Ad-hoc analysis allows Ulmart to increase average revenue on customers interacting with product blocks


Objective is a highly visited e-Commerce website with several different types of catalogs and merchandising blocks, where users can find a wide range of products and services. With the advent of new metrics implementation and more detailed analysis of user behaviour, the business wanted to perform the following tasks:

  1. Understand customer behaviour by analyzing their interactions with product merchandising blocks.
  2. Calculate how the disparity between their price and the price of their competitors’ influences their conversion rates and revenue.

Ulmart needed a real-time tool to analyse massive amounts of data to accomplish their analytical tasks. In addition, this tool visual reports based on processed data.


According to the recommendations from OWOX, Ulmart chose Google BigQuery for advanced analysis. It is a cloud database with low latency and powerful computing capabilities that supports real-time data collection and processing without allocating additional time and human resources (system administrators and developers) for the system setup, customization and maintenance.

Ulmart’s online data flow now has the following structure:

Ulmart’s analysts can now create automatic reports, including visual reports, using BigQuery Reports add-on for Google Sheets based on their datasets from Google BigQuery.

They create queries that are available to all their colleagues who have access to Ulmart’s project in Google Cloud Platform. Some of the queries require help from technical staff. In these cases, they use the add-on’s feature of including dynamic parameters that can be modified in the interface without editing the query.

The reports that are created provide insight into how to optimize merchandising blocks and product allocation that Ulmart can further implement on their website.

To analyze their competitors’ prices they query data from the website along with data from the price monitoring system. The relationship between the website’s conversion rate and disparity between Ulmart’s price and the market average price is calculated using a single SQL query. BigQuery Reports Add-on exports the query results into Google Sheets for further visualisation, in a simple way for decision making.


  1. Average revenue from customers increased.
  2. Increased margin on a several of product SKU's without decreasing the number of transactions.

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    Main goal was to build an analytical system which would be able to automate collection and calculation of key performance indicators. We achieved the following results: — automatic calculation of ROI for paid advertising campaigns on different levels — calculation and analysis of additional KPIs through GA API — possibility to create non-standard reports and flexibility in setting up data collection from the website with GTM So, we obtained more opportunities for business process management