Let’s see what are the key differences between predictive and prescriptive analytics:
Predictive analytics aim to predict what is going to happen and aren’t valuable unless they are actionable. Predictive analytics use many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current and historical data to make predictions about the future.
Prescriptive analytics take predictive data one step further by helping businesses decide the best course of action based on the generated predictions. They allow companies to assess a number of possible outcomes based upon their actions. They use a combination of techniques such as business rules, algorithms, machine learning and computational modelling procedures, which are applied against input from many different data sets including historical and transactional data, real-time data feeds, and big data.
An example of prescriptive analytics in the financial services is working to decide which services and products to offer to certain customers based on specific actions they’ve taken (i.e., opening of a new account).
And it can do more. In addition to helping banks prepare for customer trends, prescriptive analytics can help management teams gain insights that could help them actually change the expected outcomes by changing their strategy, programs, policies, and practices. This is how data science and business intelligence can provide real value to a bank.
Prescriptive analytics solutions should be used with a recommendation engine that weighs your business needs and enables managers take decisions that improve business outcomes and provide value. For example, prescriptive analytics can tell a company how much they will need to reduce the cost of a product to attract new customers while keeping a profit.
Prescriptive analytics take predictive data one step further by helping businesses decide the best course of action based on the generated predictions. They allow companies to assess a number of possible outcomes based upon their actions. They use a combination of techniques such as business rules, algorithms, machine learning and computational modelling procedures, which are applied against input from many different data sets including historical and transactional data, real-time data feeds, and big data.
An example of prescriptive analytics in the financial services is working to decide which services and products to offer to certain customers based on specific actions they’ve taken (i.e., opening of a new account).
And it can do more. In addition to helping banks prepare for customer trends, prescriptive analytics can help management teams gain insights that could help them actually change the expected outcomes by changing their strategy, programs, policies, and practices. This is how data science and business intelligence can provide real value to a bank.
Prescriptive analytics solutions should be used with a recommendation engine that weighs your business needs and enables managers take decisions that improve business outcomes and provide value. For example, prescriptive analytics can tell a company how much they will need to reduce the cost of a product to attract new customers while keeping a profit.
Drive value by using both approaches
According to Gartner, “Bringing together forecasts (a form of predictive analytics) with optimization (a form of prescriptive analytics) lets an organization explore how changes to different variables are likely to affect the outcomes or alter the relative trade-offs. This combined, composable approach gets to the heart of the task of adding business value.”Tagged under