Page 417 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Future Development
1. AI-Driven Predictions: The future of SCS may involve predictive analytics powered by advanced machine learning
techniques. These systems could predict when a price is likely to drop, based on historical patterns and market conditions,
offering consumers a more proactive shopping experience.
2. Personalized Shopping Assistants: AI-powered smart comparison systems may evolve into fully personalized shopping
assistants that not only compare prices but also recommend products based on an individual's preferences, purchase
history, and even social media activity.
3. Seamless Integration with Other Technologies: The future of SCS will likely see deeper integrations with other e-
commerce technologies, such as virtual reality (VR) for product visualization, chatbots for customer service, and voice
assistants for hands-free shopping.
4. Blockchain for Transparency: Blockchain technology could play a role in enhancing transparency in price comparisons
by providing tamper-proof records of pricing history and product availability, thereby increasing consumer trust.
5. Global Expansion: As global e-commerce grows, smart comparison systems could expand to include international price
comparisons, offering consumers a global view of pricing for the same product, enabling smarter cross-border shopping
decisions.
6. Gaps in existing research: Smart comparison systems are transforming online shopping by providing real-time price
insights, enabling consumers to make better purchasing decisions. These systems leverage technologies like big data, AI,
and machine learning to track price fluctuations across multiple platforms, compare prices, and offer personalized
recommendations. They not only show current prices but also provide price history, alerts for price drops, and predictions
for future trends, ensuring shoppers get the best deals. Advanced algorithms and dynamic pricing adjust prices based on
market conditions, giving consumers a competitive edge in timing their purchases. These tools empower users to shop
smarter and save money.
App Workflow
1. User Sign-Up/Login
 Sign-Up: User registers using email or social accounts.
 Login: Existing users log in using their credentials.
2. Onboarding and Profile Setup
 Introduction: Brief overview of app features.
 Profile Setup: User inputs preferences (categories of interest, budget, etc.).
3. Product Search and Comparison
 Search: User enters product name or browses categories.
 Real-Time Data Pull: App fetches current prices from various online stores.
 Display Comparison: Users view products with real-time price insights, ratings, and reviews.
4. Filters and Sorting
 Filters: Apply filters like brand, price range, features, availability.
 Sorting: Sort options by price, ratings, newest arrivals, etc.
5. Detailed Product View
 Product Details: Description, features, specifications.
 Price History: Graph showing past price changes.
 User Reviews: Aggregated reviews from multiple platforms.
6. Price Alert Setup
 Alert Creation: User sets price drop alerts for specific products.
 Notifications: Push notifications, emails, or SMS when the price drops.
7. Bookmark and Wishlist
 Save for Later: User can bookmark products or add them to a wishlist.
 Sync Across Devices: Access bookmarks and wishlist across multiple devices.
8. In-App Purchases and Redirect
 Purchase: Direct in-app buying if supported.
 Redirect: App redirects to the online store site for purchase.
9. Post-Purchase Features
 Order Tracking: Real-time tracking for ordered products.
 Feedback: User submits feedback or reviews from within the app.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 407