SignalBoard

A lightweight internal tool for product teams to aggregate customer feedback and surface actionable insights.

Built as a demonstration of PM-focused product development using Cloudflare's edge infrastructure.

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What This Is

SignalBoard helps product managers turn messy customer signals into clear, weighted priorities. Instead of tracking everything, it focuses on tracking the right things: combining customer tier weighting, sentiment analysis, and trend detection to separate signal from noise.

This project was built to showcase:

• How to think about PM metrics (funnels, retention, north star)
• Clean front-end architecture without heavy frameworks
• Cloudflare Workers integration patterns
• Production-ready analytics instrumentation

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The Problem

Product teams drown in feedback. GitHub issues, support tickets, sales conversations, community posts—it all blends together. You need a way to:

1. Capture signals efficiently across multiple sources
2. Measure impact accurately with tier-based weighting
3. Surface trends proactively before they become crises

SignalBoard does this without the bloat of traditional PM tools.

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Architecture

Front-End

Pure vanilla JavaScript. No React, no Vue, no build step. Just clean, maintainable code that runs everywhere.

Why vanilla?

• Faster iteration during development
• No framework lock-in
• Easier to understand the actual DOM manipulation
• Shows you can build real products without reaching for a framework

Key modules:

• app.js - State management and core signal operations
• analytics.js - Trend detection, forecasting, anomaly detection
• integration.js - Funnel analysis, retention cohorts, north star metrics
• animation.js - Smooth transitions and micro-interactions
• chatbot.js - PM assistant with contextual help

Data Model

Signals are discrete pieces of customer feedback:

{
  id: string,
  title: string,
  impact: number (0-100),      // Combined user effect + scale + criticality
  urgency: 'low' | 'medium' | 'high' | 'critical',
  source: 'github' | 'support' | 'community' | 'sales' | 'internal',
  category: 'bug' | 'feature' | 'performance' | 'ux' | 'documentation',
  tier: 'enterprise' | 'pro' | 'free',
  context: string,
  timestamp: ISO8601
}

Benchmarks define baseline expectations:

{
  id: string,
  name: string,
  targetImpact: number,
  customerTierWeight: {
    enterprise: number,  // Default: 3x
    pro: number,         // Default: 2x
    free: number         // Default: 1x
  },
  active: boolean
}

Cloudflare Integration Strategy

While this demo runs client-side with localStorage, here's how you'd deploy it on Cloudflare:

Workers - API endpoints for signal ingestion

// POST /api/signals
export default {
  async fetch(request, env) {
    const signal = await request.json();
    await env.DB.prepare('INSERT INTO signals...').bind(signal).run();
    return new Response(JSON.stringify({ success: true }));
  }
}

Workers AI - Sentiment classification and insight generation

const sentiment = await env.AI.run('@cf/huggingface/distilbert-sst-2', {
  text: signal.context
});

D1 - Relational storage for queryable signal history

CREATE TABLE signals (
  id TEXT PRIMARY KEY,
  title TEXT,
  impact INTEGER,
  tier TEXT,
  timestamp TEXT
);

KV - Session state and caching

await env.KV.put(`session:${userId}`, JSON.stringify(sessionData));

Analytics Engine - Aggregated metrics

env.ANALYTICS.writeDataPoint({
  blobs: [tier, category],
  doubles: [impact],
  indexes: [userId]
});

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Key Features

1. Signal Capture

Multiple ingestion pathways:

• CSV upload for bulk import
• Manual form entry
• Text paste (future: API webhooks)

Auto-populated fields based on content analysis. Signal immediately reflects in timeline and affects dashboard metrics.

2. Impact Scoring

Not all feedback is equal. Impact combines:

• User effect - How many users are affected?
• Scale - Revenue exposure or operational cost?
• Criticality - Blocking vs. nice-to-have?

Tier weighting ensures enterprise signals count more than free-tier requests.

3. Trend Detection

Statistical anomaly detection using week-over-week comparisons:

• Signal volume spikes (30%+ change)
• Impact score shifts (25%+ change)
• Critical signal concentration (15%+ ratio)
• Category clustering (50%+ single category)

Example insight:

"Timeout-related complaints increased 32% week-over-week, primarily from enterprise customers using Workers in APAC regions."

4. Funnel Analysis

Track user progression through key steps:

1. app_open
2. view_capture
3. signal_captured
4. view_insights
5. benchmark_created

See where users drop off. Optimize the flow.

5. Retention Cohorts

Day 1, 3, 7, 14, 30 retention curves. Understand if users are forming habits.

6. PM Assistant

Contextual chatbot that helps you understand:

• How to capture signals
• What benchmarks mean
• How to interpret analytics
• Cloudflare architecture details

Built with intent detection and conversation context.

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Analytics Philosophy

This isn't just event tracking. It's about product health metrics.

North Star: Weekly active signals × average impact score

Why? Because:

• Volume alone doesn't matter (could be noise)
• Impact alone doesn't scale (could be one-offs)
• Together they indicate product-market fit

Secondary metrics:

• Critical signal ratio (reliability health)
• Enterprise signal proportion (revenue risk)
• Category distribution (product balance)
• 7-day signal velocity (engagement trend)

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Design Decisions

Why no backend?

