Advista

Designed With 😇 :

• Fastapi
• Nextjs
• Py
• Tailwind
• TypeScript

ADVISTA is a solution that identifies user pain points and triggers from multiple data sources like Google, YouTube, Reddit, Quora, blogs, forums, social media, and app reviews. It analyzes competitor ads and strategies to uncover high-performing hooks, CTAs, and content formats based on the target audience, ad objectives, and ad formats. It scrapes data from across the internet, summarizes key triggers and user problems, and suggests the best-performing hooks, CTAs, and solutions tailored to the topic and audience. ADVISTA also provides direct links to scraped YouTube videos and competitor ads for easy validation and inspiration. It visualizes insights with word clouds and includes a chatbot to help generate CTAs and hooks.

GitHub Link 🔗

• https://github.com/ayushjrathod/LevelSuperMind_OutOfBounds

Deploy Link 🔗

• https://youtu.be/ikxR2ujRr5I

Problem it solves 🙅‍♂️

• Problems Solved: We solved the problem of identifying user pain points and triggers by analyzing data from blogs, forums, social media, app reviews, YouTube videos, and competitor ads. ADVISTA helps uncover trends, pain points, and effective solutions. It simplifies the process of finding the best-performing hooks, CTAs, and content formats for different ad objectives and audiences. We made it easier to validate and draw inspiration from YouTube videos and competitor ads by providing direct links to them.

Challenges I ran into 🙅‍♂️

• During development, these were the actual hurdles I faced: Reddit's API changes made it tricky to maintain consistent data collection Different platforms had different data structures - merging insights from YouTube comments, Reddit posts, and Google trends required complex normalization Google search volume data needed to be paired meaningfully with social conversations User testing showed people wanted clean insights highlighting patterns across platforms, not separate data dumps Processing and storing data from multiple platforms quickly became expensive and required careful optimization Early versions struggled to distinguish between genuine trends and temporary platform-specific spikes