🚀 TrendSense: Market Opportunity Discovery Platform

Discover emerging product opportunities by combining TikTok trend signals with Google search data — enabling faster, data-driven product decisions.

Overview

TrendSense is a trend intelligence prototype that aggregates TikTok and Google trend signals to identify and rank emerging food product opportunities, and classify them into develop vs distribute recommendations.

📌 Problem

Businesses—especially small and mid-sized brands—struggle to identify emerging product trends early enough to act on them.

Key challenges include:

• Trend signals are fragmented across platforms (TikTok, Google, etc.)
• Raw data is difficult to interpret without technical expertise
• Decisions are often reactive rather than data-driven
• Lack of a centralized system to validate and prioritize opportunities

As a result, companies miss early-stage opportunities or invest in low-demand products.

💡 Solution

TrendSense aggregates and processes trend data from multiple sources to generate actionable product insights.

The platform:

• Combines TikTok keyword signals with Google search trends
• Cleans and filters raw trend data
• Ranks opportunities based on signal strength and relevance
• Classifies opportunities into:
  • Develop → New product opportunity
  • Distribute → Existing product/category alignment
• Presents results through an interactive dashboard

This enables faster, data-backed decision-making for product strategy.

🧩 Key Features

• 📊 Multi-source trend aggregation (TikTok + Google Trends)
• 🧠 Opportunity ranking and scoring engine
• 🔍 Intelligent filtering of data that does not match minimum criteria
• ⚖️ Business decision classification (Develop vs Distribute)
• 🖥️ Interactive dashboard for exploration and insights
• 🔗 REST API backend powering frontend UI

🏗️ Architecture

High-Level Flow

Components

• Data Ingestion Layer
  Collects raw trend data from TikTok and Google Trends. Each data source is processed independently through separate pipelines, with outputs later combined for ranking and insights.
• Processing Pipeline
  Cleans, filters, and standardizes raw signals to produce high-quality inputs for ranking.
• Ranking & Scoring Engine
  Ranks opportunities based on combined signal strength (e.g., frequency, growth, consistency) and generates an overall opportunity score to identify actionable product opportunities.
• Opportunity Classification Logic
  Classifies ranked opportunities into actionable categories:
  • Develop → New product opportunities
  • Distribute → Existing product alignment opportunities
• Backend (Flask)
  Handles API requests and exposes processed data via endpoints /api/dashboard and /api/browse.
• Frontend (React + Vite)
  Displays dashboard views, ranked opportunities, and keyword insights

📊 Data Sources

• TikTok Trend Signals
  • Derived from curated datasets capturing early-stage consumer interest.
• Google Search Trends
  • Used to validate demand and measure search interest over time.

Note: A combination of real and curated datasets was used for this hackathon prototype.

🧱 API Endpoints

GET /api/health
Simple health check to confirm the backend is running.

{
  "status": "ok"
}

GET /api/dashboard
Returns aggregated insights for the main dashboard view, including:

• Top products (ranked opportunities)
• Trending keywords
• Opportunity highlights
• Summary stats

GET /api/browse
Returns the full ranked list of product opportunities.

• Combines TikTok + Google Trends data
• Filters non-food items
• Deduplicates and ranks by signal score

Each item includes:

• Name, category, score
• Opportunity type (Develop / Distribute)
• Approval status, ingredients, metadata

🏗️ Tech Stack

Frontend

• React
• Vite

Backend

• Python
• Flask

Data

• Google Trends
• TikTok datasets

🎯 How It Works from TikTok datasets and Google Trends

1. Collect trend data
2. Clean and filter non-relevant or duplicate entries
3. Calculate trend signals using keyword popularity (TikTok) and search interest (Google Trends)
4. Rank opportunities using a combined score that prioritizes both rapid growth and sustained demand
5. Classify opportunities into Develop or Distribute based on trend strength and market saturation
6. Serve results via Flask APIs
7. Display insights in an interactive dashboard

⚙️ Setup Instructions

Prerequisites
Make sure you have installed

• Python 3.9+
• Node.js (v16+ recommended)
• npm

Initial setup
Create and activate a virtual environment
Install dependencies using pip install -r requirements.txt

Backend

cd backend  
python app.py

Backend will run at http://127.0.0.1:5000

Frontend

cd frontend  
npm install  
npm run dev

Frontend will run at http://localhost:5173/

Open http://localhost:5173/ to view the application.

Notes

• The frontend is configured to proxy API requests to the Flask backend
• Make sure the backend is running before starting the frontend.

🧠 Challenges and Learning

• Cleaning and filtering trend data
• Designing ranking logic across multiple sources
• Aligning cross-platform signals (TikTok vs Google)

🚀 Future Improvements

• Real-time data ingestion
• ML-based trend prediction
• Cloud deployment
• Improved UI/UX

📷 Demo/Screenshots