Pescriptive Analysis: Understanding "What Should We Do?" with Real-World Examples

byMD Modasshar hussain -
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Pescriptive Analysis: Understanding "What Should We Do?" with Real-World Examples

Hey there! Imagine you’re at a crossroads, trying to decide which path to take. You’ve analyzed the past (descriptive analysis), understood why things happened (diagnostic analysis), and even predicted what might happen next (predictive analysis). But now, you’re faced with the ultimate question: “What should we do?” That’s where Prescriptive Analysis comes in. It’s the final piece of the puzzle, helping you make the best possible decision based on data.

In this blog, we’ll break down what prescriptive analysis is, why it’s important, and how it’s used in real-world scenarios. By the end, you’ll understand how prescriptive analysis can guide you to take the right action at the right time. Let’s dive in!

 

Pescriptive Analysis: Understanding "What Should We Do?" with Real-World Examples

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What Is Prescriptive Analysis?

Prescriptive analysis is the process of using data, algorithms, and models to recommend the best course of action to achieve a desired outcome. It answers the question, “What should we do?” by combining insights from descriptive, diagnostic, and predictive analysis to provide actionable recommendations.

Think of it like a GPS for decision-making. Descriptive analysis tells you where you are, diagnostic analysis explains how you got there, predictive analysis shows you possible routes, and prescriptive analysis recommends the best route to reach your destination.

 

Why Is Prescriptive Analysis Important?

Prescriptive analysis is a game-changer because it helps us:

  1. Make Better Decisions: It provides data-driven recommendations to achieve the best outcomes.
  2. Optimize Resources: It helps allocate resources (time, money, people) more efficiently.
  3. Reduce Risks: It identifies potential risks and suggests ways to mitigate them.
  4. Improve Efficiency: It streamlines processes and eliminates guesswork.

 

Key Components of Prescriptive Analysis

Prescriptive analysis typically involves the following steps:

  1. Define the Objective: Clearly state the goal you want to achieve (e.g., increase sales, reduce costs, and improve customer satisfaction).
  2. Collect Data: Gather relevant data from descriptive, diagnostic, and predictive analysis.
  3. Build Models: Use algorithms and simulations to explore different scenarios and outcomes.
  4. Evaluate Options: Compare the potential outcomes of different actions.
  5. Recommend Actions: Provide actionable recommendations based on the best possible outcome.
  6. Implement and Monitor: Take action and monitor the results to ensure the desired outcome is achieved.

 

Real-World Examples of Prescriptive Analysis

To really understand prescriptive analysis, let’s look at some real-world examples.

 

Example 1: Supply Chain Optimization

A retail company wants to optimize its supply chain to reduce costs and improve delivery times. Here’s how prescriptive analysis can help:

  1. Define the Objective: Reduce supply chain costs while maintaining delivery efficiency.
  2. Collect Data: Gather data on inventory levels, supplier performance, transportation costs, and customer demand.
  3. Build Models: Use optimization algorithms to simulate different supply chain scenarios.
  4. Evaluate Options: Compare the costs and delivery times of different scenarios.
  5. Recommend Actions: The analysis recommends consolidating shipments, switching to a more cost-effective supplier, and adjusting inventory levels.
  6. Implement and Monitor: The Company implements the recommendations and monitors the results, achieving a 15% reduction in costs and faster delivery times.

 

Example 2: Personalized Marketing

An e-commerce company wants to increase sales by targeting customers with personalized offers. Here’s how prescriptive analysis can help:

  1. Define the Objective: Increase sales by 10% through personalized marketing.
  2. Collect Data: Gather data on customer behavior, purchase history, and preferences.
  3. Build Models: Use machine learning algorithms to predict which products each customer is most likely to buy.
  4. Evaluate Options: Compare the potential impact of different marketing strategies (e.g., discounts, bundles, recommendations).
  5. Recommend Actions: The analysis recommends sending personalized product recommendations and offering targeted discounts to high-value customers.
  6. Implement and Monitor: The Company implements the strategy and achieves a 12% increase in sales.

