ASOS

Objective

To improve the shopping experience for their diverse consumer base, ASOS hired Inventix to rebuild and develop their current mobile applications (iOS and Android) and a responsive online platform. A cutting-edge, user-friendly layout, frictionless shopping and checkout processes, and better personalization options were all goals of the project.

1. Vast Product Directory

Managing and presenting a sizable, dynamic inventory of apparel and cosmetics that includes information on sizes, colors, prices, and availability.

Solution: To effectively handle the storage, retrieval, and real-time changes of product data, Inventix designed a scalable database architecture. Advanced search algorithms made sure that customers could find the things they were looking for promptly.

2. Real-Time Pricing and Inventory Updates

Providing users with up-to-date details on product availability, price adjustments, and promotions is a challenge.

Solution: To ensure users always saw accurate and current information, Inventix created real-time inventory and pricing synchronization with ASOS’ internal systems.

3. Cross-Platform Consistency

Providing a uniform purchasing experience across online platforms and mobile apps is a challenge.

To guarantee consistency in the user experience, Inventix used a cross-platform development framework and responsive design concepts. Both the web platform and the mobile apps shared the same codebase.

4. Protected Payment and Checkout Processing

Creating secure online payment options for purchases of clothing and cosmetics presents a challenge.

Solution: To protect user financial data, the development team integrated various payment methods and put strict security controls in place. Systems for fraud detection and encryption have been implemented.

5. Individualization and Suggestions

Enhancing personalisation features, such as product recommendations based on user preferences and behavior, is a challenge.

To examine user behavior, clickstream data, and purchase histories, Inventix deployed machine learning algorithms. Personalized product recommendations and carefully selected collections were created using these findings.

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