Prescriptive Analytics may be defined as the branch of analytics which provides guidance on how to make optimal use of data and gain maximum output from it. It is related to both descriptive analytics and predictive analytics, but it helps users to determine the best solution among various possibilities. Descriptive analytics offers BI insights into what has happened, predictive analytics focuses on forecasting possible outcomes but prescriptive analytics aims to find the best solution.
How does Prescriptive Analytics work
Prescriptive analytics requires a clear path and a specific algorithm-based model. The first step in this kind of analytics is the business requirement. Recommendation can be provided only on the accuracy of the provided information. There is no specific template for a type of problem. For each requirement, the model is customized to suit its needs.
Benefits of Prescriptive Analytics
Predictive and descriptive analytics’ results are always not sufficient unless they are simulated and controlled if required. Prescriptive Analytics fills the gap in multiple ways:
Optimize business actions – Since prescriptive analytics is able to go beyond forecasting any major strategic organizational changes, users can now see which factors affect the outcomes.
Cost efficiencies and in-house capabilities – Organizations who invest in in-house capability extension technique gain more profits. These solutions helps internal teams to be involved in a collaborative process and understand the impact of the decisions.
Improve productivity – When we say entire business is benefited, it also implies that the entire team got involved and collaborated. This process reduces data silos and combined effort is encouraged which ultimately leads to improved productivity.
Building a scalable & repeatable process – Creating an in-house simulated environment helps to mimic current market conditions. This kind of analytics shows market is fluid, so a scalable model is necessary. After running multiple scenarios and comparing all results, it can adopt most efficient way to make the process scalable.
Expense Reduction – With the help of an accurate model, companies can ensure proper inventory management. This helps in cost reduction and also the manual processes involved.
Applications in Various Industries
Prescriptive Analytics has many use cases. Some of them being:
Healthcare Industry – Different categories of diseases can be identified. Not only that, they can also be multi-classified based on a range of parameters. Specific patient population can also be classified. With this technology at hand, the data collection and analysis gets a tad easier. It can use patient and clinical data to promote wellness and manage diseases in an effective manner.
Oil Production – In this segment, optimization of fracking procedure has led to a massive growth in the whole process.
Travel & Transportation Sector – Right from online travel to hotel booking, determining prices and pitching sales based on customers’ preferences, route optimization, categorizing types of travelers, etc. These insights can be derived from the data already available in the current databases. This encourages healthy competition in the business.
Miscellaneous – It can determine optimum, personalized option for a client and enhances customer experience. It can help in providing the correct routing information to drivers. Prescriptive analytics also helps estimate the best engagement opportunities possible in social media. In case of wine retailer companies, customer loyalty is increased as they can have chat sessions with wine experts.
Future Prospect
Knowledge of the right or appropriate solution for any problem means half of the problem is already solved. With its augmented use, business leaders can decrease financial losses. It can also leverage suitable applications that can enable to select the apt marketing emails to customers. Thus, prescriptive analytics has much to offer in any domain where it is used.
