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7Common Analysis Mistakes You Need to Avoid- Second Part

In the 1st part of this article, we discussed a group of common mistakes concerning data and figures analysis. Avoiding these errors enhances your experience in dealing with data and contributes to the success of your analysis optimization. In this article's 2nd part we'll resume with those common mistakes:

Doing all the analysis operations manually

In general, social networks and digital marketing analysis tools are one of the fastest-growing markets in the recent period, and this is clear evidence of the importance of these tools and their role in facilitating the task of obtaining concrete data from various marketing activities.

Not using these tools, will lead you to spend more money, time, and effort to get primitive data that will not even help you taking savvy decisions to boost your business.

Ignoring the difference between traffic types

Traffic or visits coming to your website are varied and different. Traffic sources can be organic sources, such as referrals, email, social media, or search engines. They can be paid, in case you are running some advertising campaigns whether on social networks, Google, or any other sources. All these traffic channels are not with the same value.

If you have 1000 visits to your site during a given month, you have to divide that number into smaller parts according to different marketing channels and ask yourself the following:

What percentage of visits is coming through search engines? What about the e-mail campaigns you are running? What are the growth rate and increase of these figures during the months? And so on.

Knowing these numbers will help in understanding where you should invest your money, time, and effort to get the best out of each channel.

Wrong Comparison Criteria

In the world of analysis and statistics, you can't compare an orange with an apple, instead, you should compare it with another orange.

When it comes to digital marketing, the most difficult part is to know the difference between data types to make a good and healthy comparison that drives decisions.

For example: if a page X on your site has more traffic than another page Y on the same website, this doesn’t mean that page X was performing better than page Y.

There are tons of factors you should take into consideration to actually have a judgment whether this page is contributing to your goals from online presence/marketing?
For example, suppose you have 200 visits to page Z on your site through email marketing and 150 other visits to the same page through mobile phones. Does this necessarily mean that you should increase investment in marketing e-mail marketing rather than mobile phones?

Well not necessarily.

Maybe you allocated less budget to target mobile phones. Maybe the page is not rendering correctly on mobile devices. Maybe the email got extra shares within some other client's organization, etc.

Believe me; you should dig deeper into your data to have good future decisions.