Predictive vs. Prescriptive Analytics- When to Use Them
Without predictive and prescriptive analytics, businesses are left manually estimating future risks and trends to develop risk management and process enhancing plans. These forecasting models take the guesswork out of developing impactful marketing strategies, customer experience, and preventative measures by using business data.
However, these systems have different goals and features that make each method optimal for specific processes. Therefore, companies should understand the differences between predictive and prescriptive analytics to determine which model best suits their needs.
What is Predictive Analytics?
Predictive analytics uses statistics and sophisticated modeling to detect potential issues before they occur. By further enhancing projections through software integration, predictive models cross-examine historical data with real-time metrics from each supply chain system.
This establishes a relationship between data sets, which streamlines information tracking. With customized algorithms, the solution alerts users when incoming metrics do not align with previous patterns, suggesting component failures or discrepancies. Companies can then anticipate future problems, allowing management to conduct routine maintenance and take prevention measures.
For example, a company can integrate its point-of-sale (POS), inventory control, and replenishment systems with predictive analytics software to profile relationships between sales and inventory. This data analytics enables predictive software to recognize when inventory levels do not accurately reflect sales, alerting management of erroneous data entry or purchase orders.
Although predictive analysis does not pinpoint future events with certainty, the solution outlines detailed reports on each estimated activity to help management better access the risks and develop actionable insights.
A predictive analytics solution can also forecast trends in demand, sales, traffic, and revenue by monitoring customer behavior and purchases. Management can then prepare stock levels and staff to optimize sales and customer service to boost revenue. This function gives businesses a significant competitive edge over companies that are unable to anticipate demand fluctuations.
What is Prescriptive Analytics?
Prescriptive analytics seeks to provide advice to mitigate future business risks and problems. By gathering historical, real-time, and big data, this analytics model collects several inputs to identify and define trends, errors, and risks. When potential issues are detected, businesses can use prescriptive analytics to determine all available plans of action.
Prescriptive software uses machine learning and forecasting techniques to assess each risk and outline several solutions. Sophisticated systems can then determine what impact each plan of action would yield on future outcomes.
This function allows management to understand the effects of each action before execution, promoting data-driven decision making. Without this feature, companies would have no other option than to perform trial-and-error solutions until they determine the most effective option.
The advanced functionality of prescriptive analytics also enhances data management by processing multiple forms of data as they become available. The system can handle structured, unstructured, qualitative, and quantitative information to create a well-rounded diagnostic and proposal. By continuously integrating new data, businesses can ensure the plans are derived from accurate, relevant information.
Besides risk management, prescriptive analytics defines emerging business trends, such as an influx in traffic, allowing management to take advantage of new opportunities. Management can then run analytics on projects, such as new marketing campaigns or brand launches, to define how each plan would perform.
Predictive vs. Prescriptive Analytics - Which is Better?
Although the terms are often used synonymously, predictive and prescriptive analytics have very different capabilities and goals.
Predictive analytics simply indicates the possible risks that may occur but does not provide any advice on solutions. On the other hand, prescriptive analytics is able to project future outcomes and present the best way to approach the situation.
- Models specific business operations
- Forecasts general risks that may occur
- Predicts when future events may take place
- Generates non-actionable insights
- Can indirectly expend one function to optimize another
- Models the whole business
- Completely data-driven
- Proposes recommended solutions
- Considers the effects of each proposition
- Provides actionable insights and metrics
- Runs diagnostics on scenarios
- Considers all data inputs
- Uses real-time information to update projections and proposals
While prescriptive analytics is the more advanced model, Gartner's study disclosed that only 11% of medium to large companies are utilizing the service. However, this is projected to increase to 26% by 2022.
Therefore, businesses should consider their industries and processes to determine if using prescriptive analytics would enhance their performance.
While both analytics models can be utilized in various operations, there are specific processes in which both reach optimal performance.
Predictive analytics is typically used to define short- to medium-term patterns independent of other trends, such as-
- Sales trends of specific items or brands
- Short-term insurance risks
- Customer demand planning
- Inventory management
- Profit margins
- Maintenance requirements
Prescriptive analytics can look at all processes to assess business performance as a whole. So, while this model can measure specific trends, it is also able to enhance more significant functions, including-
- Data extraction over several databases and disparate sources
- Establishing optimal manufacturing and inventory management methods
- Determining the best marketing strategy to develop an impactful campaign
With forecasting software, companies can integrate all established systems to streamline data management, creating a well-rounded perception of business performance and actionable insights. By taking advantage of these services, organizations can make data-driven decisions to improve sales and revenue while mitigating risks.