Big Data analytics is a very important topic of study today, as there is a lot of data available to companies, and handling them manually is almost an impossible task. Data analytics techniques are used to filter, extract, collect, and store information to drive real business value.
Prescriptive analytics is also a data analytics technology that is used widely to identify data-driven strategic decisions. There are two other popular techniques namely descriptive analytics and predictive analytics.
In this article, we will understand what prescriptive analytics is and how it is different from the other two. Let us begin.
What is Prescriptive Analysis?
Prescriptive analytics is a method of analyzing data and making immediate recommendations on how to improve company procedures to meet a variety of expected results.
In simple words, prescriptive analytics analyzes the data, extracts all the information that we get to know from the data, and makes predictions of what could happen. It is the final step in modern, computerized data processing, as written by talend in one of their articles.
Prescriptive analytics is the next logical step after descriptive and predictive analytics. Descriptive analytics acts as an initial catalyst for clear and concise data analysis, establishing "what we know," and predictive analytics applies mathematical models to current data to inform (predict) future behaviour, allowing us to understand "what could happen”.
The prescriptive analysis takes away the guesswork out of the data analytics and saves the time of data scientists and marketers by automatically connecting dots for them.
Working of Prescriptive Analytics:
According to Investopedia, prescriptive analytics is based on artificial intelligence techniques such as machine learning, which is the capacity of a computer program to comprehend and advance from data without extra human input while adapting.
Machine learning allows for the processing of today's massive amounts of data. As new or extra data becomes available, computer systems automatically change to take advantage of it, in a far faster and more complete manner than human capacities could handle.
For example, the Bayes classifier is a typical machine learning method that computes the conditional probability of an event occurring using Bayes' Theorem, a statistical model.
ID3, which builds a decision tree that forms a graph of possible outcomes from a dataset, is another typical (nonstatistical) machine learning approach. The purpose of both statistical and nonstatistical algorithms is to build a model from previous data that can accept new inputs and predict their results.
Prescriptive analytics complements predictive analytics, which employs statistics modelling to forecast future outcomes based on present and previous data. It, however, goes far beyond.
Although, it cannot be said that prescriptive analytics is a foolproof technique, it needs to be closely monitored. Organizations must know what questions to ask and how to react to the responses in order for them to be effective. The output findings will not be correct if the input assumptions are false.
Also Read | What is predictive modelling?
Use Cases of Prescriptive analytics:
There are many examples of prescriptive analysis in real life. We have researched and listed some of them below:
Prescriptive Analysis in Financial Markets:
In the financial market, the prescriptive analysis might be quite useful. Statistical modelling is used by quantitative researchers and traders to strive to optimize profits. Similar strategies may be used by financial institutions to control risk and profitability.
Financial institutions, for example, can create algorithms that sift through previous trading data to assess trade risks.
The resultant insights can assist them in determining how to size positions, hedge them, and even whether to trade at all. These companies may also employ models to cut transaction costs by determining how and when to conduct their transactions.
Prescriptive Analysis in Airlines:
Prescriptive analytics may help an airline maximize profits by automatically altering ticket rates and availability based on a variety of parameters such as consumer demand, weather, and gas costs.
For example, when the computer detects that pre-Christmas ticket sales from Los Angeles to New York are trailing behind last year's, it may automatically cut prices while ensuring that they do not fall too low due to this year's higher oil prices.
At the same time, if an algorithm determines that demand for tickets from St. Louis to Chicago is higher than usual due to icy road conditions, it can automatically hike ticket costs.
Prescriptive Analysis in Retail:
Let's look at an example to assist us to comprehend. At some time, we've all probably visited Amazon and made a purchase. When you launch the Amazon app, the site will automatically propose a number of things based on your previous purchases and online searches. The prescriptive analysis is used to accomplish this.
They assess what other customers have purchased using data from other customers with comparable buying and search histories.
Amazon and other major retailers are sifting through massive amounts of data using a predictive analytics technique. The ultimate objective is to locate items with a better likelihood of being purchased. In fact, youtube also follows the same approach.
Prescriptive Analytics in Venture Capital:
Algorithms that analyze risks and propose whether to invest can help to enhance investment judgments, which are typically dependent on gut impulses.
In the venture capital area, one example is a Harvard Business Review experiment that compared the effectiveness of an algorithm's recommendations regarding which firms to invest in the decisions of angel investors. The results were complex.
Angel investors who were less experienced in investing and less proficient at managing their cognitive biases beat the algorithm; but, angel investors who were experienced in investing and able to manage their cognitive biases outperformed the algorithm.
This experiment highlights the important role that prescriptive analytics must play in decision-making, as well as its ability to assist the decision-making process when experience is lacking and cognitive biases need to be identified.
Because an algorithm is only as good as the data it's trained on, human judgment is essential regardless of whether an algorithm is used. (here)
Prescriptive analytics in Higher Education:
Colleges and university admission offices may not come to mind when you think of institutions that use and analyze large amounts of data. However, it turns out that predictive analytics may help them just as much as other industries.
A common example is a college admissions department getting a report in July indicating that autumn enrollment rates had decreased. This might result in panic and the adoption of a strategy that may or may not succeed without prescriptive analytics.
Colleges may use prescriptive analytics to figure out the best methods to enrol potential students. Colleges would only benefit from predictive analytics if they knew which students were most likely to enrol.
Prescriptive analytics in Content curation:
Businesses' algorithms collect information depending on your interactions with their platforms (and potentially others, too). An algorithm's publication of a specific recommendation might be triggered by a combination of the viewer’s prior behaviour.
For example, If you watch technical review videos on YouTube on a regular basis, the platform's algorithm is likely to evaluate that data and propose that you watch more of the same sort of video or comparable material.
TikTok's "For You" feed and Instagram's “explore” page are examples of prescriptive analytics in action on social media. According to the company's website, a user's interactions on the app are weighted based on indications of interest, similar to lead scoring in sales.
Higher customer engagement rates, better customer happiness, and the ability to retarget consumers with advertising based on their behavioural history are all possible outcomes of this prescriptive analytics use case.
Prescriptive analysis in banking:
The identification and flagging of bank fraud is another algorithmic use of prescriptive analytics. It would be practically hard for a human to manually notice any questionable behaviour in a single account due to the enormous volume of data maintained in a bank's system.
An algorithm analyses and scans fresh transactional data for abnormalities using past transaction data from customers. For example, suppose you normally spend $3,000 each month, but your credit card has a $30,000 charge this month.
The program looks for trends in your transactional data, notifies your bank, and suggests a course of action. In this case, the best course of action could be to revoke the credit card because it was perhaps stolen.
Also Read | Improving Digital Customer Experience in Banking
When applied correctly, prescriptive analytics may assist companies in making decisions based on well-researched data rather than rash decisions based on intuition.
It can simulate and display the likelihood of alternative outcomes, allowing firms to better grasp the degree of risk and uncertainty they face than relying on averages. Worst-case events may be predicted more accurately, allowing businesses to plan accordingly.
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