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Prescriptive Analytics: Optimize Business Decisions in 2024

Cem Dilmegani
Updated on Jan 11
8 min read

With the ever-increasing amount of data, business leaders need to quickly turn this information into actionable insights. Prescriptive analytics can be a crucial tool for business leaders to make informed decisions and stay ahead of the competition. However, compared to predictive analytics, it is less popular.

Prescriptive analytics is a type of data analysis that can make predictions and specific recommendations whereas predictive analytics uses previous data to predict the future. In this article, we provide a comprehensive guide to prescriptive analytics by explaining:

  • Differences between descriptive and predictive analytics
  • Its benefits
  • Its applications to provide business leaders with a competitive edge.

What is prescriptive analytics?

Prescriptive analytics uses cutting-edge technology that identifies patterns, trends, and relationships in datasets using complex algorithms like machine learning. As a result, it gives executives actionable suggestions based on the relevant data.

Prescriptive analytics is business analytics that advises on how to capitalize on future opportunities or prevent future risks. It analyzes historical performance and trends to determine how to achieve future goals.

What are the components of a prescriptive analytics architecture?

Prescriptive analytics is a branch of advanced analytics that suggests the optimal course of action. It can use: 

  • Data analysis: The data analysis process can gather and clean important data.
  • Mathematical modeling: The modeling process can explain correlations and forecast future events using statistical and machine learning methods.
  • Optimization algorithms: Optimization algorithms can suggest the best course of action depending on the criteria and goals specified in an optimization problem. The optimization algorithms can offer recommendations based on:
    • Historic data 
    • Current trends 
    • Limitations 
    • Cost optimization 
    • Risk management
    • Resource usage.

What is real-time prescriptive analytics?

Advanced analytics uses descriptive and predictive analytics with machine learning algorithms to provide real-time prescriptive analytics. It uses algorithms and machine learning to find patterns and relationships in time series in real-time. So, it uses data and restrictions to decide the optimal action.

How does real-time prescriptive analytics work?

Prescriptive analytics can help firms remain ahead of the competition with the most up-to-date information to make smart decisions based on market trends and data.

Real-time prescriptive analytics incorporates several steps:

  • Data collection: The initial stage collects real-time sales, customer behavior, sensor, and other data.
  • Data pre-processing: The data is cleaned and made ready for analysis. This includes resolving issues with incomplete or inconsistent data, normalizing the data, and putting it in a form that can be used for analysis.
  • Modeling: The data is then examined to create models that explain the connections between the variables and forecast future results. Regression models, decision trees, neural networks, and other machine-learning techniques can be used.
  • Optimization: After modeling, the prescriptive analytics engine employs optimization algorithms to find the optimum action. Algorithms can assess cost, risk, and resource limits to provide recommendations.
  • Action recommendation: The prescriptive analytics engine makes recommendations based on optimization outcomes. Real-time dashboards or decision-makers can display these recommendations for judgment.

What is the difference between prescriptive, diagnostic, descriptive, and predictive analytics?

Prescriptive analytics is one of the analytics tools used in business. Some data analytics types can be listed as: 

1. Diagnostic analytics

Diagnostic analytics examines data and correlations between variables to determine the core cause of an issue. Its purpose is to detect problems, anomalies, and deviations from expected behavior.

Descriptive analytics, on the other hand, summarizes and characterizes data to comprehend patterns and relationships. It gives historical context and aids in the identification of trends and patterns in data.

Diagnostic and descriptive analytics are similar in that both employ data to evaluate and comprehend trends in data. But, diagnostic analytics focuses exclusively on identifying the core cause of a problem, whereas descriptive analytics offers a broad perspective of what has occurred in the past.

2. Descriptive analytics

Descriptive analytics summarizes and characterizes current and historical data to uncover patterns and relationships in the data. It gives historical context and aids in identifying trends and patterns in data.

On the other hand, predictive analytics uses current and historical data, statistical algorithms, and machine learning to determine the likelihood of future outcomes. It assists companies in making data-driven recommendations by forecasting future happenings and outcomes based on data.

