In marketing, strategy, and tech, we hear a lot of jargon around ‘measurement’ and the ‘software landscape’. ‘Insights’ have become ubiquitous, with many organizations and individuals using this term in many different ways.
In previous publications, we’ve described how insights come in many different forms. What are customers saying about your products and company? How about your competition? Who is your target market, what do they care about, and how do you reach them effectively? What trends are affecting your industry and how should you respond? These are relevant questions to the decision-making of businesses, and they can be answered through social media analytics. First, we will go over the difference between quantitative and qualitative analysis to better understand how you can derive insights from social data.
Quantitative Analysis: The What
With qualitative analysis, we use hard numbers to draw conclusions and measure success. Examples of these include the number of fans/followers, brand mentions, share of voice with competitors, social shares, and more.
Similar to the infamous McNamara Fallacy, this analysis supports the theory that “If you can’t measure it, it doesn’t exist” because it can’t be proven. Therefore, in quantitative analysis, the only data taken into account for strategic decision-making is the data you can count.
This type of analysis is efficient and easy to gather and analyze because of its simplicity and concrete nature. Since it doesn’t deal with complex data, it is more easily comprehended, and therefore, more ‘credible’ than insights gained from qualitative research, as it provides numerical proof.
Consequently, quantitative analysis disregards data that isn’t in the form of easily digestible numbers; therefore, if you can only understand a fraction of the available information, it isn’t comprehensive or insightful enough. In social media, if your Facebook campaign post has 1,000 likes, you get to know its “weight”. However, the 1000 likes don’t necessarily mean that the campaign was successful. From the perspective of social listening, it is important to understand the context and nuance; it is critical to understand the underlying ‘why’ of the 1,000 likes to see if it’s something that the company wants to do again in the future.
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Qualitative Analysis: The Why
Qualitative analysis is less related to hard numbers, and more to the meaning behind those numbers. So why did your company get 1,000 likes? Did they like the product? Or was it the message? What about the athlete that was sponsored? These intangibles provide knowledge and insights that can help drive more effective and strategic solutions.
In a presentation about The Power of Social Data with Schireson consultancy, Jazz at Lincoln Center (JALC) was able to gain valuable insights about their audience. In the following visual, we see how JALC was able to understand the main interests of their audience and compare this to those of their competitors. Knowing this information about their audience allowed JALC to carry on more informed campaigns and promote customer intimacy.
For the most comprehensive solution, a combination of both volumetrics and insights is ideal.
Although it is harder to work with qualitative data, it is necessary to make sense of it since it provides deeper insights, as well as nuance and perspective that would be impossible to get from mere volumetrics. The results can give an understanding of the story behind the results of the analysis of quantitative data. For example, with qualitative data, you can find popular trends or even emotional sentiment around brands.
Volumetrics only touch the surface of understanding; they are a numerical indication of user reaction, but if complemented with insights, businesses can discern what’s happening in social and why. Although all these insights can be derived from metrics, not all metrics can be insights.
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Understanding the basics of social media analytics is the first step to obtaining social media insights of strategic importance to organizations. As the data analytics industry grows throughout time, possessing basic knowledge about this topic will help you find the optimal solutions to social data analysis.
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