Are people around you constantly extolling the benefits of metrics and analytics?

Web analytics, digital analytics, social media analytics, the list goes on. Yes, we get it. Metrics and ROI are important.

But, are you feeling overwhelmed? Are you unsure about where to start, or which metrics to analyze in relation to your specific business context?

The problem lies in the misconception that analytics begins with metrics. In reality, analytics should start with a systemic or holistic examination of, first, the organizational situation (problem) that precipitated the need for analytics, and, second, your own subjective role within the analytics process.

To learn about how to do so, especially in the context of social media, please read on.

Analytics Are Subjective, Not Objective

In typical analytics—let's say social media analytics—we make an assumption that the data and social media groups are "out there," waiting to be analyzed.

That is not entirely accurate. Social network data, like all social data, is not consistent, coherent, or predefined. Such data is selectively harvested by analysts and marketers like you, according to your subjective judgments and preconceived notions.

You make many subjective judgments when analyzing a social media group that is important to your business.

First, you must decide on which relationships in the group to focus on based on the context and goals of your inquiry. For example, in a Twitter group, you could pick between the follower relationship, the retweet relation, the "favorited tweet" relation, or the mention relation.

You must also pick the aspects of the social group structure to focus the analysis on (e.g., network density).

Also, you might be interested in identifying top influencers in certain social media groups to engage in influencer marketing. "To win the content marketing game, brands need to focus more on optimized, personalized, and influencer activated content experiences," the CEO of TopRank Marketing is quoted as saying in HubSpot's Not Another State of Marketing Report.

Before identifying top influencers, you as an analyst must decide on what your definition of a "top influencer" is. Is it someone who gets mentioned a lot by others in the group, or is it someone who shares lots of content? Is it someone who is connected to lots of other influencers, or is it a user who connects members of the group who are otherwise disconnected from the membership at large?

Those sorts of judgments in analytics are made based on your interpretation of the goals and context of the particular inquiry, your understanding of the social media group under analysis, and your past experiences with similar projects.

You will view the social media group being analyzed from within your organizational context and subjective frame of reference. Your subjective perceptions will be influenced by your prior experiences, biases, and assumptions. Your subjective frame of reference will influence the initial assumptions regarding the group, the selection of the relationship to structure the analysis around, the choice of which aspect of the social network structure to focus on, the choice of metrics to be used to measure influence, and the significance of the results in the context of the social media group and the organization that you belong to.

Accordingly, analytics is not objective. It is subjective.

So, how important is it for you to learn more about your subjective perceptions on both the social media group being analyzed and the business context?

It is vital, because it can help you gain a more systemic or holistic understanding of the situation under analysis before you decide which metrics to track. It can also help you to explain the business value of tracking particular metrics to your managers.

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Analyzing the Analyst: A Guide to Holistic Analytics for Tracking the Right Metrics

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ABOUT THE AUTHOR

image of Dr. Shavindrie Cooray

Shavindrie Cooray is an is an associate professor in management information systems at Curry College, MA. Dr. Cooray has presented at international conferences and published her research in the areas of user-driven systems development, participatory systems design, data mining in social networks, and virtual teams.