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Data Analysis Pitfall: What are those, and How to Avoid Them

Digital marketing, without no doubt, is a data-driven field in the web. The Search Engine Optimization (SEO) has to work with an incomplete and questionable data, with these situations, users end up jumping in the wrong conclusion in their attempt to authenticate the arguments or measure the issues and opportunities.

I have enumerated a few of the drawbacks of the data analysis. Drawbacks of data analysis are common in the web industry, however, there are measures to avoid these.

The first drawback is when users jump into conclusion.

With a study conducted about the rank factor regarding brand awareness, there are three conditions as to why a domain authority has a positive correlation with the ranking. The web might have a link that can cause it to rank well; the sites get more links because of their rank; and nonetheless, there is a third factor that can make the web acquire both rank and links.

The reason behind this is, users tend to fail in critically analyzing the potential mechanisms. There are various possibilities to look into before jumping to conclusions, these possibilities can be linearity; joint causation; broad applicability; reverse causation; and complete coincidence.

The second drawback is when users miss the context.

Traffic drop analyses might transpose the statistics of the web, to make sure the results, you have to compare it to the expectations.

The third drawback is when you trust the tools more than your instinct.

You cannot solely depend on your business decision on the analytic tools, competitors can easily manipulate the numbers. There are some major challenges most analytic tools have, how analytical tools are easy to manipulate; how vulnerable analytical tools to the ad blockers; how arbitrarily group hits into sessions; and how analytical tools perform in the sampling observation.

With this situation, you have to understand the strengths and weaknesses of the analytical tools that we utilize, to know when these tools are accurate or not. The answer to this problem is seen to be combining multiple data sources.

However, combining multiple data sources can be a drawback of the data analysis.

There are software platforms that can defeat the purpose by combining together the data of two or more of these platforms, such as analytics, search console, AdWords, and rank tracking.

The problem with these platforms are, they do not have the same definitions and it tends to break the connection between the data. Thus, these tools have to be carefully understood. To have a reliable source, you have to spend on paid search to acquire a decent volume, conversion rate, and bounce rate estimates.

Use a visibility metric, to not lose on the web rankings, and to have more branded keywords or long-tail rankings.

To sum up, the steps to avoid the pitfalls of data analysis, you have to critically analyze the correlations and case studies; to not overlook on the context when the web traffic changes; and to remember that these analytical tools have limitation and the data are directional than precise.

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