Social Media Marketing

How to Correctly Analyze the Data in Social Networks?

Since the advent of social networks, the information flow generated by the interaction between users and these systems has been established as a rich source of data that many factors; from advertising and marketing specialists to social scientists they have wanted to use to their advantage.

Analyze Data in Social Networks

Analytical tools such as the popular Google Analytics or other more advanced programs for the likes of Lucky Orange or Webtrends, part of the software which is often used to extract this data and process it to produce relevant and useful information. Now this is not as simple as it seems.

Databases with terabytes of complex systems weight and segmentation of information, who wants to get out of the analysis of information poured into social networks (even micro levels) should not only be functional working tools but also some method.

Then, we extracted some assumptions of the analysis of data obtained in social networks for this operation to be productive and the risk of obtaining biased or incorrect information is not run.

1. You have to know what you are looking for

Although it is a bit redundant, we must be clear what information is sought to focus the analysis strategy in collecting such information. For example, if what is sought is to measure the number of users responding to a publication made ​​on Facebook very early in the morning, simply use the built Facebook to make that measurement tool. However, if something specific you want like to know which places belong users viewing the information, how they click or then seek more information in the same fan page, another will require focus and other tools.
This not only serves to discriminate well what you want to know, but also to eliminate irrelevant information that could affect the quality of sampling. In the same example, the number of visits to a publication becomes irrelevant if we want to know is the residence time within the same individual user.

2. Keep in mind the bias of the social network

All social networks have bias, it is something that must be taken into account. Thus, users of networks such as Instagram or Pinterest , respond to certain population segment, with more or less certain specific tastes and inclinations. So, if more comprehensive information Facebook is desired (and in some cases Twitter) provide more accurate statistics interaction.

If, however, a shoe store (a brand that usually has a greater presence in more visual networks like Instagram) want to poll if your female audience responds well to a new design, the most effective place will not Instagram and Facebook, because in the first will be your target audience.

3. You have to carefully choose the type and quality of the measurement

A quantitative or qualitative measure is wanted? What you want to focus the attention: the visits in the places of origin of users or their interactions within a web? This is pure statistics, but it has to be defined to get good data analysis to be performed.
In some cases, companies decide to hire costly market research rather than using the tools in Internet that allow the same measurements and quite acceptable level of certainty.

4. We must take measurements with different tools

This is necessary to have several sources, resulting in having more accurate information. This method is used by almost every science to prove certain assumptions and is also the best for use in the subject of data analysis.

Moreover, when we are talking about big data , there is no other way to reach a conclusive and satisfactory outcome using different analytical tools. Skipping this step can often be catastrophic when the results of the use of information that has not been confirmed are.

5. You have to compare the data with reality and rethink if necessary

Do not suffer from gullibility. Even after following these simple steps and have processed the information until you have become useful and employable, it should be continuously monitored to user feedback. And that is when the ‘human factor’ is present, all numbers may fail.

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Note that for some time, many scientists have criticized the decisions made based on the data in social networks; especially because even today the analysis process fails to properly discriminate the group which researches, the aforementioned human factor and the high level of Internet fraud (false accounts, bots, etc.)

What’s the verdict? The analysis of the data whose source social networking is useful and works when it is used well. So for now we will continue to see much of this in the coming years.