Description

Current State

Overview and Summary

ShopSmart, an e-commerce platform with over 1 million active users, offers a wide range of products, including electronics, clothing, home goods, and more. Despite a large customer base, ShopSmart faces challenges in personalizing user experiences and improving customer engagement. The current system lacks an effective recommendation engine, leading to suboptimal product discovery and lower conversion rates. To address these issues, ShopSmart aims to implement an advanced E-Commerce Recommendation System to enhance user experience, increase sales, and improve customer retention.

Problem Statement

The current e-commerce platform at ShopSmart has several challenges:

• Inadequate product recommendations resulting in poor user engagement.

• Difficulty in personalizing user experiences based on individual preferences.

• Low conversion rates due to ineffective product discovery.

• Limited insights into customer behavior and preferences.

• Challenges in retaining customers and encouraging repeat purchases.

Objectives of the E-Commerce Recommendation System

• Provide personalized product recommendations to enhance user engagement.

• Improve product discovery by suggesting relevant items based on user behavior.

• Increase conversion rates through targeted recommendations.

• Gain insights into customer preferences and purchasing patterns.

• Enhance customer retention by offering a personalized shopping experience.

Business Objectives and Success Criteria

• Business Objective 1: Increase average user session duration by 20% within 6 months.

• Business Objective 2: Boost conversion rates by 15% within 6 months.

• Business Objective 3: Achieve a 25% increase in repeat purchase rates within the first year.

• Business Objective 4: Improve customer satisfaction scores by 30% within the first year.

Future State

Client Requirements

• The system shall analyze user behavior and purchase history to generate personalized recommendations.

• The recommendation engine shall use machine learning algorithms to improve accuracy over time.

• The system shall provide real-time recommendations on product pages, home pages, and during checkout.

• The system shall include collaborative filtering, content-based filtering, and hybrid recommendation techniques.

• The platform must support A/B testing to evaluate the effectiveness of different recommendation strategies.

• The system shall generate detailed analytics and reports on recommendation performance and customer behavior.

• The system must integrate seamlessly with the existing e-commerce platform.

System Requirements

• Scalability and Performance: Support up to 10 million daily recommendation requests with minimal latency.

• Usability: Provide clear and intuitive recommendation displays for users.

• Security: Ensure secure handling of user data in compliance with data privacy regulations.

• Interoperability: Integrate with existing e-commerce systems and third-party data sources.