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Computer Vision solution for a cosmetic retailer with Recommendation System
This AI-powered mobile solution is designed for a renowned beauty retailer based in the US. It aims to personalize the user's skincare routine, providing a smarter and highly customized experience for anti-aging treatments. In order to enhance the customer experience, the client expressed the desire to integrate a recommendation system powered by ChatGPT.
Project background
Not so long ago the sale of cosmetic products depended mostly on the physical store experience: the customer needed to try out different shades or beauty products before they could find the right one that suits them best.
Retailers now have to meet the growing expectations of the customers for engaging experiences in stores. That’s why the consumers’ experience in beauty stores is now greatly transformed by technology, namely by diverse mobile opportunities: starting with chatbots and finishing with AI-powered solutions to pick up needed products fast.
Our client, a cosmetic retailer, has a variety of beauty items, including products for all skin types, under-eye care, and decorative cosmetics. They were looking for innovative mobile solutions that can analyze the customer’s face and type of skin, and then offer the best-suited product and, thus, improve their customers’ experience.
- Duration: November 2020 – April 2021
- Location: New York, US
- Industry: Beauty/Health/Retail
- Services:
- Custom software development, Native mobile app development, UI/UX design
Business needs
The client’s beauty company has a wide range of beauty products and many customers in the United States. To serve everyone, the company needed to train a large staff of consultants. Therefore, to optimize processes and minimize human labor, an idea of an app appeared. This solution should help end customers choose the right cosmetics themselves. Thanks to AI-powered mobile solution the number of dissatisfied customers should decrease, and the costs of hiring and training new consultants should be optimized.
Product features
- Face analysis. Analyzes collected user data received from a photo (selfie)
- Recommendation System powered by Chat GPT. Based on the collected data, makes recommendations on available beauty products
- Helpful hints. Offers hints and tips for the buyers to simplify the selection of items in the catalog
Solution
CHI Software developed a mobile application based on Computer Vision, photo face recognition, and powered by ChatGPT capabilities. Here’s how it works:
– A user takes a selfie
– Using the detectors based on the histograms of forfeited gradients from the Dlib library, the program can accurately determine the user’s face on the photo
– Using the same approach, we were able to train a system that can detect 126 feature points on the user’s face
– The app then analyzes the unique facial features of a user and adopts the user profile
– The applied AI technologies then help select the needed skincare
– Our system allows to perform a detailed/smart analysis of the user’s face
– The app now provides personalized skincare and beauty recommendations with the help of ChatGPT API integration.
Our technology stack
- C++
- Open CV
- Dlib
- Caffe
- Swift 4.2
- Alamofire
- REST
- AVFoundation
- CoreGraphics
- CoreImage
- CoreAnimation
- Multithreading
- UserDefaults
- Firebase analytics
- SafariServices (API)
- PHP 7.2.12
- MySQL
- MariaDB (AI)
- AWS EC2, S3, RDS
Client values
- Increased sales and revenue: By implementing personalized recommendations that drive cross-selling and upselling, our client anticipates achieving a sales growth of up to 10%, as relevant product suggestions lead to higher order values.
- Enhanced customer experience: Our client aims to improve overall customer satisfaction, loyalty, and experience by providing personalized suggestions that cater to individual preferences and needs, ultimately leading to an expected customer retention growth of 5-7%.
- Improved customer retention: Through the use of personalized recommendations based on customer interests, our client expects to foster higher customer engagement, resulting in an anticipated growth of 8-10% or more in customer retention rates.
- Efficient inventory management: Our client looks forward to leveraging recommendation systems to optimize inventory by analyzing customer preferences, which is expected to potentially reduce inventory costs by 5-8% and minimize stockouts.
- Increased cross-selling and upselling: By utilizing personalized recommendations to promote complementary and higher-value products, our client expects to maximize revenue through cross-selling and upselling opportunities, aiming for a growth of 10% in revenue from these strategies.
Employee testimonial
Artem Luban C++ Developer
The beauty app was an interesting project in terms of technology with a revolutionary idea to improve the beauty industry behind it. I've practiced and boosted my skills in implementing Computer Vision in Face Analysis business solutions. As for challenges, the most difficult part was the detection of skin defects like acne. There needed to be clear criteria for their detection since defects' visibility can be very different under different lighting conditions.