Tasks
- Deliver personalized product recommendations for every online customer
- Increase ecommerce revenue and average order value
- Improve customer engagement and loyalty
- Introduce AI-powered personalization without building an in-house ML platform
- Create a scalable foundation for future omnichannel customer experiences
Results
- 14% increase in ecommerce revenue
- 14% increase in average order value
- 200% increase in customer engagement with personalized recommendations
- AI-powered recommendation engine implemented using Amazon Personalize
- Foundation established for future omnichannel personalization and AI initiatives
Description
MonAmie is one of Kazakhstan's largest beauty retailers, operating 29 stores and employing more than 1,000 people. The company offers products from hundreds of global beauty brands, including Chanel, Bulgari, and many other premium manufacturers.
Committed to delivering exceptional customer experiences both online and in-store, MonAmie continuously invests in digital transformation and innovative technologies that strengthen customer engagement and business growth.
Challenge
As Kazakhstan's highly competitive beauty retail market continued to evolve, MonAmie's ecommerce platform relied on traditional rule-based recommendation algorithms. Every visitor received nearly identical product suggestions regardless of browsing behavior, purchase history, or personal preferences.
This limited the effectiveness of online merchandising, reduced conversion opportunities, and made it difficult to build long-term customer loyalty.
At the same time, MonAmie was developing its mobile application and wanted to deliver the same personalized shopping experience online that customers already enjoyed in its physical stores.
Although the company recognized the value of artificial intelligence and machine learning, its internal engineering team lacked the expertise required to build and maintain a recommendation engine from scratch.
To modernize its ecommerce platform, MonAmie partnered with AWS and Softprom to implement an intelligent recommendation solution based on Amazon Personalize.
Solution
Softprom, an AWS Partner, worked closely with MonAmie throughout the project, providing consulting, architecture guidance, implementation support, and local technical assistance during deployment.
After evaluating several cloud platforms, MonAmie selected AWS because of its mature AI services, ease of implementation, and proven experience in delivering personalized customer experiences at global scale.
The project centered around Amazon Personalize, a fully managed machine learning service that enables organizations to create highly relevant product recommendations using technologies developed for Amazon.com.
Customer profiles, browsing history, and purchase data from MonAmie's ecommerce platform were securely imported into Amazon Personalize datasets to train recommendation models.
The implementation included two core recommendation strategies:
- User-Personalization-v2, which generates individualized product recommendations based on each customer's behavior and interests.
- Item-Attribute-Affinity, which identifies customer preferences for specific product attributes and recommends similar products across categories. For example, customers purchasing a fragrance with a particular scent profile receive recommendations for other products with comparable characteristics.
Throughout the implementation, AWS specialists provided best practices and technical guidance, while Softprom managed the practical deployment, integration, and coordination with the customer's technical teams.
The result was an AI-powered recommendation engine capable of generating personalized product suggestions in near real time, significantly improving the online shopping experience.
AWS Services Used
Amazon Personalize
Amazon Personalize is a fully managed machine learning service that enables organizations to build highly personalized customer experiences without requiring extensive ML expertise. Using technologies developed for Amazon.com, it delivers individualized recommendations in near real time based on customer behavior and preferences.
Amazon Rekognition (planned)
Following the successful implementation of Amazon Personalize, MonAmie plans to extend AI capabilities into its physical stores using Amazon Rekognition. The retailer aims to recognize returning customers and provide sales consultants with personalized recommendations during in-store visits, creating a seamless omnichannel shopping experience.
Solution Architecture
The solution integrates MonAmie's ecommerce platform with Amazon Personalize to continuously generate personalized recommendations based on customer interactions.
Customer behavior, purchase history, and product catalog information are securely synchronized with Amazon Personalize datasets, where machine learning models analyze user preferences and generate individualized recommendations.
The recommendation engine delivers personalized product suggestions to the ecommerce website in near real time, enabling every customer to receive content tailored to their interests while continuously improving recommendation accuracy as new interaction data becomes available.
Business Benefits
Implementation of Amazon Personalize delivered measurable business value almost immediately.
The retailer achieved a 14% increase in ecommerce revenue while simultaneously increasing the average order value by 14%. Even more significantly, customer engagement with personalized recommendations increased by 200%, demonstrating the effectiveness of AI-driven personalization.
Beyond these measurable improvements, MonAmie established a scalable AI platform that supports future innovation. The project also accelerated internal adoption of machine learning technologies, helping technical teams recognize the business value of AI solutions.
Building on this success, MonAmie plans to expand personalization beyond ecommerce by introducing AI-powered customer recognition and recommendation capabilities in physical stores, creating a truly unified omnichannel customer experience.
Client Feedback
"When you start implementing something new, you get some resistance. But when we started using Amazon Personalize, our team understood the benefits and how easy it is. After that, they loved it."