Case Study | Ulmart Russian E-tailer Gains Winning Insights from OWOX and BigQuery At a Glance • Challenge: Ulmart, Russia’s leading online retailer, needed an advanced data analysis tool to gain deeper insights into customer purchasing behavior, improve decisionmaking, and save money by using open source technology. • Solution: Ulmart chose Google BigQuery for data processing and OWOX’s BI BigQuery Reports Add-on to take advantage of the Google Sheets interface and Google Cloud Platform security. • Results: Ulmart can now create actionable dashboards, analyze its pricing against the market, and maximize its margin without losing market leadership.

About Ulmart Ulmart, the Russian leader in online retail sales, has 32 cyber markets and more than 280 pick-up points in over 190 cities across the country. Ulmart achieved its success by using innovative technologies and modern software and by providing quality service.

About OWOX OWOX specializes in implementing Google Analytics Premium for e-commerce businesses and developing unique online services based on Google BigQuery. More than one million transactions are generated weekly on OWOX clients’ sites. OWOX services for data collection, processing and management work 100% in Google Cloud Platform and are trusted by more than 1000 businesses.

BigQuery Analyze Big Data in the cloud with BigQuery. Run fast, SQL-like queries against multi-terabyte data sets in seconds. Scalable and easy to use, BigQuery gives you real-time insights about your data.

Background With e-commerce sales worldwide approaching $1.7 trillion and expected to account for nearly 10% of all retail sales within five years, online retailers face increasing pressure for faster, higher quality service from customers in a 24/7 marketplace. Ulmart, Russia’s top e-commerce company, had achieved its leadership position through a combination of widespread pick-up locations, rapid-fire delivery and ever-expanding product offerings. Maintaining and strengthening its number one status became the company’s central goal.

Challenge To remain dominant in the highly competitive e-commerce marketplace, Ulmart would need to dive deep into the purchasing behavior of its customers. How did customers interact with the various product groupings on its website? To what extent did Ulmart’s pricing versus its competitors impact buying decisions? Answering these questions would require a real-time tool able to calculate and analyze massive amounts of data and then create actionable dashboards and metrics-based reports.

Solution While searching for the right combination of technologies that would provide a solution, Ulmart discovered OWOX, a Google Cloud Platform partner that specializes in e-commerce businesses and online queries. Based on OWOX recommendations, Ulmart chose Google BigQuery to perform the advanced analysis it needed. BigQuery, a cloud database with low latency and powerful computing capabilities, is able to support real-time data collection and processing without allocating additional time and human resources (system administrators and developers) for system setup, customization and maintenance. Ulmart’s online data flow now follows this structure:

Based on data sets from BigQuery, Ulmart’s analysts have the opportunity to automate and visualize reports using OWOX BI BigQuery Reports Add-on for Google Sheets. Analyst queries can be shared with all colleagues who have access to this project in Google Cloud Platform. And the OWOX Add-on has dynamic parameters that can be customized and modified in the interface, so even non-technical marketing and management staff can create reports using BigQuery. To analyze competitors’ prices, OWOX developed a system that uses a single SQL query to analyze the correlation between Ulmart’s prices and average prices on the market. The OWOX Add-on then exports these query results into Google Sheets for further visualization. Ulmart analysts can now present complex data in a simple, easy to understand fashion to the company’s decision makers.

Results After testing assumptions about online product allocation through a rigorous analysis of the data, Ulmart’s merchandising team has been able to adjust that distribution and thus increase revenue from customers interacting directly with product display blocks on its site. Using its newfound ability to run ad-hoc analysis, Ulmart can now create reports with a high level of granulation and get results fast. The company can create system-generated alerts for staff who control its price management structure, resulting in greater margins for many product SKUs. Familiar syntax and super-fast queries in Google BigQuery allow Ulmart to process massive amounts of data and make real-time decisions. And because most users can create reports based on preset queries with dynamic parameters, Ulmart saved thousands of dollars on employee training and purchasing of expensive software licenses.