Prescriptive Analytics

Prescriptive Analytics is a method of analyzing text data to identify patterns and trends. This information can be used to predict future events or outcomes.

Descriptive vs Prescriptive analytics

Descriptive analytics is the process of organizing and analyzing data to describe, summarize, and understand it. This type of analytics can be used to answer questions such as “What happened?” and “How did it happen?” Descriptive analytics does not try to predict future events; rather, it looks at past data to understand what has already happened.

Predictive analytics is the process of using data to make predictions about future events. This type of analytics can be used to answer questions such as “What will happen?” and “When will it happen?” Predictive analytics relies on historical data to build models that identify patterns and trends. These models are then used to make predictions about future events.

So, while descriptive analytics looks at past data to understand what has already happened, predictive analytics uses past data to try and predict what will happen in the future. Prescriptive analytics takes this one step further by not only predicting what will happen but also recommending what should be done to achieve the desired outcome.

Prescriptive analytics is often used in marketing and sales applications. For example, a company might use prescriptive analytics to identify customers who are likely to purchase a product or service. The company could then target these customers with specific marketing campaigns.

Prescriptive analytics can also be used in supply chain management. For example, a company might use prescriptive analytics to predict demand for a product. The company could then use this information to manage inventory levels and production schedules.

In summary, prescriptive analytics is a method of analyzing data to identify patterns and trends.

Example Uses of Prescriptive Analytics

  • predicting future customer behavior
  • identifying at-risk customers
  • recommending marketing campaigns
  • managing inventory levels
  • scheduling production

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