 

Example 3: Healthcare Treatment Plans

A hospital wants to improve patient outcomes by recommending personalized treatment plans. Here’s how prescriptive analysis can help:

  1. Define the Objective: Improve patient recovery rates by 20%.
  2. Collect Data: Gather data on patient medical history, treatment outcomes, and lifestyle factors.
  3. Build Models: Use predictive models to identify the most effective treatments for different patient profiles.
  4. Evaluate Options: Compare the success rates of different treatment options.
  5. Recommend Actions: The analysis recommends a combination of medication, physical therapy, and lifestyle changes for each patient.
  6. Implement and Monitor: The hospital implements the recommendations and sees a 25% improvement in recovery rates.

 

Example 4: Financial Portfolio Optimization

An investment firm wants to maximize returns for its clients while minimizing risk. Here’s how prescriptive analysis can help:

  1. Define the Objective: Maximize portfolio returns while keeping risk below a certain threshold.
  2. Collect Data: Gather data on stock performance, market trends, and economic indicators.
  3. Build Models: Use optimization algorithms to simulate different portfolio compositions.
  4. Evaluate Options: Compare the potential returns and risks of different portfolios.
  5. Recommend Actions: The analysis recommends a diversified portfolio with a mix of high-growth and low-risk investments.
  6. Implement and Monitor: The firm implements the portfolio and achieves a 10% return with minimal risk.

 

How to Perform Prescriptive Analysis

Now that you’ve seen some examples, let’s talk about how to perform prescriptive analysis. Here’s a step-by-step guide:

  1. Define the Problem: Clearly state the goal you want to achieve.
  2. Collect Data: Gather relevant data from descriptive, diagnostic, and predictive analysis.
  3. Build Models: Use algorithms, simulations, or optimization techniques to explore different scenarios.
  4. Evaluate Options: Compare the potential outcomes of different actions.
  5. Recommend Actions: Provide actionable recommendations based on the best possible outcome.
  6. Implement and Monitor: Take action and monitor the results to ensure the desired outcome is achieved.

 

Tools for Prescriptive Analysis

There are many tools available to help you perform prescriptive analysis. Here are a few popular ones:

  1. Python: A programming language with libraries like SciPy and PuLP for optimization and modeling.
  2. R: A statistical programming language with packages like lpSolve for linear programming.
  3. Excel Solver: A built-in tool for optimization problems in Excel.
  4. IBM Decision Optimization: A powerful tool for prescriptive analytics and optimization.
  5. Tableau: A data visualization tool that supports prescriptive analytics.

 

When to Use Prescriptive Analysis

Prescriptive analysis is useful in a variety of situations, including:

  • Business Strategy: Deciding the best course of action to achieve business goals.
  • Resource Allocation: Optimizing the use of resources like time, money, and people.
  • Risk Management: Identifying and mitigating potential risks.
  • Healthcare: Recommending personalized treatment plans for patients.
  • Finance: Optimizing investment portfolios or pricing strategies.

 

Limitations of Prescriptive Analysis

While prescriptive analysis is incredibly powerful, it has its limitations:

  1. Data Quality: The accuracy of recommendations depends on the quality of the data.
  2. Complexity: Some models can be difficult to build and interpret.
  3. Assumptions: Prescriptive models are based on assumptions that may not always hold true.
  4. Implementation Challenges: Even the best recommendations are useless if they’re not implemented effectively.

 

Final Thoughts

Prescriptive analysis is the ultimate tool for decision-making. It takes the insights from descriptive, diagnostic, and predictive analysis and turns them into actionable recommendations. Whether you’re optimizing a supply chain, personalizing marketing campaigns, or improving patient outcomes, prescriptive analysis helps you make the best possible decisions.

So, the next time you’re faced with a tough decision, remember: prescriptive analysis can help you answer the question, “What should we do?” And with that knowledge, you’re one step closer to achieving your goals.

 

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