What Is Prescriptive Analytics and Why Is It Important: 5 Top Use Cases​

What Is Prescriptive Analytics and Why Is It Important: 5 Top Use Cases

For decades, we’ve been using data and analytics to describe what happened. In the past few years, we’ve started using data analytics to not just describe but also diagnose and predict. This has led to the development of 4 major categories of data analytics –

  • Descriptive analytics – Trends shown by the data
  • Diagnostic analytics – Factors contributing to those trends
  • Predictive analytics – Whether the trends will continue or recur
  • Prescriptive analytics – Determining the optimal course of action

All four types are often used in tandem to dig deeper into a problem and create a full picture of the story that data tells.

In this article, we are going to specifically talk about prescriptive analytics and its major use cases. So, let’s get started.

What is prescriptive analytics?

Recent advancements in automation and artificial intelligence (AI) have made it possible to dynamically change the way things are done. From huge, heterogeneous data sources, extract insights – not just reporting – and tailor them for each user. These insights, crucially, go beyond reporting on the present and forecasting the future to suggesting actions to accomplish the desired goals. This is where prescriptive analytics comes into action.

Prescriptive analytics is the process of using data to determine the optimal course of action. This form of analysis generates recommendations for the next steps by taking into account all relevant elements. Prescriptive analytics, as a result, is a powerful tool for making data-driven judgments. 

It is crucial to note that algorithms can make data-driven recommendations but they can’t take the place of human judgment. Prescriptive analytics should be considered a tool for informing decisions and plans. Your input is vital and necessary for providing context and protections for automated results.

Prescriptive analytics can be used to do manual analyses, construct proprietary algorithms, or employ third-party analytics products with built-in algorithms.

How does it work?

Prescriptive analytics is based on AI, primarily machine learning (ML), which entails algorithms and models that allow computers to make decisions based on statistical data patterns and correlations.

5 top use cases of prescriptive analytics

5 top use cases of prescriptive analytics​

Prescriptive analysis has its application across varied industries. To look further into this, here are the top 5 sectors that it shines in –

  1. Sales and marketing – Marketing and sales firms have access to a wealth of client data that can aid in the development of effective marketing tactics, such as determining which items go well together and how to price them. Prescriptive analytics enables marketers and salespeople to be more accurate with their campaigns and client outreach because they are no longer limited by their intuition and expertise.

  2. Financial sector – Statistical modeling is used by quantitative researchers and traders to strive to maximize returns. Similar strategies can be used by financial institutions to control risk and profitability. Financial institutions, for example, can create algorithms that sift through previous trading data to assess trade risks. The ensuing insights can assist them in determining how to size positions, hedge them, and even whether to trade at all. These companies can also employ models to cut transaction costs by determining how and when to place their trades.

  3. Transport – In the package delivery and transportation industry, cost-effective delivery is critical to success and profitability. Reduced energy usage and logistical difficulties such as erroneous delivery destinations can save time and money by improving route planning and resolving logistical concerns. Shippers generate enormous amounts of data. Instead of hiring armies of analysts and dispatchers to find out how to run their business, these organizations can automate and build prescriptive models that provide recommendations.

  4. Social media content – Businesses’ algorithms collect information depending on your interactions with their platforms. An algorithm’s publication of a specific recommendation can be triggered by a combination of your previous behavior. If you watch shoe review videos on YouTube regularly, the platform’s algorithm is likely to evaluate that data and propose that you watch more of the same type of video or comparable material.

  5. Product development – Product development and enhancement can also be aided by predictive analytics. Surveying consumers, doing tests with beta versions of a product, conducting market research with people who aren’t current product users, and gathering behavioral data as existing users engage are all ways that product managers might obtain user data. All of this data may be evaluated, either manually or algorithmically, to spot trends, figure out what’s causing them, and predict if they’ll happen again. Prescriptive analytics can assist in determining which features to add or exclude from a product, as well as what has to be changed to provide the best possible user experience.


Beyond these 5 industries, prescriptive analytics can be a useful tool for optimizing operations, growing sales, and managing risk. But to use it effectively, the AI models and algorithms need a strong data pipeline that can ensure the data being fed into the models is accurate and clean.

As a result, prescriptive analytics is a significant step forward in terms of technology and human intelligence.

If you liked reading this piece, you can find more such articles on the Dresma blog.

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