M.video Success Story: Measuring the ROPO effect

About M.video

Founded in 1993, M.video is the leading consumer electronics and home appliances retail chain in Russia, and also one of the largest retailers of consumer electronics in Europe. As of 2017, M.video had more than 420 stores in 169 cities across Russia, reaching an annual turnover of more than 200 billion rubles.

The company is the first in Russia to adopt an omnichannel retail strategy. M.video offers more than 20,000 items, available on the company’s website and in the retail chain.

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M.video’s customers typically purchase products in three unique ways:

  • Ordering solely online at mvideo.ru. The revenue from the website accounts for about 20% of the company's total turnover
  • Purchasing offline in the company’s chain stores.
  • Finding products on the website and buying them in offline stores. This behavior is known as ROPO Effect — research online, purchase offline.

M.video’s marketers were looking to evaluate the impact online channels have on offline sales. This would allow them to more accurately calculate the return on advertising investments and build a better marketing strategy based on the complete data. Another task was to improve the online experience of the customers, by discovering the reasons why they choose to buy in offline stores after visiting the website. To achieve these two goals, M.video’s marketers decided to integrate the data about online and offline touchpoints of the signed-in users (about 12% of the total number of website visitors).


M.video collects, stores and processes all the data in different systems:

  • The data about user interactions with the website is collected in Google Analytics 360.
  • The data about offline purchases and order returns is collected in the company’s CRM system (SAP). The structure and the collection algorithm of this data is completely different from Google Analytics.

To analyze the impact of online channels on the company’s total revenue, the company’s marketers needed to have all the data combined in a single system. Google Analytics is not suited for the task, as it doesn’t support data reprocessing: once processed, the data cannot be modified if an order is canceled or returned for any reason. Moreover, importing all the data about offline transactions of users who have never visited the website, would significantly distort the accuracy of the Google Analytics statistics. Google Analytics may also fail to track some of the purchase data on website pages, because the JavaScript wasn’t loaded in the browser.


In order to achieve their goals, M.video’s marketers decided to take the following steps:

  1. Bring together the data about user interactions with the website, offline purchases, and order completion rates.
  2. Combine the data about offline purchases with the data about online sessions.
  3. Visualize the data for in-depth analysis.

The flowchart of this entire process is given below:

data collection and integration flowchart to measure the ROPO effect

Step 1. Bring all the data together in a single system

M.video sends data about all user actions and orders placed on the website to the Google BigQuery cloud data warehouse, using native integration available to Google Analytics 360 accounts. Therefore, the company’s specialists decided to use Google BigQuery to collect all other information.


We chose to use GBQ because this platform is best suited for our tasks. For website analysis, we use Google Analytics that integrates seamlessly with GBQ. Altogether this allows us to fully understand our business processes, with great level of detail.

Aleksandr Tychinsky,
Web Analytics Manager at M.video
Aleksandr Tychinsky

To transfer the data about offline purchases and order completions from the CRM to Google BigQuery, M.Video specialists set up automatic daily data uploads via FTP.

Step 2. Process the data

OWOX BI analysts helped M.video combine and process the collected data. First, the data about online orders was supplemented with statuses of each order, using an SQL query. The query merges the data based on matching values from two tables, with the ID of the transaction (Order ID) used as the key.

Merging the data by the Transaction ID

Next, the analysts combined the data about offline purchases and website behavior of the same customers. For this purpose, they used the User ID in Google Analytics. A User ID is a unique identifier assigned to each user who has signed in on the company’s website. M.video associates User IDs with the customer loyalty cards in the CRM system, and sends the data as custom dimension values to Google Analytics. The time period for data integration was set to 180 days, while considering the time period from the website visit to the purchase. In this way, more granular audience segmentation was made possible.

As a result, M.Video received the following data about each of the orders (both online and offline):


It was a very interesting task to work on, as the major part of the job was performed in BigQuery. We combined the data from several different sources, under a single structure. It’s a particular pleasure that despite the research intensive nature of the task, the client can immediately use the results in their course of work.

Maryna Prihodko,
Digital Analyst at OWOX BI
Maryna Prihodko

Step 3. Visualize the data as dashboards and reports

OWOX BI helped M.video visualize the information in Google Data Studio by creating an informative dashboard. The company can export the data from the dashboard for a more detailed analysis and budget planning.

For example, the interactive bar chart, the screenshot of which is given below, shows the number of online, offline, and ROPO purchases along with the revenue obtained from them. This data can be filtered by city, time period, and product type. ROPO purchases constitute about 10% of the total revenue, depending on the city. The chart also demonstrates that the percentage of orders from each channel does not coincide with the percentage of revenue obtained from the channel — it depends on the average order value. In this case, online purchases have a higher average order value than offline purchases.

the share of orders and revenue generated from customers who research products online before buying offline

The table below demonstrates the additional revenue from ROPO purchases across different regions, channels, and product categories. The data can be exported in a tabular format and is used by the company in distributing the advertising budget.

associated ROPO-revenue


  • M.video obtained an informative automated dashboard, allowing them to take into account the ROPO effect in the operational planning of advertising campaigns.
  • The company found out that online channels contributed to about 10% of offline revenue.
  • By analyzing the behavior of users who research products on mvideo.ru before buying them in the company’s chain stores, M.video is now able to discover the reasons why these customers choose to shop offline. The company can now revamp the website for a better user experience and higher conversion rates. As an example, the company discovered that most offline customers who had visited the website used discount coupons when purchasing offline. Armed with this information, M.video has already improved the customer experience with discount coupons on the website. In addition, the company simplified the online credit application form so that customers don’t have to go to a brick-and-mortar store to buy on credit.

By implementing these solutions, we were able to better understand the impact of online channels on offline sales, pinpointing the specificities of the ROPO behavior of our customers. This allowed us to more effectively interact with these customers and more accurately evaluate our marketing campaigns. details that were previously hidden from us.

We’ve been working with our colleagues at OWOX BI for some time now, they’ve been a big factor in a number of our success stories. Through this collaboration, we have attained an excellent information environment, allowing us to make decisions on the development, refinement, and promotion of our website, mvideo.ru. By using BI Pipeline, BI Attribution and BI Smart Data, we supplemented our internal data with information from other systems and were able to see the details that were previously hidden from us.

Aleksandr Tychinsky,
Web Analytics Manager at M.video
Aleksandr Tychinsky

P.S. Do you have any questions regarding this success story? Feel free to ask them in the comments below — we’re always happy to answer! And don’t hesitate to try OWOX BI if you’re willing to boost your marketing performance.