Predictive Analytics and Prescriptive Analytics: Using Technology to Make Smart Decisions
Posted: Sun Dec 22, 2024 6:25 am
The use of data has become essential when it comes to planning, creating campaigns and optimizing processes in various sectors, especially in marketing and sales. It is in this context that predictive analytics and prescriptive analytics have gained strength, becoming part of the tools for analyzing and understanding information and results.
To collect and transform data into valuable insights, this technique uses several technologies, such as AI (Artificial Intelligence) and machine learning. By mapping and mining data, these tools are applied to improve decision-making in companies.
Due to the similarity in terms, it is common for predictive and prescriptive analytics to be treated as synonyms, but they play a complementary role in operational processes.
In this text, we will address the similarities and differences between the techniques, highlight the most widely used methods in the job market and show how both analyses improve the performance of your business.
Read also: Data marketing: knowing everything about the data-driven structure
Predictive analytics and prescriptive analytics: what's the difference?
The main question surrounding predictive analytics and prescriptive analytics is what distinguishes the two practices.
The etymology of the words itself helps to answer this question: while predictive analysis has a predictive character, prescriptive analysis is linked to recommendation. To make this clearer, we will delve deeper into the characteristics of both below.
Predictive Analytics
The foundation of predictive analytics is logic, the mathematical formula. Therefore, the model takes into account statistical elements to predict something that was not known until now.
And how does this happen? From the extracted database, predictive analytics will search for patterns of behavior, results and other information, resulting in assertive predictions.
It is worth noting that in this context, predictions do not mean guessing or assuming something, but rather a reliable analysis of the history of data offered so that the technique can predict actions that will be necessary, such as increasing investments or preventing risks.
Prescriptive Analytics
Unlike predictive analytics, prescriptive analytics is a combination of analyses that, at singapore phone number example the end of the process, will recommend the most assertive actions for the organization.
Using predictive analytics tools, prescriptive analytics simulates different possibilities to identify which practice is most suitable for a given decision.
We can say that prescriptive analytics draws on the sources of descriptive analysis, which discusses the present time, and predictive analytics, which offers a vision of the future.
A quick and effective way to differentiate between predictive and prescriptive analytics is as follows:
Predictive analytics answers “what will happen?”
Prescriptive analytics answers “what should be done”
It is for this reason that prescriptive analytics takes into account objectives and constraints to determine the best action to be taken.
Practical examples of predictive analytics
For both models, the basis of the analyses is the data and information already collected and stored. In the case of predictive analytics, one of the most classic cases is the predictions of seasonal marketing campaigns, as they take into account the results obtained in the same period in the previous year.
Predictive analytics are widely used in cybersecurity, especially in cases of fraud prevention and detection of virtual threats . The same is true for machine maintenance and repair, as there is an understanding of the period in which equipment will need repair, due to the machinery's usage cycle.
Practical examples of prescriptive analytics
In turn, prescriptive analytics offers suggestions for improvements to the processes that are being carried out. A great way to see this in action is in customer relationships, where it is possible to recommend suggestions based on personal behavior or past experiences.
Bringing it into everyday life, geolocation and mobility applications and tools are other good examples of prescriptive analytics, as they offer the best route for the user to reach their destination.
Finally, it is possible to use prescriptive analytics in inventory planning, managing the quantity of products and services available, as well as selecting the best supplier network to offer the best support.
Combining the use of predictive and prescriptive analytics
The combination of using predictive analytics and prescriptive analytics can be extremely powerful in helping companies make informed decisions and optimize their processes.
When thinking about product launches, you can use predictive analytics resources to understand current trends and project production demand for the coming months.
As a follow-up action, it is possible to recommend specific actions based on these predictions, applying prescriptive analytics to increase production or control inventory.
Speaking of the customer experience approach, the combined use of predictive and prescriptive analytics is also viable. While the history indicates which services customers are most likely to purchase, the prescriptive part helps to personalize campaign communications according to predicted preferences.
