Building KPEIZ: Social Media Analytics for the Data-Driven Marketer
Social media marketing in 2019 was evolving rapidly. Marketers needed more than vanity metrics—they needed actionable insights. That's what we set out to build at KPEIZ: a platform that turned social media data into strategic decisions.
The Vision
KPEIZ wasn't just another analytics dashboard. It was a comprehensive platform that: - Tracked performance across Facebook and Instagram - Compared your metrics against competitors (benchmarking) - Identified trending content and optimal posting times - Generated detailed reports for clients - Managed multiple accounts and brands
The Technical Stack
We built on proven technologies: - Angular 8 for the frontend - NodeJS with Express for the backend - MongoDB for flexible data storage - Chart.js for beautiful visualizations - Gridster for customizable dashboards
Integrating Social APIs
The heart of KPEIZ was its integration with social platforms. Using the Facebook Graph API and Instagram Graph API, we pulled: - Post metrics (likes, comments, shares, reach) - Follower demographics - Engagement rates over time - Story performance - Audience insights
The challenge? These APIs had rate limits and required careful error handling. We built a robust queuing system that respected limits while ensuring data freshness.
The Module System
I implemented several key modules:
1. Packs Module Users could purchase different analytics packages. Each pack unlocked certain features. This required careful permission management and feature flagging.
2. Business Tags Users could tag posts with custom business categories (product launch, event, promotion). This enabled analysis by campaign type—powerful for strategic planning.
3. Invoicing System Integrated billing with payment processing. Users could upgrade, downgrade, and manage subscriptions seamlessly.
4. Benchmarking This was the killer feature. Compare your performance against industry averages or specific competitors. We aggregated anonymized data to provide meaningful comparisons while respecting privacy.
Data Visualization
Chart.js powered our visualizations: - Line charts for metrics over time - Bar charts for post comparisons - Pie charts for demographic breakdowns - Radar charts for multi-metric comparisons
Every chart was interactive—hover for details, click to drill down, export as image.
The Dashboard Builder
Using Gridster, we let users build custom dashboards: - Drag-and-drop widgets - Resize charts and cards - Save layouts for different views - Share dashboards with team members
This customization made KPEIZ feel personal—each user's workspace was unique.
Report Generation
Marketers needed reports for clients. We built a report generator that: - Pulled data for any date range - Included key metrics and charts - Generated PDF or web reports - Branded with the user's logo - Scheduled automatic delivery
Payment Integration with Stripe
For payments, we integrated Stripe: - Secure card tokenization - Subscription management - Webhook handling for payment events - Invoice generation - Failed payment recovery
Stripe's API was a pleasure to work with—well-documented and reliable.
Security Considerations
Handling social media tokens and payment information required serious security: - OAuth tokens encrypted at rest - API keys in environment variables, never in code - HTTPS everywhere - Rate limiting on our APIs - Regular security audits
Performance Optimization
With users potentially tracking hundreds of posts: - Data aggregation in the background - Caching of computed metrics - Pagination for large datasets - Lazy loading of chart libraries - Database indexing for fast queries
The UX/UI Challenge
Analytics platforms can be overwhelming. We focused on: - Clean, uncluttered interfaces - Progressive disclosure (show basics, reveal details on demand) - Consistent design language - Responsive design for mobile access - Helpful tooltips and onboarding
API Development
On the backend, I built secure REST APIs: - Authentication with JWT tokens - Role-based access control - Input validation and sanitization - Comprehensive error handling - API documentation with Postman collections
MongoDB Optimization
As data grew, database performance mattered: - Compound indexes for common queries - Aggregation pipelines for complex analytics - Data archiving for old metrics - Regular backups and replication
What Made KPEIZ Special
It wasn't just features—it was the combination: - Real social media data, not demos - Actionable insights, not just numbers - Flexible enough for agencies, simple enough for individuals - Affordable pricing that scaled with usage
Key Lessons
- API integration requires defensive programming - Data visualization is an art and a science - Performance matters when dealing with large datasets - Security can't be an afterthought - User feedback drives the best features
The Result
KPEIZ attracted hundreds of users, from solo marketers to agencies managing dozens of clients. The platform processed millions of social media data points, generating insights that informed real marketing strategies.
Personal Growth
This project taught me full-stack development in the truest sense. From social APIs to payment processing, from data visualization to security, I touched every part of the stack. It made me a more complete developer.