In the world of business, it\u2019s not uncommon to find people using terms they don\u2019t completely understand. The difference between quantitative and qualitative analytics certainly falls into that category.<\/p>\n
For those interested in pursuing a career in business analytics<\/a>, it\u2019s critical to understand the differences between the two and the situations where they are applied. Understanding them is key for research in all kinds of areas, from marketing and sales to high-level business strategy.<\/p>\n Generally speaking, quantitative analysis involves looking at the hard data, the actual numbers. Qualitative analysis is less tangible. It concerns subjective characteristics and opinions \u2013 things that cannot be expressed as a number.<\/p>\n Here\u2019s a closer look at aspects of both and how they are used.<\/p>\n How many coins do you have in your pocket? How old is your car? What\u2019s your height and weight? Those are simple examples of using quantitative traits. Each can be expressed as a number.<\/p>\n In business, quantitative analytics uses such traits to create datasets that managers can consider when making strategic decisions. Examples of this could include the following:<\/p>\n In all the above cases, quantitative analysis is used to come up with hard data that leads to better decisions.<\/p>\n Because quantitative analysis strips all issues down to facts and figures, all ambiguity of language, interpretation, and emotion is removed. If done properly using strict rules, smaller datasets can be extrapolated to analyze and predict the behavior of larger groups<\/a>.<\/p>\n For example, if 700 out of 1000 potential customers abruptly leave your website from the same page, it\u2019s reasonable to assume this is happening at a similar ratio with larger amounts of customers.<\/p>\n Businesses use qualitative analytics to assess situations where hard numbers are impossible. Where quantitative analytics is objective and deductive in assessing a situation, qualitative is subjective and inductive.<\/p>\n Simplified versions of qualitative analysis would include answering questions about the softness of a blanket, the aesthetic value of a new piece of clothing or the impact of a Claude Monet painting. None of these can be expressed in numbers, but it\u2019s possible to have a strong and clear \u2013 if subjective \u2013 opinion.<\/p>\n In business, gathering information on this type of subjective material is part of qualitative analytics. It could involve:<\/p>\n Clearly, these are areas where opinion counts. However, a drawback of qualitative analysis is that findings cannot be applied to larger populations. Just because 50% of one set of customers prefer your product in the color red, that doesn\u2019t mean a similar percentage of a larger group will feel the same way.<\/p>\n Some of the most common methods of gathering qualitative data include:<\/p>\n Additionally, most traditional forms of qualitative research employ trained moderators. This helps keep interviewer bias from creeping into the process.<\/p>\n With qualitative analytics, it\u2019s more difficult to generate information that is definitively factual. However, it is the only way to create useful data<\/a> in areas that cannot be reduced to numbers.<\/p>\n That\u2019s a brief overview of both quantitative and qualitative analytics. Both have their uses, depending on the situation being assessed. It\u2019s vital for business students<\/a> to clearly understand the differences between the two and what situations call for their use.<\/p>\n\n\nQuantitative Analytics<\/h2>\n
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Qualitative Analytics<\/h2>\n
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