ARTFinder - Transforming Data into Your Next Great Ad

Designed With 😇 :

• Django
• Py
• React
• Tailwind

Our solution automates the research and analysis process in ad creation, making it faster and more efficient for marketers. By scraping data from a variety of sources such as Google, Reddit, Quora, app reviews, and more, and storing this data in a DataStax Langflow and Gemini, our system analyzes it to extract valuable insights. This data is processed using a language model to generate recommendations that help advertisers craft highly effective and user-centric advertisements.

Features

Automated Data Scraping: Collects data from Google, YouTube, Reddit, Quora, app reviews, and other sources.

Competitor Insights: Analyzes competitor ads to uncover successful strategies and tactics.

Language Model Insights: Uses advanced AI to generate actionable recommendations based on user pain points and trends.

User-Friendly Interface: Simple input fields for topics and brand guidelines, with easy-to-understand insights and suggestions.

GitHub Link 🔗

• https://github.com/adilns786/LevelSupermind

Deploy Link 🔗

• https://socialserver-8nju.onrender.com/api/post/

Problem it solves 🙅‍♂️

• The objective of ART Finder is to streamline the research phase of ad creation by automating data gathering and analysis. This tool will: Identify user pain points and triggers from multiple data sources such as Google, YouTube, Reddit, Quora, and app reviews. Analyze competitor ads and strategies to uncover high-performing hooks, CTAs, and content formats. Generate actionable insights and suggestions to help marketers craft effective, user-centric ads. Key Features: Comprehensive Research Automation: Scrapes data from blogs, forums, social media, and app reviews. Analyzes YouTube videos and competitor ads to identify trends, pain points, and effective solutions. Actionable Insights Generation: Summarizes key triggers and user problems. Suggests best-performing hooks, CTAs, and solutions tailored to the topic and audience. Reference Dashboard: Provides direct links to scraped YouTube videos and competitor ads for easy validation and inspiration. Visualizes insights with graphs, word clouds, and sentiment analysis. User-Centric Interface: Simple input fields for topics and brand guidelines. Intuitive dashboard showcasing insights and recommendations at a glance.

Challenges I ran into 🙅‍♂️

• DataStax Integration: Integrating DataStax Gemini DB posed challenges due to compatibility issues with certain data formats, as well as difficulties in ensuring smooth communication between the scraping tool and the database. There were also issues related to optimizing the database for large-scale data retrieval and storage, which required fine-tuning for performance. Web Scraping: Scraping data from multiple websites like Google, Reddit, YouTube, Quora, and app stores presented several obstacles: Anti-Scraping Measures: Websites implemented CAPTCHA, IP blocking, and rate limiting, making automated scraping difficult. Dynamic Content Loading: Some websites used JavaScript for dynamic content loading, which made it hard to extract meaningful data without handling asynchronous content loading. Data Structure Inconsistencies: The varying structure of data across platforms required custom parsing logic, leading to time-consuming debugging and adjustments.