Rozetka increases direct marketing revenue by 18% using Related Products in Google Analytics
Customer Company Size
Large Corporate
Region
- Europe
Country
- Ukraine
Product
- Google Analytics Premium
- Google Tag Manager
- Google BigQuery
Tech Stack
- Google Analytics
- Google Tag Manager
- Google BigQuery
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Big Data Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- E-Commerce
- Retail
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Supply Chain Visibility
- Inventory Management
Services
- Data Science Services
- System Integration
About The Customer
Rozetka is the leading online retailer in Ukraine and the most visited online store in the Commonwealth of Independent States. The company offers a wide range of products, including appliances, electronics, home goods, clothing, shoes, jewelry, and even flight and railway tickets. Rozetka is constantly implementing new functionalities to increase sales volumes. As a market leader, Rozetka's customer database offers a huge potential for monetization through repeat sales. The company's website also attracts a significant number of visitors, providing a large amount of data that can be used for product recommendations.
The Challenge
Rozetka, Ukraine’s leading online retailer, was looking to increase revenue per user and average order value. The company had a large customer database and a wide variety of products, which provided a significant amount of data that could be used for product recommendations based on users' behavior and transactions. However, Rozetka needed help with product bundling, merchandising, product recommendations, and email campaigns. The company aimed to monetize its customer database through repeat sales and improve its direct marketing efforts.
The Solution
Rozetka, with the help of analytics specialists OWOX, implemented a product recommendation system based on data from Google Analytics' Related Products functionality. The first step was to gather structured data about users' interactions with products from all touchpoints, including the desktop site, mobile-optimized site, apps, and the call center. This was done using Google Tag Manager. The second step involved exporting product relations data from Google Analytics using Core v3 Reporting API and importing it to BigQuery. This process helped to verify product availability status, exclude goods from incompatible categories, and exclude goods that users had purchased earlier, thereby increasing the quality of recommendation data. The final step was to create direct marketing lists with improved email recommendations enabled by the integration.
Operational Impact
Quantitative Benefit
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