In marketing, nothing works without data, at least not if success is only to be recognized in sales. Instead, we all deal with website visitors, click behaviors, hold times, conversions and many other metrics to help us understand why we are successful or not. This data is then further optimized so more success is visible. But what if the data is wrong or falsified?
Those who uses tools like Google Analytics, can learn a lot about their visitors. As a standard, a number of important key figures are already issued here and those who continue to deal with the tool can also set up specific evaluations which are individually adapted to their own requirements. But all the great numbers are of no use if they are not looked at properly.
Again and again there are false figures which can be quickly recognized if only the overall picture is considered. The classic is here, that the analysis tool includes the own visits on the website. If you are often on the page, the conversion rate may be corrupted. Popups can also produce incorrect data. For them, usually the X for closing is too small and if the user wants to click on it, he misses it more often than some directing the user to the ad which arises. This is reflected in the dwell time and bounce rate.
Thus, many other possibilities can be enumerated, how data can be misinterpreted or really wrong numbers arise. Further examples:
- not every traffic source is considered differentiated
- comparisons are drawn between different periods which cannot be compared (eg summer and winter)
- different channels are compared (eg social media with email marketing)
- and much more
In the next analysis, it is therefore worth looking again carefully, which numbers are used and where they came from. Often, this shows that some things were not correct, and after the correction different numbers emerges and as a result also approaches for optimization.