The purpose of Qmon is allowing you to make better data driven decisions. In many cases the insights Qmon delivers are used to take action on improving your online business. But they can also prevent you from carrying out investments that would later be proved unnecessary. Here’s a case in which Qmon saved a client a massive amount of time and labour.
As the Gartner study Measuring the Business Value of Data Quality stated in 2011, Data quality effects overall labour productivity by as much as 20% and poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits. The client in this case is a global fashion brand. Although the maturity of their online business could be considered medium, their data quality was at 93%. They did however have an issue in their web analytics data. At some occasions, when someone logged in (clicking only once) the analytics data showed four page views. Since the issue only occurred sometimes it was not found, neither by scenario testing nor crawlers. When the clients themselves logged in to check, the tagging seemed to be fine.
Extensive optimisation project
Determined to solve this issue, the client was about to initiate a substantial optimisation project, devoting quite a lot of time to developing a new log-in procedure that cured the website from this error – hopefully.
Finding the issue with Qmon
Just before this project started, the client heard about Qmon. Because of Qmon’s ability to monitor 100% of the website’s visits and to deliver specific information about tagging issues, they decided to give the tool a go. After all, there was a significant chance that Qmon was able to show what was going wrong.
And so it did. It turned out that in certain scenarios, visitors had to log-in four times to continue to the next page, explaining the four page views that showed up in the analytics data. This issue was then solved by the IT department, and the extensive project for improvement of the log-in was cancelled.