changes
  • Enough structure and safety to prevent incorrect or risky rule changes

Understanding the Problem

I started with stakeholder interviews to uncover why this mattered and what success required. An affinity map helped me cluster the input into clear themes and constraints.

Key Questions:

  • What triggered the decision to build this?
  • What does success look like for this product?
  • Who will use it?
  • How do users solve this today?
  • What constraints shape delivery?
Workspace board layout with sections on user design, key workflows, adoption and learnability, system integration, design constraints, and success measurement, featuring clustered sticky notes in pink, yellow, blue, and purple.
Interview Synthesis:
  • The team explored turning backend expertise into a product offering that helps business teams manage recurring customer pain points.
  • Target users span SMB and enterprise teams who currently rely on existing tools to cover parts of the workflow.
  • Success meant proving value and viability fast, while aligning a team new to UX.

Workflow Transformation

Next, I mapped the current workflow and the desired future workflow. The mapping was guided by 3 questions:
  • What actually happens today end-to-end?
  • What pain points and business risks are motivating the need for a new solution?
  • How do departments collaborate today?

1. Original Workflow

Workflow diagram showing original process stages from Issue Detection to Visibility & Governance with description and roles of Owner and Support at each stage.
A mostly linear flow with a default response, limited personalization, and manual exception handling later in the process.

2. Future Workflow

Future workflow diagram illustrating steps from Issue Detection to Visibility and Governance with areas of ownership and support in Automation, Revenue Ops, Data/Analytics, Marketing Ops, and Engineering Support.
A “classify & route” step was introduced to handle cases by scenario instead of pushing everything through one default flow.
Business team could make routine changes through the UI, and engineering could concentrate on integrations and reliability.

Who I Designed For

This work also clarified who the first release should support. Since outreach is executed through a CRM, I prioritized Revenue Ops as the primary user and focused the first phase on their core tasks.
Portrait of a middle-aged man with short hair and beard wearing a light blue button-up shirt in a bright office, alongside text describing the role, pain points, workflow, design implications, and success criteria of a Revenue Ops Manager (Workflow Owner).

Benchmarking the Space

I reviewed publicly available tools via marketplace apps (Shopify ecosystem), Mobbin, and AI-assisted scanning to identify relevant competitors. I captured recurring patterns and standout approaches to inform our IA and core user flows.

01

Rule builders show results for each rule and each step to help teams tune.

02

Dashboards show both rates and impact, then break results down by scenario and reason so teams know what to fix next.

03

Templates give a reliable starting point and speed rule setup.

From Workflow to Product Structure

Once the future workflow was aligned, I translated it into product structure.
I created the information architecture based on core workflows and roles, then validated it against competitor patterns and technical constraints with engineering.
That process helped define the first release around two priority areas:
  • Rulesets
  • Rule Building
Website sitemap diagram with main sections Home, Overview, Recovery Flow, Customers, Analytics & Insights, Campaigns & Communications, Integrations, and Administration, each branching into detailed subsections.
This turned a broad product idea into a more buildable and focused experience.

Exploring the Rule Builder

The core design challenge was how to let non technical users create and edit complex rules without introducing ambiguity.
I explored two directions:
Diagram of two connected blocks with text lines, representing a node structure or rule builder interface.
Node based flow builder
Powerful, but too complex for frequent day to day edits.
Interface mockup depicting a search bar at the top, followed by a shaded content box with two lines of text, and a rectangular button aligned to the bottom right.
Free text to rule
Familiar and appealing, but risky. It would require guided input, strong validation, and extra engineering effort to keep rule translation consistent and safe.
To balance usability, correctness, and delivery constraints, I designed a block and dropdown based builder. This made available options explicit, reduced ambiguity, and guided users toward valid configurations.
A grid of seven rounded rectangular blocks arranged in three rows with empty gray placeholder bars inside each block.
This was the key design decision of the project: prioritize clarity and reliability over maximum flexibility.

Clarifying Steps, & Edge Cases

Structured blocks kept rule creation deterministic and easy to validate, while free text added ambiguity, so I documented the block-based experience in a user flow, including key edge cases.
User flow diagram illustrating the process of creating, editing, reviewing, approving, testing, and activating rules with decision points and edge cases annotated.

Prototyping and Testing

After building a high-fidelity prototype in Figma, I ran moderated usability testing to validate whether business users could create and edit rules confidently.
In the first round, participants could complete the task, but they spent too much time figuring out how to build a valid rule. Even when users said the experience felt easy, I observed hesitation, backtracking, and uncertainty about whether they might publish something incorrect.
That insight changed how I framed the problem. The issue was not just learnability. It was confidence.

What I Changed

Based on testing, I redesigned the experience into a more guided, step by step builder.
I introduced:
  • Clearer progression through the flow
  • More explicit status and structure
  • Confirmation moments to reduce fear of mistakes
  • Stronger guidance around what users could safely do next
A horizontal process flow diagram with four white rounded rectangles connected by right-pointing arrows on a light gray background.
Instead of asking users to assemble logic more freely, the final concept scaffolded the experience so the product felt safer and easier to trust.
In follow up testing, the guided builder improved time to create a rule by about 50%. Users also reported feeling more confident, especially because the clearer structure and confirmation steps reduced anxiety about publishing something wrong.

Building a UX Foundation

This team was new to working with UX, so I created a UX foundation deck to align on the strategy, goals, and deliverables. It established a shared language early and served as a reference point for key artifacts and decisions throughout the project.
Venn diagram titled UX Success showing three overlapping circles labeled Business Goals, User Needs, and Technical Constraints with UX in the intersection, explaining key questions for each area.

Trust but Verify

This was my first 0→1 project using AI deeply, so I treated it like a research teammate rather than a decision-maker. I used AI to accelerate synthesis, market scanning, and ideation, then validated outputs through source checks, stakeholder review, feasibility constraints, and user feedback.
To reduce errors and bias, I cross-checked results across multiple AI systems and primary sources, and I used AI to critique my interview scripts and facilitation between testing rounds.
Diagram showing an AI framework with three stages: Generate by AI (draft interview guide, summarize notes, competitor analysis, UI variations), Verify by Human (source & date checks, stakeholder validation, constraint checks, user feedback) with AI cross-check, and Apply by persona + workflow (IA + user flows, edge cases, final screens + prototype), with a feedback loop from Apply to Generate labeled Learnings from Apply.

Impact

Faster rule creation
Follow up testing showed about a 50% improvement in time to create a rule.
Higher confidence for first time users
Users felt safer moving through the flow because progress, status, and confirmations were more explicit.
Stronger product foundation
The project translated a messy backend process into a clear structure for future product development and cross functional alignment.

Reflection & What's Next

This project reinforced that designing workflow heavy products is not only about helping users complete tasks. It is also about helping them feel confident enough to act.
It also showed me how much impact design can have in 0 to 1 environments when it shapes not just the interface, but also the workflow, scope, and shared understanding behind the product.
Next, I’d define a lightweight measurement plan (adoption, rule error rate, time-to-change) and continue strengthening governance UX (approvals, versioning, rollback) to support sustainable business ownership.

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