Tabulation of social metrics such as Facebook likes, Tweets, or followers.Summarizing past events such as regional sales, customer attrition, or success of marketing campaigns.Here are some common applications of descriptive analytics: The majority of industry literature echoes the sentiment that with predictive and prescriptive analytics, the business data that once simply described past events can now view “nuggets of useful information,” thanks to big data-powered descriptive analytics. With huge amount of multi-channel customer data coming in, the data-driven businesses are far better positioned to gauge the individual preferences of customers and design appropriate personalized offerings. The author notes that with the help of big data, all three types of data analytics are now used to better understand the customer. 80% of business analytics falls within the ambit of descriptive analytics.Īny good data scientist may try to use the results of descriptive analytics and further tweak the data or trends or pattern analysis to forecast future trends in business. Most statistical calculations are generally applied to descriptive analytics.ĭescriptive vs Predictive vs Prescriptive vs Diagnostic Analytics describes descriptive analytics as the simplest form of data analytics, which captures big data in small nuggets of information. Thus, descriptive analytics is more suited for a historical account or a summary of past data. In contrast to both, the descriptive analyst simply offers the existing data in a more understandable format without any further investigation. While the predictive data analyst uses investigation to understand the future, the prescriptive data analyst uses investigation to suggest probable actions. In predictive and prescriptive analytics, the data analyst has to “investigate” beyond surface data. Thus, collection and interpretation of large amount of data may be involved in this type of analytics. Some common methods employed in descriptive analytics are observations, case studies, and surveys. As this form of analytics doesn’t usually probes beyond surface analysis, the validity of results is more easily implemented. Descriptive analytics rarely attempts to investigate or establish cause and effect relationships. What Does Descriptive Analytics Do?ĭescriptive analytics helps to describe and present data in a format which can be easily understood by a wide variety of business readers. It is relevant to note that in the big data world, the “simple nuggets of information” provided by descriptive analytics become prepared inputs for more advanced predictive or prescriptive analytics that deliver real-time insights for business decision making. Descriptive analytics has limited shelf life and quickly becomes outdated.Ī third application of descriptive analytics involves company reports that simply provide a historic review of an organization’s operations, sales, financials, customers, and stakeholders.This type of analytics offers “a rear-view mirror into business performance.”.Two interesting points about descriptive analytics are: As an incentive to the winning media channel, you can even declare a prize! You will some other interesting examples of descriptive analytics in this HBS blog post. You can then publish your findings through visual dashboards and share them with your senior or peers in the company. You can even compare this traffic-source data to historical traffic-source data from the same media channels. Descriptive analytics will help you to review the current traffic data of the product page to compare the media channel outputs. A web server’s summarized performance reports may help the user analyze the past events and assess whether a past marketing campaign was successful or not based on preset key performance indicators (KPIs).Īnother example of descriptive analytics may be that you are responsible for monitoring the traffic sources (media channels) to the premier product page of your company’s website. The most common example of descriptive analytics is the reports that a user gets from Google Analytics tools. In descriptive analytics, data aggregation, and data mining techniques are used to collect and review the historical data of a business to gauge the past performance.
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