In the new era of information technology, the use of data is essential to building a good strategy. Therefore, the combination of predictive and prescriptive analytics offers a significant competitive advantage for your company, allowing you to make smarter decisions in all aspects of your business.
To collect and transform data into valuable insights, this technique uses several technologies, such as AI (Artificial Intelligence) and machine learning. By mapping and mining data, these tools are applied to improve decision-making in companies.
Due to the similarity in terms, it is common for predictive and prescriptive analytics to be treated as synonyms, but they play a complementary role in operational processes.
In this text, we will address the similarities and differences between the techniques, highlight the most widely used methods in the job market and show how both analyses improve the performance of your business.
Read also: Data marketing: knowing everything about the data-driven structure
Predictive analytics and prescriptive analytics: what's the difference?
The main question surrounding predictive analytics and prescriptive analytics is what distinguishes the two practices.
The etymology of the words itself helps to answer this question: while predictive analysis has a predictive character, prescriptive analysis is linked to recommendation. To make this clearer, we will delve deeper into the characteristics of both below.
Predictive Analytics
The foundation of predictive analytics is logic, the mathematical formula. Therefore, the model takes into account statistical elements to predict something that was not known until now.
And how does this happen? From the extracted database, predictive analytics will search for patterns of behavior, results and other information, resulting in assertive predictions.
It is worth noting that in this context, predictions do not mean guessing or assuming something, but rather a reliable analysis of the history of data offered so that the technique can predict actions that will be necessary, such as increasing investments or preventing risks.
Prescriptive Analytics
Unlike predictive analytics, prescriptive analytics is a combination of analyses that, at singapore phone number example the end of the process, will recommend the most assertive actions for the organization.
Using predictive analytics tools, prescriptive analytics simulates different possibilities to identify which practice is most suitable for a given decision.
We can say that prescriptive analytics draws on the sources of descriptive analysis, which discusses the present time, and predictive analytics, which offers a vision of the future.
A quick and effective way to differentiate between predictive and prescriptive analytics is as follows:
Predictive analytics answers “what will happen?”
Prescriptive analytics answers “what should be done”
It is for this reason that prescriptive analytics takes into account objectives and constraints to determine the best action to be taken.
Practical examples of predictive analytics
For both models, the basis of the analyses is the data and information already collected and stored. In the case of predictive analytics, one of the most classic cases is the predictions of seasonal marketing campaigns, as they take into account the results obtained in the same period in the previous year.
Predictive analytics are widely used in cybersecurity, especially in cases of fraud prevention and detection of virtual threats . The same is true for machine maintenance and repair, as there is an understanding of the period in which equipment will need repair, due to the machinery's usage cycle.
Practical examples of prescriptive analytics
In turn, prescriptive analytics offers suggestions for improvements to the processes that are being carried out. A great way to see this in action is in customer relationships, where it is possible to recommend suggestions based on personal behavior or past experiences.
Bringing it into everyday life, geolocation and mobility applications and tools are other good examples of prescriptive analytics, as they offer the best route for the user to reach their destination.
Finally, it is possible to use prescriptive analytics in inventory planning, managing the quantity of products and services available, as well as selecting the best supplier network to offer the best support.
Combining the use of predictive and prescriptive analytics
The combination of using predictive analytics and prescriptive analytics can be extremely powerful in helping companies make informed decisions and optimize their processes.
When thinking about product launches, you can use predictive analytics resources to understand current trends and project production demand for the coming months.
As a follow-up action, it is possible to recommend specific actions based on these predictions, applying prescriptive analytics to increase production or control inventory.
Speaking of the customer experience approach, the combined use of predictive and prescriptive analytics is also viable. While the history indicates which services customers are most likely to purchase, the prescriptive part helps to personalize campaign communications according to predicted preferences.
In the new era of information technology, the use of data is essential to building a good strategy. Therefore, the combination of predictive and prescriptive analytics offers a significant competitive advantage for your company, allowing you to make smarter decisions in all aspects of your business.