OURARTfinder

Designed With 😇 :

• Py
• React

ART Finder streamlines the research phase of ad creation by automating data collection and analysis. It gathers insights from platforms like Google, YouTube, Reddit, Quora, and app reviews to identify user pain points, triggers, and trends.

The tool analyzes competitor ads to uncover effective hooks, CTAs, and content formats, providing marketers with actionable insights and personalized recommendations. Its user-friendly dashboard visualizes data with graphs, word clouds, and sentiment analysis, while offering direct links to relevant videos and ads for validation.

With a simple input interface for brand details and an integrated chatbot for support, ART Finder helps marketers create data-driven, user-focused ad campaigns with ease.

GitHub Link 🔗

• https://github.com/NawadhaJadiya/ARTfinder.git

Deploy Link 🔗

• https://our-art-finder.vercel.app/

Problem it solves 🙅‍♂️

• ART Finder addresses the challenge of conducting time-consuming, manual research during the ad creation process. Marketers often struggle to identify user pain points, analyze competitor strategies, and generate actionable insights efficiently. ART Finder automates data gathering and analysis from platforms like Google, YouTube, Reddit, Quora, and app reviews. It streamlines the process of uncovering trends, effective hooks, CTAs, and user triggers, enabling marketers to create impactful, data-driven ad campaigns with minimal effort.

Challenges I ran into 🙅‍♂️

• The primary challenge faced during the development of ART Finder was scraping data from platforms like YouTube and Google Trends. Extracting meaningful insights required overcoming obstacles such as dynamic content loading, API restrictions, and anti-scraping mechanisms. For YouTube, gathering video details, reviews, and comments necessitated parsing complex HTML structures while ensuring data accuracy. With Google Trends, the challenge was accessing real-time trend data and structuring it for analysis. Implementing robust solutions, including the use of APIs, advanced scraping libraries, and adherence to platform policies, was crucial in ensuring reliable data extraction while maintaining the integrity and legality of the process.