Rozetka increases average order value by 9% and direct marketing revenue by 18%
Rozetka constantly implements new functionality to increase sales volumes. As the market leader, the company’s customer database offers huge potential for monetization through repeat sales. Meanwhile, thanks to the site’s variety of products and significant visitor numbers, they have a large database that can serve as a source for recommendations based on user behaviour and transactions.
With the goals of increasing revenue per user and average order value, Rozetka needed help with product bundling, merchandising, product recommendations and email campaigns. Supported by OWOX analytics specialists, the company implemented a product recommendation system based on data from Google Analytics Related Products functionality. This data could then be used for direct marketing in user emails.
The first step was to implement a system in Google Analytics to gather structured data about user interactions with products from all touchpoints, including
- The desktop site (via Google Tag Manager for web).
- The mobile-optimised site (via Google Tag Manager for web).
- Apps (via Google Tag Manager mobile apps SDK).
- The call centre (via operator extension and Google Tag Manager for web).
The second step was to export the product relations data from Google Analytics using Core v3 Reporting API, then import it into BigQuery. This increased the quality of recommendations data by:
- Verifying the product availability status.
- Excluding goods from incompatible categories.
- Excluding goods that users had already purchased.
The final step was to create direct marketing lists with improved email recommendations.
Direct marketing revenue increased by 18%.
Average order value increased by nearly 9%.