Google Case Studies Rozetka increases direct marketing revenue by 18% using Related Products in Google Analytics

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	Rozetka increases direct marketing revenue by 18% using Related Products in Google Analytics Google

Rozetka increases direct marketing revenue by 18% using Related Products in Google Analytics

Google

Technology Category	Analytics & Modeling - Big Data Analytics Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries	E-Commerce Retail
Applicable Functions	Business Operation Sales & Marketing
Use Cases	Inventory Management Supply Chain Visibility
Services	Data Science Services System Integration
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

E-Commerce

Retail

Applicable Functions

Business Operation

Sales & Marketing

Use Cases

Inventory Management

Supply Chain Visibility

Services

Data Science Services

System Integration

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.

About 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.

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.

Impact #1	Rozetka was able to provide relevant product recommendations to consumers.
Impact #2	The company improved data quality by integrating information from its existing Enterprise Resource Planning (ERP) system.
Impact #3	Rozetka increased its average order value and revenue per customer from every email.

Impact #1

Rozetka was able to provide relevant product recommendations to consumers.

Impact #2

The company improved data quality by integrating information from its existing Enterprise Resource Planning (ERP) system.

Impact #3

Rozetka increased its average order value and revenue per customer from every email.

Benefit #1	Direct marketing revenue increased by 18%.
Benefit #2	Average order value increased by nearly 9%.

Benefit #1

Direct marketing revenue increased by 18%.

Benefit #2

Average order value increased by nearly 9%.

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Overview

Rozetka increases direct marketing revenue by 18% using Related Products in Google Analytics

Operational Impact

Quantitative Benefit