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How we created a web analytics tool for an e-commerce website

Service: web analytics.

Niche: Ecommerce.

Result: A handy tool for web analytics developed within 27 hours.

The Challenge

In November 2014 we were contacted by a company that wanted to have its website analyzed in order to get answers to non-trivial questions in the form of reports that could not be compiled on Google Analytics. At the same time, there was no complete understanding of what kind of reports would answer the questions posed by our client.

Together we compiled a list of parameters and metrics that would be necessary to obtain these answers. We also developed a system of automatically updated non-trivial reports. This file would on a daily basis update all reports as soon as they were opened, since the data visualization was connected to the daily updated MySQL database duplicated from Google Analytics through API.

веб аналитика

The scheme of data collection, aggregation and visualization

  1. Google Analytics collects information about all sessions on the website.
  2. Through API the information collected on Google Analytics is aggregated and exported to the MySQL database.
  3. An Excel file is then connected to the MySQL database and every time it is opened it sends a request to receive the updated data while at the same time updating all tables and diagrams.

The Solution

  1. We established a MySQL database containing all information that our client wanted to analyze.
  2. We wrote a script to daily update the database with data from the previous day.
  3. We divided traffic into the categories of activities, involvement and type of the visitor.

The category of involvement:

  • Non-involved (sessions that would last no longer than 60 seconds);
  • Interested (sessions that would last from 60 to 180 seconds);
  • Involved (sessions that would last longer than 180 seconds).

The category of activities:

  • New visitors (the number of days since the last visit — 0);
  • Visitors active during the last two weeks (the number of days since the last visit — 1-30);
  • Visitors that have come back to the website after two weeks (the number of days since the last visit — over 30).

The category of the type of the visitor:

  • Signed-in visitors (those who have signed but who have not visited their personal account (PA));
  • Users of personal account (those who have visited their PA);
  • Other visitors (those who have not visited their PA).

The category of the website version:

  • PC;
  • mobile.

The category of the language preferences:

  • RU (Russian);
  • KZ (Kazakh).

The category of the type of device:

  • Desktop;
  • Mobile;
  • Tablet.
  1. We set up data visualization with a possibility to use different filters in the Excel file that was connected to the MySQL database. All reports in this file were automatically updated every 24 hours.

It took us 24 hours to work towards this BI solution. We set up the process of collecting data from from Google analytics directly into the MySQL database.

The Result

Our solution enabled the client to:

  1. Check the volume of traffic and behavioral parameters by day.
  2. Present the segmentation of visitors in the form of a report.
  3. Present the segmentation of visitors in the form of a diagram.
  4. Monitor the changes in behavioral parameters.
  5. See the value of new content.
  6. Compare the indicators of a particular website page.

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