Affiliate Campaign Analytics Platform
The client-facing analytics application inside a three-app micro-frontend CRM.
- Frontend Developer
- React · Next.js · TypeScript · TailwindCSS
Overview
One of three independently deployable applications — Admin, Client and Supplier — that make up a CRM platform for B2B market research, data insights and analytics. This is the client-facing surface, where external users work through campaign and analytics workflows on top of shared platform infrastructure.
Problem
Three products with heavily overlapping interface needs had to evolve and deploy on their own cadence without the UI drifting apart or the teams rebuilding the same things three times.
My Role
- Built and scaled the client-facing application within the micro-frontend architecture.
- Co-architected the shared, reusable UI component library that all three micro-frontends consume.
- Wired UI workflows into backend intelligence services for an AI-powered lead-generation product.
Architecture
React + Next.js application, composed as an independently deployable micro-frontend.
A versioned component library used as the single interface source of truth across Admin, Client and Supplier.
UI workflows connected to backend intelligence services powering lead generation.
Technology
- React
- Next.js
- TypeScript
- TailwindCSS
- Micro-frontends
- Shared component library
- Independent deployment
Engineering Challenges
Independent deployment without UI drift
Each application shipped on its own schedule, so shared surfaces risked diverging in behaviour and style over time.
Three teams, one interface language
Common patterns were being rebuilt per application, multiplying maintenance.
Driving AI services from the UI
Lead-generation intelligence lived in backend services the interface had to orchestrate predictably.
Solution
Independent deployment without UI drift
A single versioned component library became the contract between apps — interaction and visual patterns changed in one place and propagated on upgrade.
Three teams, one interface language
Consolidating primitives into the shared library cut duplicate code and reduced new-feature time by an estimated 40%.
Driving AI services from the UI
Modelled the UI workflows around those service calls so the product surface stayed responsive and observable as the data moved.
Performance
- Campaign-ready marketing pages for the platform reached a Lighthouse performance score of 98.
Product Experience
- Navigation, data views and forms behave identically across all three applications because they render from the same library.
Results
Links
Client engagement at Jasper Colin — not publicly linkable.