Data analysis plays an extremely important role in the life of a digital marketer. You’d be surprised to know that every second digital marketer has manipulated data analysis at some point in their career just for convenience.
Should you worry?
Data manipulation at this level is simply a different type of data reading tactics. If you understand it you’ll get better insights, if you don’t understand then you’d settle with obvious results.
Here’s citing an example;
You could say that your enquiry page shows a bounce rate of about 60%. This shows that 60% of visitors reaching your enquiry page just leave the page & do not perform any further action. You could perceive this as a negative behaviour and blame the quality of your page that could not engage the users within the website environment.
A very less common observation would be that maximum users are bouncing off your enquiry page because they got the information they were looking for first-hand.
On the contrary, if the bounce rate of a webpage where you’re hosting an interesting story is high then you should worry. It is a clear sign that the story is not interesting enough to keep the user engaged to the page.
Any type of data must not be analyzed as per its type; for example don’t look at overall page views, or overall bounce rate. You should always check how relevant a page view or a bounce rate is for a certain category of pages. A downward trend for one category could mean different things to different pages.
Sometimes certain data trends are a bit confusing and your analysis comes out of an assumption.
This happens when there is a correlation seen in two data points. Let’s say, you see an increase in subscription to newsletters of your health website. Simultaneously, there is an increase in visitors on the blog section about yoga.
You could say that the subscription has increased due to the content of your yoga blog. More users on the blog and therefore more subscriptions.
It could also mean, that the newsletter subscriptions are increasing due to an external campaign and that is leading to increase in traffic on your health blog.
Correlation in two types of data trends need to be studied carefully as there could be an external factor like a campaign or referral activity affecting it. This practice is imperative to avoid any one-sided decision.
Data points, especially the ones that are taken over longer duration are not always accurate. If you try to break it down into months, weeks & then compare you will always see a disparity. You know, Google Analytics starts sampling the data after a certain high range.
Therefore, companies that require absolutely accurate data always go for Google Analytics Premium or Adobe Analytics. There are equally good tools in the market and basis your needs you can decide.
For the ones who have to make do with the free version, they should always collect data regularly and keep building on their analysis.
Collate data over smaller time intervals & then compare your results rather than looking at data over a large number of years. In such large duration data is bound to get sampled.
Having said this, companies are still into the habit of collecting and analyzing data every week & every month. That’s a very traditional way of data analysis. Move on to strategically mapping your data with objectives so that you get relevant insights which can be acted on.