Zoomania E-commerce

Custom-developed online store with Machine Learning elements

JobSite technologies

Python
Django
Postgresql
ElasticSearch
HTML5
Vuejs
Javascript
jQuery
CSS3
Celery
Final Result
Zoomania Custom solutions
Our client needed an online store whose functionality goes far beyond the “boxed solutions." The architecture of the Zoomania store is open to improvements and the introduction of new functionality. We developed it taking into account the subsequent transformation into a marketplace. Like all the projects we developed, Zoomania meets all the requirements of Highload-optimization and withstands high traffic and an order flow every second.
Typically, customers of online stores tend to buy quality products at low prices. We've developed a microservice that analyzes competitor sites, compares their prices with Zoomania prices and, using a specific formula, dynamically forms the most attractive price for the product. Thus, we can offer the user the best price on the market, gradually increasing the base of regular customers.
The next challenge was to develop so-called "auto-contractor" for suppliers. We've developed a microservice (with Machine Learning elements) that runs every day through the remaining goods in the warehouse and, using an analytical algorithm, estimates the consumption of goods in conjunction with popularity, and then calculates the required number of goods of each type for the order. Based on these estimates, a list of goods for purchase from suppliers is formed. The administrator pre-moderates the file data, after which it allows the microservice to send it by letter to the mail of the suppliers.
In accordance with the next task, we created a system that analyzes the sale of goods and dynamically generates the number of bonuses that are returned after purchase to the customer’s internal account. For more popular products, bonuses are awarded less, for less popular - more. These bonuses can then be used as a discount for future purchases. Thus, we increase the chances of selling less popular products and increase customer loyalty.
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