Prescriptive Analytics: Recommending Actions to Optimize Outcomes

While predictive analytics forecasts future events or trends, prescriptive analytics takes it a step further by recommending specific actions to achieve the best possible outcomes. Prescriptive analytics focuses on using the insights from predictive models to guide decision-making, optimize processes, and determine the best course of action for achieving business goals. In this article, we will explore what typically happens during the prescriptive analytics phase, the techniques used to generate actionable recommendations, and how organizations can leverage these insights to enhance performance and drive strategic decisions.

1. The Purpose of Prescriptive Analytics

The primary purpose of prescriptive analytics is to provide organizations with actionable recommendations that can optimize future decisions and outcomes. It helps answer questions like:

• What is the best pricing strategy for maximizing profits?

• Which marketing tactics should be used to retain high-value customers?

• How should inventory be managed to minimize stockouts while reducing holding costs?

• What actions should be taken to improve employee retention based on predicted churn rates?

Unlike descriptive and predictive analytics, which focus on understanding past performance and forecasting future events, prescriptive analytics provides specific guidance on the actions that should be taken based on predicted outcomes. It combines data-driven insights with optimization techniques to recommend the best possible decisions, balancing multiple objectives, constraints, and potential risks.

2. Optimization Models

A key component of prescriptive analytics is the use of optimization models. These mathematical models are designed to find the best solution to a problem by considering various factors and constraints. Optimization models are often used in areas such as resource allocation, logistics, scheduling, and pricing.

Some common optimization techniques include:

• Linear Programming: Linear programming is used when the objective and constraints can be expressed as linear relationships. It helps businesses optimize decisions like production schedules, transportation routes, or staffing levels. For example, a retailer might use linear programming to determine the optimal mix of products to stock, given limited shelf space and varying profit margins.

• Integer Programming: Similar to linear programming, integer programming is used when the decision variables are required to be integers (whole numbers). This is useful in problems like determining how many units of a product to produce, or how many delivery trucks are needed.

• Non-linear Programming: Non-linear programming is used for more complex problems where the relationships between variables are non-linear. This could be applied in cases like pricing optimization, where the relationship between price and demand isn’t linear.

• Goal Programming: Goal programming is an extension of linear programming that is used when multiple, often conflicting, objectives must be considered. For example, a company might want to maximize profits while also minimizing carbon emissions. Goal programming helps balance these competing objectives.

By using optimization models, prescriptive analytics can provide organizations with specific recommendations for how to achieve the best possible outcome, given the available resources and constraints.

3. Simulation Models

Another important tool in prescriptive analytics is simulation modeling. Simulation models are used to replicate real-world systems and processes, allowing organizations to test various scenarios and evaluate the potential outcomes of different actions before making decisions.

Simulation models are particularly useful when:

• The system being modeled is complex and involves uncertainty (e.g., customer demand, market fluctuations, or operational processes).

• The impact of different decisions needs to be tested under various conditions.

For example, a manufacturing company might use a simulation model to test different production schedules and see how they affect costs, delivery times, and customer satisfaction. By running multiple simulations with different inputs (e.g., varying demand levels or labor availability), businesses can understand how different decisions will affect performance and choose the best course of action.

Simulation models are also valuable for assessing risk and uncertainty, as they allow decision-makers to explore a range of possible outcomes based on varying assumptions.

4. Decision Support Systems (DSS)

Prescriptive analytics often involves the use of Decision Support Systems (DSS), which are software tools that help organizations make data-driven decisions. DSS integrate data, analytical models, and decision-making frameworks to provide recommendations on optimal actions.

These systems use a combination of:

• Data-driven insights: Extracted from data sources and predictive models.

• Business rules: Preset guidelines that help ensure that recommendations align with business goals and constraints.

• Optimization and simulation techniques: Used to generate the best possible recommendations and evaluate different scenarios.

For example, a DSS in a retail setting might recommend optimal stock levels for each product based on sales forecasts, seasonal demand, and inventory costs. In a healthcare setting, a DSS could help clinicians determine the most effective treatment plan for a patient based on clinical guidelines, medical history, and predictive models of patient outcomes.

5. Evaluating and Implementing Recommendations

Once prescriptive analytics has generated recommendations, it’s important to evaluate and prioritize them based on the business’s goals and objectives. The evaluation process involves considering factors such as:

• Feasibility: Is the recommended action practical given the resources and constraints?

• Cost-benefit analysis: What are the potential costs and benefits of implementing the recommendation? Will the benefits outweigh the costs?

• Risk assessment: What risks are associated with the recommended action? How might these risks impact the business?

In some cases, businesses may need to refine or adjust the recommendations based on real-world conditions, such as market changes, customer preferences, or supply chain disruptions. The decision-makers must ensure that the recommended actions align with the overall strategy and goals of the organization.

Once the evaluation is complete, the recommendations are implemented. During implementation, businesses should monitor the outcomes closely and adjust the approach as necessary. This process of continuous evaluation ensures that the actions taken are achieving the desired results and helps the organization stay agile in a constantly changing environment.

6. Real-time Decision-Making and Automation

In some cases, prescriptive analytics can be used in real-time decision-making and automation. By integrating prescriptive models into business operations, organizations can make decisions on-the-fly based on the most up-to-date data.

For example, in dynamic pricing models, prescriptive analytics can adjust prices in real-time based on factors such as demand, competition, and customer behavior. In supply chain management, prescriptive analytics can automatically reorder inventory when stock levels fall below a certain threshold, ensuring that products are always available when customers need them.

Real-time decision-making powered by prescriptive analytics enables organizations to be more responsive to changing conditions, improving efficiency and customer satisfaction.

7. Continuous Improvement and Monitoring

Prescriptive analytics is an ongoing process. As businesses implement recommended actions, it’s important to continuously monitor performance and gather feedback. This feedback loop allows organizations to refine their strategies and make adjustments as needed.

By continuously improving and optimizing their decision-making processes, businesses can adapt to changes in the market, better meet customer needs, and stay ahead of competitors.

Conclusion

Prescriptive analytics is a powerful tool for organizations seeking to optimize outcomes and make data-driven decisions. By using optimization models, simulation techniques, decision support systems, and real-time decision-making tools, prescriptive analytics provides actionable recommendations that help businesses achieve their goals and maximize their performance.

While prescriptive analytics doesn’t just predict future events, it guides organizations toward the best actions to take based on those predictions. It turns insights into concrete actions that can drive success. In the next article, we will explore Storytelling in analytics, where we will discuss how to communicate the results of data analysis in a way that is clear, compelling, and impactful for decision-makers.