Prescriptive analytics is a technique that uses data analysis to quickly suggest ways to enhance business practises in order to achieve a range of desired outcomes.
Prescriptive analytics, to put it simply, is the process of analysing data, extracting all relevant information, and making predictions about what might occur. According to Talend’s paper, it is the last phase of contemporary computerised data processing.
After predictive and descriptive analytics, prescriptive analytics makes sense. While predictive analytics applies mathematical models to current data to guide (predict) future behaviour, enabling us to comprehend “what could happen,” descriptive analytics serves as the initial spark for succinct and unambiguous data analysis, defining “what we know.”
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.
Artificial intelligence techniques like machine learning—which refers to a computer program’s ability to understand and learn from data without additional human input while adapting—are the foundation of prescriptive analytics.
Processing the enormous volumes of data available today is made possible by machine learning. Computer systems automatically adapt to take use of new or additional data, far more quickly and thoroughly than human capacities could handle.
One common machine learning technique is the Bayes classifier, which uses a statistical model called Bayes’ Theorem to calculate the conditional probability of an event occurring.
Another common (nonstatistical) machine learning technique is ID3, which creates a decision tree that creates a graph of potential outcomes from a dataset. Developing a model from historical data that can take in fresh inputs and forecast their outcomes is the goal of both statistical and nonstatistical algorithms.
Prescriptive analytics is a useful tool in conjunction with predictive analytics, which uses statistical modelling to anticipate future events based on historical and current data. But it goes well beyond that.
Prescriptive analytics requires careful oversight even though it cannot be deemed a perfect method. To be effective, organisations need to know what questions to ask and how to respond to the answers. If the input assumptions are incorrect, the output findings will also be incorrect.
Use Cases of Prescriptive Analysis
Prescriptive analysis could be quite helpful in the financial market. Quantitative researchers and traders utilise statistical models in an attempt to maximise profits. Financial firms may employ similar tactics to manage risk and profitability.
To evaluate trade risks, financial institutions, for instance, can use algorithms that comb over historical trading data.
The resulting insights can help them decide whether to trade at all or how to hedge and size their positions. By choosing how and when to perform their transactions, these businesses may also use models to reduce transaction
Prescriptive analytics has the potential to optimise airline profitability by automatically adjusting ticket prices and availability in response to a range of factors, including weather patterns, customer demand, and fuel expenses.
For instance, the computer may automatically lower rates to prevent them from falling too low owing to this year’s higher oil prices if it notices that pre-Christmas ticket sales from Los Angeles to New York are lagging behind last year’s.
In addition, an algorithm has the ability to automatically raise ticket prices if it detects that there is a greater than normal demand for tickets from St. Louis to Chicago because of snowy roads.
Most of us have undoubtedly visited Amazon at some point and made a purchase. The Amazon app will provide several suggestions for you right away based on your past purchases and internet searches. This is achieved by the application of prescriptive analysis.
Using information from other consumers with similar search and purchase histories, they evaluate what other customers have bought.
Amazon and other big-box retailers are employing predictive analytics to sort through enormous volumes of data. Finding products with a higher chance of being purchased is the ultimate goal. Actually, YouTube uses a similar strategy as well.
The use of algorithms to assess risks and recommend investments can improve investing decisions, which are often based on intuition.
One example from the venture capital space is an experiment published in the Harvard Business Review, which examined how well an algorithm recommended which companies to invest in against what angel investors decided to do. The outcomes were intricate.
Angel investors who were less skilled at managing their cognitive biases and had less investing experience beat the algorithm; on the other hand, angel investors who were more experienced and had better cognitive bias management skills beat the algorithm.
The present study underscores the critical function of prescriptive analytics in decision-making, particularly in situations where experience is scarce and cognitive biases must be detected.
Whether or not an algorithm is utilised, human judgement is still necessary because an algorithm is only as good as the data it is trained on.
When you think of organisations that use and analyse a lot of data, you might not think of colleges and university admission offices. Predictive analytics, it turns out, might benefit them just as much as other sectors.
A report indicating a decline in autumn enrollment rates received by a college admissions department in July is a typical example. Panic may ensue, leading to the implementation of a plan that, in the absence of prescriptive analytics, may or may not work.
Prescriptive analytics can be used by colleges to determine the most effective ways to recruit prospective students. Predictive analytics would only be useful to colleges if they were aware of which students were most likely
Based on your interactions with their platforms and maybe others, businesses’ algorithms gather information. A combination of the viewer’s previous actions may cause an algorithm to publish a certain recommendation.
Prescriptive analytics is used on social media platforms; two examples are the “explore” page on Instagram and the “For You” feed on TikTok. A user’s interactions on the app are weighed based on signs of interest, much like lead scoring in sales, according to the company’s website.
This prescriptive analytics use case could lead to increased customer satisfaction, higher customer engagement rates, and the capacity to retarget customers with ads based on their past behaviour.
Prescriptive analytics is also used algorithmically to detect and report bank fraud. With so much data kept in a bank’s system, it would be nearly impossible for a human to manually identify any suspicious activity in a single account.
Using client transaction history, an algorithm examines and looks for anomalies in newly collected transactional data. Let’s say, for illustration purposes, that you typically spend $3,000 per month, but this month your credit card has been charged $30,000.
The application scans your transactional data for patterns, alerts your bank, and makes recommendations. Since the credit card may have been stolen in this instance, revoking it might be the wisest course of action.
When used appropriately, prescriptive analytics can help businesses make data-driven decisions instead of snap judgements based only on gut feeling.
Compared to relying solely on averages, it can simulate and present the likelihood of various outcomes, giving businesses a greater understanding of the level of risk and uncertainty they face. More precise prediction of worst-case scenarios would enable businesses to make appropriate plans.