ART : Exceptions

Designed With 😇 :

• Mongodb
• Nextjs
• Nodejs
• Py
• TypeScript

Company Details:

1. Company Name: GreenGlow Energy Solutions
2. Description: GreenGlow Energy Solutions specializes in providing eco-friendly and sustainable energy solutions, including solar panel installation, wind turbine technology, and energy-efficient appliances for residential and commercial properties.
3. Domain: Renewable Energy
4. Objective of Ad: Increase awareness and drive customer engagement for their new solar panel technology, which promises 20% higher efficiency than current market standards.

Top Competitors:

1. Tesla Energy
2. • Domain: Renewable energy and solar technology (SolarCity).
   • Ads to scrape: Tesla's campaigns promoting solar roofs or Powerwall systems.
2. Sunrun
3. • Domain: Residential solar energy systems.
   • Ads to scrape: Sunrun's campaigns about affordable leasing options for solar panels.
3. First Solar
4. • Domain: Commercial and utility-scale solar energy solutions.
   • Ads to scrape: First Solar's campaigns highlighting energy savings and environmental impact.

GitHub Link 🔗

• https://github.com/DhruvPatel284/supermind-ART.git

Deploy Link 🔗

• https://supermind-art.vercel.app/

Problem it solves 🙅‍♂️

• Advertising is a crucial aspect of any company's success, but creating effective advertisements is challenging due to: Uncertainty about Competitor Strategies: Companies often lack clear insights into what works for their competitors in similar markets. Understanding Customer Preferences: Analyzing audience reactions to competitor ads (e.g., comments on YouTube) is time-consuming and complex. Data-Driven Decision Making: Without reliable data, companies struggle to optimize their campaigns and stand out. Practical Applications This project addresses these challenges by providing a platform that: Analyzes Competitor Ads: Scrapes advertisements of top competitors for the selected ad objective, helping companies understand industry trends and successful strategies. Customer Sentiment Analysis: Extracts and analyzes comments on competitor ads, offering insights into customer preferences, pain points, and what resonates most effectively. Campaign Optimization: Provides actionable feedback, enabling companies to craft ads that replicate the success of competitors while avoiding common pitfalls. Efficient Resource Utilization: Reduces guesswork and saves time by delivering targeted insights, allowing businesses to focus on creating impactful advertisements. How It Enhances Existing Tasks Improved Targeting: By analyzing successful ads, companies can design highly targeted campaigns that better meet audience expectations. Enhanced Creativity: Insights into competitor strategies spark ideas for unique and innovative campaigns, ensuring differentiation. Increased ROI: By identifying what works, companies can allocate resources more effectively, improving ad performance and return on investment. Broader Impact Safety in Investment: Ensures marketing budgets are spent effectively by relying on data-backed strategies rather than assumptions. Market Competitiveness: Levels the playing field for smaller companies by granting them insights typically accessible only to larger corporations. Customer-Centric Approach: Helps companies align their messaging with customer expectations, fostering trust and brand loyalty.

Challenges I ran into 🙅‍♂️

• 1. Scraping Competitor Ads Challenge: Extracting ads and their associated data (e.g., transcripts, comments) from platforms like YouTube was difficult due to dynamic content loading, anti-scraping measures, and inconsistent data formats. Resolution: Dynamic Content Loading: Used tools like Selenium or Puppeteer to interact with dynamically loaded pages and ensure all content was visible before scraping. Anti-Scraping Measures: Implemented randomized headers, delays, and proxy rotation to avoid detection. Data Normalization: Processed extracted data using Python libraries like BeautifulSoup and Pandas to ensure consistent formatting and usability. 2. Analyzing User Comments for Sentiment Challenge: Competitor ads often have thousands of comments, making it difficult to filter relevant ones and perform meaningful sentiment analysis. Resolution: Filtering Noise: Applied natural language processing (NLP) techniques using libraries like NLTK and SpaCy to identify relevant keywords and eliminate spam or irrelevant comments. Sentiment Analysis: Used pre-trained models like VADER or Hugging Face transformers to classify comments as positive, negative, or neutral. Relevance Scoring: Implemented a scoring system to prioritize comments that directly aligned with the ad objective. 3. Identifying Competitors Challenge: Accurately identifying the top competitors for a given company and industry was challenging, especially when operating in niche markets. Resolution: Integrated APIs like Google Search or SimilarWeb to identify top competitors based on search results, market data, and traffic analytics. Provided a manual override option, allowing users to input competitors directly for more precise control. 4. Real-Time Insights Challenge: Providing real-time insights while scraping and analyzing large datasets was computationally intensive. Resolution: Used asynchronous programming (e.g., Python’s asyncio) to parallelize tasks like scraping and processing. Cached results using Redis to reduce repeated computations and enhance response times. Leveraged cloud platforms (e.g., AWS Lambda, Google Cloud Functions) for scalable and efficient processing. 5. User-Friendly Interface Challenge: Designing an intuitive UI that conveyed complex insights (e.g., competitor analysis, sentiment trends) in an understandable way. Resolution: Built interactive dashboards using React.js and Chart.js to visually represent key metrics (e.g., sentiment breakdown, competitor strengths). Included tooltips, filters, and export options to make the interface more user-friendly and customizable.