The similarity between descriptive and predictive analytics is that both use data to analyze and understand patterns in the data. However, descriptive analytics provides an overview of what has happened in the past, while predictive analytics predicts what is likely to happen in the future.

3. Predictive analytics

Data, statistical algorithms, and machine learning techniques are used in predictive analytics to determine the likelihood of future outcomes based on historical data. Prescriptive analysis, on the other hand, can extend predictive analytics by creating recommended actions based on projected results and taking into account business strategies and goals.

The similarity between predictive and prescriptive analytics is that both use data and mathematical modeling to analyze future outcomes. However, predictive analytics only predicts the likelihood of outcomes to determine future performance based on data, while prescriptive analytics provides recommended actions based on those predictions.

Data analysis comparison table of the types of analytics tools

Analytics TypeDefinitionFocusSimilaritiesDifferences
DescriptiveSummarizes historical dataUnderstanding historical dataSimilar to predictive analyticsDescribes past, predictive predicts future
DiagnosticFinds root cause of issuesFinding root causeSimilar to descriptive analyticsFocuses on the root cause, descriptive offers broader perspective
PredictivePredicts future outcomesPredicting futureSimilar to prescriptive analyticsPredicts outcomes, prescriptive recommends actions
PrescriptiveRecommends actions based on predictionsRecommending actionsSimilar to predictive analyticsRecommends actions, predictive predicts outcomes

Table 1: Comparison of descriptive, diagnostic, predictive, and prescriptive analytics.

Key prescriptive analytics features

1. Utilizes advanced algorithms and machine learning

Prescriptive analytics works with the latest algorithms, such as machine learning tools, to look at large data sets to find: 

For example, a retail company can use prescriptive analytics to look at sales data and figure out: 

  • the best times to run promotions 
  • the most popular product categories
  • the most effective marketing channels 

After that, the analysis can be used to make specific suggestions about improving the company’s marketing strategy to boost sales.

2. Analyzes large datasets to identify patterns and relationships

Prescriptive analytics can rapidly process enormous amounts of data. This can assist companies in understanding their operations and market trends by identifying hidden patterns and linkages.

Prescriptive analysis can allow companies to acquire a competitive edge in decision-making. For example, a financial services company can use prescriptive analysis to look at millions of customer transactions and find spending patterns. Then, based on how much money users spend, the results can be used to tailor marketing campaigns and credit card offers. This can be done by establishing business rules based on prescriptive analytics.

Large data enabling factors in prescriptive analytics

With the help of the following technologies, prescriptive insights can be provided from massive data sets:

  • Advanced algorithms: Prescriptive analytics can utilize modern techniques such as machine learning and statistical analysis.
  • Computational tools: Prescriptive analytics can use computational tools like parallel processing and distributed computing architectures to ensure that data analysis is rapid and scalable, even on big data sets.
  • Cloud-based platforms: Cloud-based platforms and services can allow business users to efficiently handle big data sets in cloud data warehouses without needing costly hardware and IT resources.

3. Provides recommendations and predictions based on data analysis

Prescriptive analytics is more than data analysis to find patterns and relationships in huge amounts of data. Depending on the data examined, prescriptive analytics can generate precise recommendations and predictions.

For example, if a company is studying sales data, prescriptive analytics can find trends and links between: 

Based on this information, for example, prescriptive analytics can recommend optimizing marketing expenditures to increase sales. Or it can also predict which consumer segments are likely to respond to a specific marketing campaign.

4. Helps in business strategies with continuously updated predictions

Prescriptive analytics can provide firms with data-driven insights and recommendations in real-time. These can be utilized to make informed judgments on topics like: 

For example, suppose a company monitors its supply chain activities in real-time. In this case, prescriptive analytics can assist in determining bottlenecks and inefficiencies and suggest ways to fix them, and streamline supply chain operations like order fulfillment.