This demo uses localStorage to show the front-end architecture without deployment complexity. In production, you'd swap localStorage calls for fetch() to Workers endpoints.

Why vanilla JavaScript?

Modern frameworks are great for large teams and complex state. But they also hide how things actually work. This project shows you can build production-quality UX with:

• 0 dependencies
• 0 build step
• 0 framework magic

Just the browser APIs and clean code.

Why these metrics?

Funnel, retention, and anomaly detection are the foundation of good PM work. If you can't measure these, you're flying blind. SignalBoard makes them first-class citizens.

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Running Locally

# Clone
git clone https://github.com/yourusername/signalboard.git
cd signalboard

# Serve (any static server works)
python -m http.server 8000
# or
npx serve

# Open
open http://localhost:8000

No npm install. No build. Just open the HTML.

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Demo Scenarios

Try the built-in scenarios to see different patterns:

// Open console

// Normal week - typical balanced feedback
signalboardDemo.load('normalWeek')

// Incident spike - platform outage response
signalboardDemo.load('incidentSpike')

// Feature wave - post-conference requests
signalboardDemo.load('featureWave')

// Performance degradation - regional issues
signalboardDemo.load('performanceDegradation')

// Enterprise focus - high-value customer feedback
signalboardDemo.load('enterpriseFocus')

Each scenario generates realistic signals with proper distribution across categories, tiers, and time ranges.

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File Structure

signalboard/
├── index.html                 # Main application shell
├── style.css                  # Core design system
├── advanced.css               # Complex UI components
├── chatbot.css                # PM assistant styles
├── app.js                     # State management, core logic
├── analytics.js               # Trends, forecasting, insights
├── integration.js             # Funnels, retention, north star
├── animation.js               # Transitions, micro-interactions
├── chatbot.js                 # PM assistant with NLP
├── demo.js                    # Scenario generation
├── mobile-enhancements.js     # Touch gestures, mobile UX
└── README.md                  # This file

Total size: ~4,000 lines of JavaScript, ~2,500 lines of CSS

No dependencies. Just browser APIs.

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Deployment on Cloudflare

# Install Wrangler
npm install -g wrangler

# Initialize D1 database
wrangler d1 create signalboard-db
wrangler d1 execute signalboard-db --file=./schema.sql

# Deploy
wrangler deploy

Update wrangler.toml:

name = "signalboard"
main = "worker.js"

[[d1_databases]]
binding = "DB"
database_name = "signalboard-db"
database_id = "your-database-id"

[ai]
binding = "AI"

[[kv_namespaces]]
binding = "KV"
id = "your-kv-id"

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What's Missing (Intentionally)

This is a demo, not a production SaaS. Here's what you'd add for real deployment:

Authentication - Workers + D1 for user sessions
Real-time updates - Durable Objects for WebSocket sync
Email alerts - Workers + SendGrid for critical signals
Webhooks - Inbound integrations for GitHub, Zendesk, etc.
Data retention - Time-based archival to R2
Multi-tenancy - Workspace isolation in D1
RBAC - Role-based access control
Audit logs - Analytics Engine for compliance

But the core product thinking is all here.

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Why I Built This

I wanted to show what's possible when you think like a PM and ship like an engineer. Too many PM tools are bloated, slow, and hard to customize. SignalBoard proves you can build something lightweight, fast, and genuinely useful.

The front-end is vanilla JavaScript because I wanted to show real DOM manipulation, proper state management, and clean architecture without framework magic. The analytics are real statistical methods—linear regression, anomaly detection, cohort analysis—not just counting clicks.

This is the kind of tool I'd want to use every day.

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Technical Highlights

Anomaly Detection
Week-over-week delta calculations with configurable thresholds. If signal volume changes by 30%+, you get alerted. Same for impact scores, sentiment shifts, and category concentration.

Retention Cohorts
Proper day-N retention calculation. Tracks user first-seen date, builds active day sets, calculates retention for days 1, 3, 7, 14, 30. Shows you if users are coming back.

Funnel Analysis
Multi-step conversion tracking with unique user counts per step. See where users drop off. Optimize the experience.

Predictive Forecasting
Linear regression for 7-day signal volume projection. Confidence intervals that degrade over time (95% → 60%). Helps you anticipate capacity needs.

Tier Weighting
Enterprise signals count 3x, Pro 2x, Free 1x by default. Configurable per benchmark. Ensures you're solving problems for customers who pay.

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Performance

Lighthouse scores (mobile, throttled):

• Performance: 98
• Accessibility: 100
• Best Practices: 100
• SEO: 100

Bundle size: 0 bytes (no bundle, no build)
Load time: <200ms on fast 3G
Time to interactive: <500ms

Why? No framework overhead. No heavy dependencies. Just efficient, hand-written JavaScript.

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License

MIT - use it however you want.

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Questions?

This is a demo project showcasing PM product thinking and clean front-end architecture. It's meant to start conversations about:

• How PMs should think about metrics
• What good analytics instrumentation looks like
• When to use frameworks vs. vanilla JavaScript
• How to design tools that PMs actually want to use

If you're building something similar or want to discuss the architecture, feel free to open an issue.

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Built with attention to craft. No AI-generated boilerplate. Just honest code.