Introduction
The most pivotal tool that the present companies can bank upon is undoubtedly analytics. Analytics today is called the reservoir of data for companies through which they can extract customer information. As per an estimate, the forum of big data is growing at such a rapid pace that it is slated to reach a net worth of 268 billion dollars by 2025.
With the growth of artificial intelligence and smart technologies, the nature of carrying out analytics by the companies is also changing. Machine learning techniques like clustering, classification, decision trees, bootstrapping and time series analysis are constantly being employed to filter raw data and gain critical insights.
The aim of this article is to examine the domains of analytics in detail. Starting from descriptive analytics, the article moves to provide a brief overview of predictive analytics. The article also studies prescriptive analytics in the end.
With this exploration, the boundaries of marketing vis a vis marketing are thoroughly examined.
An overview of descriptive analytics
Historical analysis of data by statistical methods is what descriptive analytics is all about. As an example, the behavior tracking of participants in an online ecosystem over a period of time can be studied using descriptive analytics. The two techniques which are most commonly used in descriptive analysis are data mining and data aggregation. Using these techniques, data is subject to a multi tier cleansing process before it can be analyzed further. Eventually, information and meaning associated with the data is comprehended.
Over the last few years, a trend has emerged in which the companies outsource their data to an analytics training institute which hosts professionals that derive critical insights from it.
A brief about predictive analytics
Predictive analytics is all about the usage of statistical modelling, machine learning and data analysis to predict future events based on historical facts. In business intelligence, predictive analytics is all about capturing valuable data based on previous transactions so that risks associated with future transactions can be predicted at an earliest stage. The applications of predictive analytics include telecommunications, stock markets, predicting the value of shares, retail, e commerce, healthcare etc.
One of the most potential applications of predictive analytics is the determination of credit score of a customer. Input variables which are used to determine such a score include previous repayments, willful default and loan history.
A glance of prescriptive analytics
Prescriptive analytics is an umbrella term which includes both descriptive and predictive analytics. To mention briefly, prescriptive analytics makes use of all those techniques which were used at the previous stages of predictive and descriptive analytics. For instance, advanced algorithms, statistical computing, modelling and optimization techniques may be used.
The best thing about prescriptive analytics is that it is used to predict the outcome of an event based on a different set of variables. This means that it is a very handy technique in predicting the consequences where insufficient data input is available. With special focus on actionable insights, prescriptive analytics has become one of the most potent tools in the hands of business analysts.
Since, prescriptive analytics is a relatively newer technique, it is being researched and developed thoroughly in analytics training institutes and centers worldwide.
The boundaries of marketing vis a vis descriptive, predictive and prescriptive analytics
The domain of marketing has undergone an evolution from the time business analytics was roped in. In a data driven market, the analysis of customer data, services, products, performance, transactions, recommendations, preferences and choices is vital for the growth of any startup. But this does not stop here. The history of the market is a part of the evolution continuum. Right from the analysis of the historical data with the help of descriptive analytics to the prediction of the boom and bust cycles with the help of predictive analytics, business analytics has undergone a paradigm shift.
Prescriptive analytics is a business research probe which can analyze a particular niche of the business market. For instance, it is prescriptive analytics which can predict why fur caps are in demand in Central Asia even in the summer season when the temperature levels are high. The answer lies in the cultural context of the region and this factor is taken into account only by prescriptive analytics.
Conclusion and future prospects of analytics
The future scope of various types of analytics is galactic and business analysts need to incorporate newer techniques in their toolkit so that they can witness a surge in their profits.
