While traversing the path between raw data and useful insight, the first stop made by most organisations is descriptive analytics. This is because descriptive analysis is less about examination and extrapolation, and more about refinement and organisation. At its most basic, descriptive analytics is used to make large quantities of information more manageable, by condensing it into smaller, more-easily comprehendible chunks and summaries.
Slightly more abstractly, descriptive analytics is used to review past events, and to accurately identify (or describe) their significance in relation to business actions. This focus on the past is essential for businesses that wish to learn from past successes and mistakes. Say, for example, that a business suffers an unexpected quarterly loss. Descriptive analytics can be used to review the associated numbers, as well as any other contributing data, to help identify exactly where the problem lies. Of course, this isn’t to say that descriptive analytics is only useful in identifying past mistakes; businesses of all types use this form of analytics to generate accurate reports for everything from company finances, to inventory management, to office productivity, and beyond.
Descriptive analytics is especially useful when it comes to social-media analysis, where organisations are interested in keeping track of the direct numbers associated with shares, likes, retweets, followers, etc. Additionally, descriptive analytics encompasses the majority of statistical analysis. Descriptive analytics are used by those who want to better understand the reality of their business and the market as it is, and who want to easily summarise various aspects of it.
A step removed from descriptive analytics, predictive analyticsbuilds upon captured data to answer the difficult question: What could happen next? To do so, predictive analytics builds upon the same or similar methods used in descriptive analytics, but adds various forms of machine learning and statistical modeling to locate possible patterns and causes hidden in the raw information.
For this form of analytics to be effective, it requires a number of additional factors, the two most-important being (according to a survey by Forbes) effective technology, and organisational support.
Predictive analytics tools, usually in the form of predictive analytics software, enable users to mine through large volumes of data to find valuable relationships between causes and consequences, and to make educated predictions about data that is not currently available. In essence, predictive analytics is used to fill in the missing pieces of the data puzzle, through a better understanding of the data that is available. This information can be shared throughout every level of an organisation, and be put to use in improving processes on all levels, rather than just in relation to sales or marketing.
With predictive analytics, businesses can take the comprehension of consumers to the next level and predict their wants and needs. Companies can now show product recommendations based on previous actions, which is key with Netflix and Amazon. Display ads more accordingly based on the type of consumer and also send out better targeted email campaigns, all in the effort to maximise the data for the best ROI or metric you are tracking.
And while the conclusions produced through predictive analytics can never be 100% accurate, they are none-the-less generally-reliable forecasts that can be used to improve business. In fact, (86%) of executives who oversee predictive-marketing efforts report increased return on investment (ROI) as a result of their predictive marketing.
The final, and most-abstract form of analysis is prescriptive analysis. Prescriptive analytics takes the forecasting ability of predictive analytics a step further, by allowing users to create various ‘what-if’ scenarios, and then extrapolating possible outcomes based on a variety of variables. This makes it possible for organisations to see beyond the consequences of decisions, and into the consequences of potential decisions, in effect, creating a computer simulation for business growth.
Prescriptive analytics are often used in healthcare professions, where physician interpretation of facts are as important as hard evidence, and where this analytical model can be used to determine possible outcomes from a variety of variables. Other examples of businesses that benefit from prescriptive analytics are package delivery services, which use this method to determine the most effective package delivery routes, and airlines, which rely on prescriptive analytics to take into account the many potential factors used to determine fair pricing on airline tickets.
Although prescriptive analytics are generally much more complex to use, and expensive to integrate, than other, more-traditional analytics models, those organisations that adopt prescriptive analytics solutions often find that it is well worth the additional cost and effort.