Jatin Kshatriya
Collaborators
Aakash Gorai
@aakashgorai256460
Himanshu Agnihotri
@himanshuagni045007
HARSHAL RATHORE
@harshalrathore2014585
Jatin Kshatriya
Adspire
ART (Automated Research and Trigger Finder) - MotionMinds
Designed With 😇 :
# **ART Finder: Automated Research and Trigger Finder**
## **Overview**
ART Finder is a powerful tool designed to automate the research phase of ad creation and content brainstorming. It gathers, analyzes, and organizes data from multiple sources to provide actionable insights for marketers, content creators, and advertisers. The platform simplifies the identification of user pain points, trends, and effective strategies, enabling the creation of high-performing, user-centric content.
## **Features**
1. **Comprehensive Research Automation**:
- Scrapes data from Google (blogs, articles), YouTube (video metadata and comments), Reddit, Quora, and app reviews.
- Extracts user pain points, emotional triggers, and trending topics.
2. **Competitor Ad Analysis**:
- Analyzes high-performing hooks, CTAs, and content formats used in competitor ads on YouTube.
3. **Actionable Insights Generation**:
- Summarizes key findings such as user problems and effective solutions.
- Suggests best-performing hooks and CTAs tailored to the topic and audience.
4. **Reference Dashboard**:
- Provides direct links to top-performing YouTube videos, competitor ads, and articles.
- Visualizes insights through graphs, word clouds, and sentiment analysis.
5. **User-Centric Interface**:
- Easy input fields for topics or brand keywords.
- Intuitive dashboard showcasing insights and recommendations in a glanceable format.
## **Key Data Sources**
- **Google**: Blogs, articles, and trending search results.
- **YouTube**: Video metadata, comments, and engagement metrics.
- **Reddit**: Discussions, questions, and posts from relevant subreddits.
- **Quora**: User-generated Q&A content on relevant topics.
- **App Reviews**: Insights from customer feedback and reviews on Google Play Store.
## **Technology Stack**
### **Frontend**:
- **Framework**: Next.js
- **Visualization**: D3.js or Chart.js
- **Styling**: TailwindCSS
### **Backend**:
- **Framework**: Django REST Framework
- **Data Scraping**: BeautifulSoup and Scrapy
- **NLP**: SpaCy, Hugging Face Transformers, or OpenAI API
- **Database**: PostgreSQL
### **AI and Data Analysis**:
- **Sentiment Analysis**: TextBlob or VADER
- **Recommendations**: OpenAI API
### **Deployment**:
- **Frontend**: Vercel
- **Backend**: AWS or Render
- **Database**: Supabase or AWS RDS
## **How It Works**
1. **Input Phase**:
- User inputs a topic or keyword (e.g., "time-saving tools").
- Optionally, brand-specific guidelines or tone preferences can be added.
2. **Processing Phase**:
- ART Finder scrapes data from selected platforms using APIs or scraping tools.
- Processes text using Natural Language Processing (NLP) for insights and sentiment analysis.
3. **Output Phase**:
- Displays actionable insights in a user-friendly dashboard.
- Provides suggested hooks, CTAs, and links to competitor ads.
- Visualizes trends with graphs and word clouds.
## **Use Cases**
- Marketing teams crafting ad campaigns.
- Content creators looking for new ideas.
- Product teams analyzing customer feedback for pain points.
- Advertisers analyzing competitors for strategies and trends.
## **Benefits**
- Saves hours of manual research.
- Identifies user pain points and emotional triggers effortlessly.
- Provides a clear direction for creating effective, user-focused ads.
- Consolidates multiple data sources into a single platform.
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**ART Finder**: Transform your research process and create ads that convert with data-driven insights!
GitHub Link 🔗
Deploy Link 🔗
Problem it solves 🙅♂️
- ART Finder eliminates the time-intensive manual research process for ad creation, helping marketers quickly identify user pain points, trends, and effective strategies.
Challenges I ran into 🙅♂️
- Data scraping and formatting.