What Is Prescriptive Analytics? A Comprehensive Guide
By Indeed Editorial Team
Published 29 September 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
With the sheer amount of data available to companies, many organisations and institutions seek ways to best leverage this information to drive successful business outcomes. One such solution is through the use of prescriptive analyses, which provides the ability to analyse huge amounts of data using complex algorithms. Understanding more about this type of analytic data collection and its applications in various industries can help you decide if it can benefit the organisation you work for. In this article, we define prescriptive analytics, explain why it's important, share the benefits of using it and provide some examples of effective use.
What is prescriptive analytics?
Prescriptive analytics is the process of understanding and analysing data to determine the best course of action. By utilising a combination of current and historical data, trends, advanced algorithms and other relevant events, the analysis aims to predict what could happen in the future and recommend subsequent next steps. For instance, if over 50% of customers left a one-star review on a certain product, an algorithm could predict that customers weren't likely to repurchase the product in future and, hence, recommend a product review or overhaul to address customer issues.
While using algorithms in analytics has many potential applications and benefits, it's important to note that they cannot completely replace the need for human judgement in making well-informed decisions. No form of analytics is completely accurate, but effective use may decrease risk and improve the efficiency of decision-making processes.
Why is prescriptive analytics important?
Prescriptive analysis helps organisations drive business value and reach their organisational goals by identifying the best course of action to take. It provides many potential benefits to an organisation, such as reductions in operating expenditure, lowered inventory, increased customer satisfaction and greater revenue streams. Analytics also save data analysts and decision-makers valuable time in making connections between what the data shows and what to do next by optimising the decision-making process with data-informed recommendations.
Benefits of using prescriptive algorithms
Company executives are constantly looking for ways to optimise business efficiency, and these data give them the ability to do so. Here are several of the benefits:
Providing a roadmap for success
By pulling together data and applying multiple simulated actions to a scenario, analytic models can help streamline an organisation's path to success. Machine-learning algorithms classify possible actions to take as a result of these predictions and then define the necessary steps to minimise failure and achieve success. With the need for trial and error greatly reduced, organisations can achieve their business goals faster.
Reducing human error or bias
If companies use artificial intelligence software to utilise analytics, this system may eliminate the need for humans to complete this type of analysis manually, decreasing instances of human error. For example, an investment firm utilising prescriptive analytics for financial forecasts may rely on such technology to limit instances of mathematical errors. In addition, many industries use these systems to help increase their ability to rely on statistics during decision-making processes.
Freeing up valuable time for other tasks
While artificial intelligence is able to process large amounts of data both faster and more accurately than a human can, the data team can spend more time on other tasks that add value. This may include focusing on data-informed recommendations and working on further testing and refining ideal solutions where there's a need for human discernment. With this optimised distribution of work, each party can focus on doing what they're best at.
Decreasing exposure to risk
A fundamental element of prescriptive analysis is determining the potential negative future outcomes of decisions. Many analytics systems can calculate the quantitative level of risk associated with specific choices. When the time comes to make decisions, having access to this data can help limit the risk associated with your actions.
Examples of effective prescriptive analysis
Here are some examples of industries that effectively use prescriptive algorithms:
Hospitals and clinics may use prescriptive analysis to administer treatments more effectively. Using large databases of medical data, prescriptive analysis systems can predict which treatments and procedures are most likely to lead to improved patient outcomes. Doctors and medical professionals then critically assess prescriptive data to inform their diagnoses and treatment plans. Prescriptive analysis may also be beneficial in the medical research field in identifying the best candidates for medical studies and, hence, increasing the research capabilities of pharmaceutical companies and other healthcare-related researchers.
Additionally, prescriptive analysis can help identify hospital patients who are at high risk of readmission. Health care professionals can pre-emptively do more for these patients by educating them on self-care upon discharge or scheduling follow-up appointments. Doing so can, in turn, reduce the likelihood of returns to the hospital or accident and emergency departments.
Financial organisations can benefit from the use of predictive analyses in many forms, such as:
Investing: Many investors use some form of prescriptive analysis to manage their portfolios. Technical analysts can use such systems to help them identify, buy and sell signals for short-term trades.
Credit scoring: Prescriptive analysis can help to determine a potential customer's payment behaviour more accurately. Through aggregating relevant data points, customers receive a score based on their track record of past payments, creating transparency around any future payment delays.
Fraud detection: An algorithm can scan new transactions and flag any unusual activity using a customer's past transactions as a baseline. For instance, if sudden credit card expenditure originates from a new location, the algorithm can alert the bank and recommend blocking or cancelling the card for safety.
Cross-selling products: By analysing your account history and transactions, an algorithm can recommend other related bank products based on your profile. For example, if you have a large pool of savings in the bank, the algorithm may recommend that you sign up for a fixed-interest deposit or start a private banking account.
Airlines and other transportation providers can use prescriptive analysis to adjust ticket prices based on numerous factors automatically. By predicting consumer behaviour, factoring in weather conditions, upcoming holidays, fuel costs and accounting for historical fares, airline companies can price their tickets to maximise revenue and profit. For example, if an algorithm flags that this year's June school holiday ticket sales are slower than last year's, it can temporarily lower prices until sales reach a certain target. Using the same demand forecasts, airlines can also optimise their fleet plans and crew schedules.
Similarly, hotels and other accommodation providers can use prescriptive analyses to help gauge expected occupancy rates and help guide promotional activities around the expected demand. For example, if October has historically been a slow season with decreased demand, a prescriptive algorithm system can recommend that the venue advertise promotions during this time.
Sales and marketing
In sales, prescriptive analysis can play an important role in lead scoring, which is the process of ranking potential leads according to how likely they're to convert into actual customers. By assigning a higher point value to actions along the sales funnel that imply purchase intent, companies can then prioritise and target these higher-scoring leads. Actions with a higher point value can include the number of page views, time spent on a page, site searches and content engagement.
In marketing terms, companies may program their analytics systems to track consumer behaviour, identify interests and pull search queries to determine the best products for advertisements. For instance, if you recently searched for airline tickets to a particular destination, a descriptive algorithm system for an online tour operator may send you promotions for activities and suggest itineraries for that destination.
Through algorithmic use of prescriptive analysis, many platforms can recommend personalised content to individual users. This might be via social media platforms, content streaming services, e-commerce sites or dating apps. Based on your past browsing and engagement history on a platform, algorithms gather data that can then focus on certain preferences and release personalised recommendations. For instance, if you regularly watch romantic comedies on a streaming platform, you're likely to see similar programmes of the same genre appear in your recommended feed.
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