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Suggest relevant products to users based on their history and behavior, increasing the average ticket and improving the customer experience.

Type of AI solution: AI agent that does not include a chatbot (it is possible to integrate a conversational interface or AI chatbot, if required)

Traditional process: In traditional e-commerce models, product recommendations are often generic, based on popular product lists or broad categories. This limits relevance for users and can lead to less satisfactory experiences, negatively impacting conversion rates and the average ticket.

Application of Machine Learning (ML):

1. User history analysis: The system collects and analyzes user behavior data, such as purchase history, viewed products, browsing time, and interaction patterns with the site.
2. Customer profile generation: Using ML algorithms, the system creates personalized profiles for each user, identifying preferences, interests, and recurring patterns.
3. Dynamic recommendation system: Based on the customer's profile, the system suggests relevant products, including:

• Similar products: Alternatives to items the user has explored.
• Complements: Items that accompany or enhance products in their cart.
• Trend-based suggestions: Popular products within a segment related to their interests.

1. Real-time personalization: As the user browses, the system dynamically adjusts recommendations based on their current behavior on the site, improving relevance with each interaction.
2. Omnichannel integration: Recommendations are synchronized across multiple channels, such as the website, mobile applications, and personalized emails, offering a consistent experience.

Benefits:

• Increase in average ticket: By suggesting complementary or higher-value products, upselling and cross-selling are encouraged.
• Improved customer experience: Personalized recommendations generate a feeling of exclusive attention, increasing satisfaction and loyalty.
• Higher conversion rate: Users are more likely to complete a purchase when they receive relevant suggestions.
• Catalogue optimization: Constant analysis of customer behavior allows identifying products with greater potential, helping to define inventory and marketing strategies.

Conclusion: A personalized recommendation system based on Machine Learning is a powerful tool for e-commerce. It not only drives revenue growth by increasing the average ticket and conversions but also improves the user experience by offering suggestions tailored to their needs. This solution positions e-commerce businesses as leaders in customer satisfaction and loyalty, guaranteeing a competitive advantage in a highly dynamic market.