Data Analytics for Online Shops

Making shopping a smooth and easy experience is the goal of many online shops. But how can easy website navigation be optimized? Unlike the physical shops, you cannot talk to the sales people in the online shops and a customer should find his or her way through the online shop. And unlike pysical shops, online shops can have hundreds of thousands of visitors every day.

To optimize the shopping experience, some important questions should be answered :

• Why do customers put products into their shopping cart and do not buy them at the end?
• Was it because of the sales process or he/she liked another product simply better?
• What impact has a customer's drop from a detail page?
• Where are the differences in customer behavior depending on the different product groups,
• Customer types or assortments?
• What are the figures compared to the previous year?
• Were there any technical errors? Was the accessibility of all pages always guaranteed?

Traditionally, answering these questions require a lot of work and usually takes a few days a month to prepare answers and presentations for the management. Since the process is slow and requires many man-days, most companies cannot perform it anytime they want or in the frequency they need.

German business consultancy company Mayato automatize this process using data preparation, analytics and visualization tools.

The company extracts data from the operational database to in-memory, ultra-fast analytics database named EXASOL. Traditionally, large volumes of data need to be extracted from the database, loaded into R statistical language based scripts and run; and then loaded back to database for analysis and visualization.

Markus Dill, Managing Director at Mayato, explains the importance of good analytics for companies in the manufacturing sector and why having an analytic database that is low-maintenance such as EXASOL is critical for business success.

But EXASOL has built-in R capabilities so R scripts can be run on EXASOLs ultra-fast and high performance analytics environment. The company uses Talend ETL tool for automated data extraction, transformation and load. The data extraction, crunching (using R) and data visualization (in Tableau software) are all automated without human intervention.

The combination of Talend, EXASOL and Tableau fully automated the process and reduced its report generation cycle from days to hours.