5 Benefits of prescriptive analytics

1. Improved decision-making process

Prescriptive analytics can provide businesses with specific predictions based on data analysis. For example, a retail company can use prescriptive analytics to look at sales data and find trends and patterns. This information can then be used to make smart decisions about: 

2. Increased revenue growth

With data-driven suggestions, prescriptive analytics can help business intelligence to improve its operations and drive revenue growth. A manufacturer can use prescriptive analytics to examine production data. This can help them find slow spots in the production line, i.e., bottlenecks that can be fixed to improve efficiency and make more money.

3. Enhanced competitive advantage

Prescriptive analytics can provide businesses with real-time, data-driven insights and recommendations that can help them stay ahead of the competition. For example, as mentioned above, a financial institution, like private equity, can leverage prescriptive analytics to look at market trends and how customers act. This information can then be used to make decisions about investing in products.

4. Improved operational efficiency

Prescriptive analytics can help businesses improve operational efficiency. For example, a logistics company can employ prescriptive analytics to look at shipping data and find ways to improve their operations, which they can implement.

5. Reduced costs

Prescriptive analytics can also aid businesses in reducing costs by pointing out where they can improve efficiency. For example, in healthcare, prescriptive analytics can be used to find patients at high risk of getting certain health problems and put them in order of importance. This information can help provide targeted treatments, which can help improve patient outcomes and lower the cost of care.

Note: The ethical use of patient data and privacy protection should always be a top priority in any healthcare application of prescriptive analytics.

5 applications of prescriptive analytics

Prescriptive analytics is a flexible technology that can be used in many different fields and ways. Here are some of the most common applications:

1. Supply chain optimization 

Businesses can improve their supply chain operations with the help of prescriptive analytics. Prescriptive analytics can give suggestions for: 

For example, prescriptive analytics can look at data on supplier performance and delivery times to find bottlenecks in the supply chain and suggest ways to improve efficiency and cut costs.

2. Sales and marketing

Prescriptive analytics can help businesses improve their sales and marketing strategies by telling them how to: 

  • Find their target audience
  • Lead generation
  • Improve their marketing campaigns. 

For example, it can look at customer data to determine how they buy things and suggest targeted marketing campaigns to boost sales and keep customers interested.

3. Financial planning and budgeting

Prescriptive analytics can help businesses with financial planning and budgeting by providing recommendations for resource allocation and expense management. For example, it can help with financial planning and budgeting in the following ways:

  • Resource allocation: By analyzing revenue and expenses, prescriptive analytics can assist businesses in optimizing resource allocation for maximum profitability. For example, it can suggest ways to redirect resources from underperforming departments to more profitable areas.
  • Expense management: Prescriptive analytics can help businesses identify areas where expenses can be reduced. For example, it can make suggestions to:
    • reduce waste
    • improve procurement processes 
    • and optimize supply chain operations.
  • Budget projections: Prescriptive analytics can estimate a company’s future revenue and expenses by analyzing historical financial data.
  • Profit optimization:  Prescriptive analytics can provide recommendations to determine the price of a product to optimize profits. For example, prescriptive analytics can consider data on:
    • pricing strategies 
    • marketing 
    • sales campaigns to determine sales prices and provide related recommendations.
  • Investment decisions: Prescriptive analytics can be used in making informed investment decisions by analyzing financial data. Prescriptive analytics can provide recommendations based on market trends and economic indicators.

Video: Why companies use prescriptive analytics: Understand Prescriptive Analytics in 20 Minutes.

4. Operations optimization

Prescriptive analytics can help businesses improve operations by suggesting ways to use resources more effectively. For example, by analyzing data about production processes, the prescriptive analytics system can: 

  • find bottlenecks 
  • suggest changes to the production line
  • figure out the best number of resources needed for each stage of production. 

This can help manufacturers improve their efficiency, reduce waste, and increase productivity, ultimately leading to the following: 

5. Risk management

Prescriptive analytics can examine data from previous incidents to identify potential risks and provide solutions to make future incidents less likely and less destructive. For example, a financial services organization could utilize prescriptive analytics to look for patterns in prior data on loan defaults and fraud. In this regard, prescriptive analytics can forecast how likely these events will occur again. For example, based on this analysis, the corporation could improve:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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