In this digital era, ‌organisations are continuously looking for new ways to make sense of their previously stored data to improve decision-making and gain a competitive edge in the industry. Prescriptive analytics takes it a step further by providing actionable insights and recommendations to optimise business processes and drive better outcomes that can be helpful for companies in the long term.

To understand prescriptive analytics better, let’s have a look,

Prescriptive Analytics

Prescriptive analytics uses descriptive and predictive analytics to provide valuable insights and suggestions to optimise your business processes and drive better outcomes. This analytics is derived using machine learning and artificial intelligence algorithms to make recommendations on the best course of action to take in a given situation. It can be used to optimise business processes, reduce costs, improve customer satisfaction, and increase revenue.

Prescriptive analytics is like a doctor’s diagnosis for your business. It identifies the root cause of issues and recommends the best treatment plan to restore health and optimise performance.

How does Prescriptive Analytics work?

Prescriptive analytics involves four key steps:

  • Data collection and Preparation

The first step is to gather relevant data from multiple sources. This data is collected from internal and external sources, such as customer data, sales data, social media data, and market data.

Let’s say you own a restaurant and want to improve its menu offerings. To gather data, you may look at customer data, such as what dishes your customers frequently order, what dishes are returned, and what items receive positive feedback when they reach customers’ taste buds. You may also collect sales data to see which dishes are the most profitable and which ones need to be reevaluated. Additionally, you could monitor social media platforms for mentions about your restaurant and specific menu items to see what your customers say and what types of dishes they may be interested in trying. Finally, you may analyse market data to see what food trends are popular and what types of cuisine are currently in demand. By gathering and analysing this data, you can make decisions on what new dishes can be added to your menu and what changes to make to improve the customers’ dining experience.

  • Data Analysis and Modelling

Once the data is collected, it is analysed to identify patterns, trends, and anomalies. This step involves using descriptive and predictive analytics techniques to gain insights into the data.

Let’s check the process for this step. You may have already decided to add new menu items based on customer preferences, adjust pricing based on profitability, or invest in marketing campaigns targeting specific demographics. You may also use this data to improve operations. For instance, you will be able to identify the busiest times of the day to schedule staff more efficiently and adjust inventory levels based on sales trends to avoid overstocking or shortages.

This makes you take data-driven decisions that improve business outcomes, such as increasing revenue, improving customer satisfaction, and reducing costs.

  • Recommendations and Optimisation

Based on the insights gained from the data analysis, prescriptive analytics algorithms make recommendations on the best course of action to take.

For example, after analysing the data, prescriptive analytics algorithms may recommend you add a new vegetarian dish to the menu, as vegetarian options are becoming increasingly popular and the restaurant’s competitors are also offering similar dishes. It also suggests that you remove a particular menu item that has consistently received negative feedback from customers. Additionally, it may recommend that you adjust the pricing strategy for certain dishes to increase profitability based on sales trends and customer demand. 

  • Implementation and Monitoring

The final step in prescriptive analytics is to take action based on the recommendations provided by the prescriptive analytics tool. The actions can be automated or executed manually, depending on the business’s needs.

For e.g., by following the recommendations suggested, you can improve your menu offerings and ultimately enhance your customers’ dining experience.

Role of Data and Technology 

Data and technology play a crucial role in prescriptive analytics. As this analytics requires large volumes of high-quality data to be effective. This data needs to be accurate, complete, and up-to-date to produce meaningful insights. Advanced analytics technologies, such as machine learning algorithms, artificial intelligence, and natural language processing, are used to analyse the data and provide actionable insights.

Benefits of Prescriptive Analytics

Prescriptive analytics offers several benefits, including:

  • Improved Decision-making Process

Provides actionable insights that can help you to make better decisions, optimise business processes, and improve outcomes.

  • Reduced Costs 

You can identify inefficiencies and areas of waste in business processes, allowing you to reduce costs and increase the profitability of your business.

  • Enhanced Customer Satisfaction

You can identify your customer behaviour patterns and preferences, enabling you to tailor the offerings to meet their needs and choices.

  • Increased Revenue

It can help you to identify opportunities to increase your revenue, such as upselling and cross-selling opportunities that can also help you to enter new areas in the market.

  • Increased operational Efficiency

Prescriptive analytics can help you ‌improve outcomes, such as patient outcomes in the healthcare department, improving customer satisfaction in retail, and revenue in the finance sector.

Applications of Prescriptive Analytics

Prescriptive analytics has numerous applications across different industries, including:

  • Healthcare Sector

When it comes to healthcare you may be able to improve patient outcomes and reduce costs. For instance, you can use it to predict patient readmissions, identify patients who are at risk of developing chronic diseases, and optimise treatment plans.

  • Retail Operations

In retail, prescriptive analysis can be used to optimise your inventory, improve pricing strategies, and enhance your customer experience. For example, prescriptive analytics can be used to identify products that are likely to sell out quickly, enabling you to stock up on those items and avoid stock-outs.

  • Financial Management

In finance, it is used to optimise your investment strategies, manage risks, and detect fraud. For example, prescriptive analytics can be used to identify fraudulent transactions that may happen and prevent financial crimes which you might face.

  • Manufacturing Operations

You can use it in manufacturing to optimise production processes, reduce downtime, and improve quality control. For example, prescriptive analytics can be used to predict equipment failures, enabling you to take preventive measures in case of any downtime thereby avoiding it completely.

  • Sports Management

When it comes to sports it is used to improve performance, optimise game strategies, and enhance the experience for viewers. For example, you can use prescriptive analysis to predict player injuries, optimise player rotations, and improve ticket pricing strategies.

Challenges Faced During Prescriptive Analytics 

  • Data Quality and Accessibility 

Prescriptive analytics require large volumes of high-quality data to be effective. If the data is incomplete, inaccurate, or outdated, the insights provided by prescriptive analytics can be misleading or incorrect, which can have adverse effects on your company.

  • Implementation Costs and Complexity

It is a complex field that requires advanced analytics skills and expertise. And implementing prescriptive analytics can be challenging, especially for small and medium-sized businesses. So you need to hire people who have the required expertise to perform this task.

  • Legal and Ethical Considerations

It involves collecting and analysing sensitive data, such as customer information and financial data. You must ensure that the data you have collected is kept secure and private to avoid any potential breaches from your end.

Summing Up

Prescriptive analytics is a powerful tool that enables you to make informed decisions quickly and efficiently. It offers actionable insights that can help you improve outcomes, reduce costs, and enhance ‌customer satisfaction. However, implementing prescriptive analytics can be challenging, and you need to ensure first that you have the necessary data, technology, and expertise to be successful.

Want to know more about it? Connect with BlueSky as prescriptive analytics is a valuable tool that can help you ‌stay ahead of the competition and achieve more.

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