ART-Finder-green-flags

Designed With 😇 :

• Nextjs
• Tailwind

ART Finder: A platform that automates ad research and analysis to create impactful, user-centric ads.

Key Features

Comprehensive Research: Analyzes data from Google, YouTube, Reddit, Quora, and app reviews.

Competitor Insights: Identifies high-performing hooks, CTAs, and content formats.

Actionable Insights: Summarizes triggers and pain points, offering tailored recommendations.

Intuitive Dashboard: Visualizes insights with graphs, sentiment analysis, and direct references.

GitHub Link 🔗

• https://github.com/Tanish2207/level_supermind

Deploy Link 🔗

• https://level-supermindgreenflags.vercel.app/

Problem it solves 🙅‍♂️

• Creating impactful ads requires extensive research to identify user pain points, understand market trends, and analyze competitor strategies. This process is time-consuming, resource-intensive, and often lacks actionable insights, leading to suboptimal ad performance. ART Finder automates the research phase, gathers data from diverse sources, and analyzes competitor strategies to deliver concise, actionable insights. This empowers marketers to save time, focus on creativity, and craft high-performing, user-centric advertisements.

Challenges I ran into 🙅‍♂️

• Data Collection from Diverse Sources Figuring out an effective way to obtain data from websites like Youtube, Instagram, Google, etc required handling varying APIs, rate limits, and unstructured data formats. Ensuring compliance with platform policies and maintaining ethical data usage was a significant consideration. Competitor Ad Analysis Extracting actionable insights from competitor ads involved recognizing effective hooks, CTAs, and content formats, which required precise pattern recognition and context understanding. Data Analysis Complexity Analyzing unstructured data such as video transcripts, app reviews, and forum discussions required robust natural language processing (NLP) techniques.