analytics

What is prescriptive analytics? Everything you need to know

Businesses today realize the value of data. Most have even taken initiatives to revamp their data infrastructure to ensure that data remains accessible and everyone can make data-driven decisions. Yet, research by Accenture shows that only one in five companies are unlocking the intrinsic value of their data.

Here’s the hard truth: Just knowing numbers is not enough for decision-making. You need real-time, actionable insights to drive business outcomes. 

And that’s where prescriptive analytics comes in. Utilizing advanced data analytics models and machine learning algorithms, prescriptive analytics provides actionable insights and recommendations so you can choose the best course of action for your business strategy. Let’s dig a little deeper. 

What is prescriptive analytics?

Prescriptive analytics is an advanced data analytics model that dives deep into data and offers recommendations and insights on ‘what should you do?’ to achieve desired outcomes. Unlike descriptive analytics which summarizes historical data and predictive analytics which forecasts future trends, prescriptive analytics goes one step further by suggesting different action plans and showing the implications of each action.

Types of analytics

Predictive vs. prescriptive analytics

Predictive analytics enables us to anticipate future outcomes in customer behavior, leading to insights such as:

  • Which customers are at the highest risk of churning next month?

  • Which customers are likely to switch from one product or service to another?

  • Forecasting next quarter's purchasing patterns for each customer.

In this approach, data scientists utilize historical customer data to train models that can predict future behaviors using advanced computational methods with machine learning, like Facebook’s Prophet model or ARIMA model. These models harness thousands of data points from customer interactions and purchase histories, identifying trends across the customer base. Predictive analytics can generate risk scores for customer churn, identifying those who might need more engagement or targeted offers.

However, predictive analytics alone doesn’t provide solutions for preventing undesirable outcomes like customer churn. Prescriptive analytics steps in to guide us on the actions needed to alter these predictions. For example,

  • Offering personalized discounts or loyalty rewards might prevent a high-risk customer from churning.

  • Tailored marketing campaigns could influence customers to consider switching products or services.

  • Customized product recommendations or services could improve purchasing patterns for customers with low engagement.

Top benefits of prescriptive analytics

For business leaders, it’s tough to know if you’re making the right decisions. The looming economic downturn, high customer expectations, organizational silos, and fierce competition are all factors that can make strategic decision-making a tiresome, lengthy process. 

But it doesn’t have to be. Here’s how prescriptive analytics simplifies strategic decision-making and drives business value: 

1. Real-time insight generation

Picture this: You are running a retail store with an online presence and want to create personalized offers for your customers. However, you seek guidance on ways to achieve this goal.

Predictive analytics will forecast future trends based on historical data. This analysis anticipates customer preferences based on past behaviors, helping you create effective promotions for each customer segment. However, predictive analytics does not provide the necessary details for making an informed, in-the-moment decision.

On the flip side, prescriptive analytics factors in real-time market data, customer data, and ongoing interactions to deliver specific recommendations with projected outcomes, such as recommending you offer loyalty programs and personalized discounts based on individual profiles. This approach provides actionable guidance.  

The same goes for other complex business problems. Since prescriptive analytics leverages machine learning and advanced algorithms to analyze large volumes of data, it identifies hidden data patterns, correlations, and potential outcomes. The result is the delivery of real-time insights with a detailed action plan, empowering you to make faster decisions. 

2. Simplifying complex data

Data exists to help business leaders make informed decisions. But as data volumes explode, decision-makers grapple with millions of variables and constraints, making it practically impossible to extract valuable insights. 

To succeed, leaders are moving away from static dashboards and turning to advanced BI tools to reduce time-to-insight. You can combine data from multiple sources, stimulate dynamic scenarios, and generate interactive visualizations. This helps you understand data correlations, gain actionable insights, and make informed decisions. Another key benefit is that such advanced features frees your data team from spending too much on sourcing the data. Instead, they can focus more on strategic initiatives. 

3. Getting a complete picture

Running a successful business demands more than sticking to a single option. It involves assessing various choices, weighing their pros and cons, and ultimately selecting the one that aligns with your goals. But this process is time-consuming. 

That’s where prescriptive analytics can help. It incorporates real-time data, simulates scenarios, and makes objective recommendations to help you gain greater context. By allowing you to see not only what's likely to happen but also which factors will drive that outcome, prescriptive analytics empowers you to turn insights into actions.

Prescriptive analytics examples

Let’s discover how prescriptive analytics and AI-powered insights are driving data-driven decisions across different industries with real-world examples:

1. Finance services

Financial institutions are constantly challenged to develop innovative solutions for their customers. Through prescriptive analytics, you gain AI-assisted insights and recommendations into market trends and customer data, helping you identify unmet customer needs and make strategic decisions. 

2. Healthcare

Prescriptive analytics enables healthcare providers to improve patient outcomes and reduce costs. By analyzing the recommendations that the algorithm offers, you can compare different treatment options, identify cost-saving opportunities, and provide personalized patient treatments. Healthcare leaders can also leverage AI-assisted insights to find areas of improvement and increase efficiency across their healthcare institutions. 

3. Marketing

Prescriptive analytics can help marketing agencies and teams analyze the performance of their campaigns and find hidden patterns in customer behavior. Armed with this information, you gain a holistic picture of your target audience, helping you decide which strategies are effective. 

4. Manufacturing

Manufacturers lose millions due to inaccurate sales forecasts, unexpected machine breakdowns, and supply chain issues. To address these issues, you need to understand the root causes of these problems and deploy a strategy that prevents downtime. By harnessing the power of prescriptive analytics, you can glean insights into product movement, discern production needs, and gauge the market's pulse to optimize your operations. 

5. Banking

Most banking institutions use prescriptive analytics for fraud detection and predicting potential risks. The model considers various factors, like changes in user behavior, transaction volumes, and emerging fraud patterns. Based on this information, the model simulates different scenarios and assesses their potential impact on your business. 

How does prescriptive analytics work?

Prescriptive analytics uses machine learning land AI algorithms to analyze large datasets and generate actionable outcomes that help you decide the best course of action for your business goal. While this may sound simple in theory, it is complex to deploy the model without the right tools in place. 

  1. Define the problem: The first step is identifying the problem you want the model to address. It also includes defining the decision variables, constraints, and other relevant factors required to generate an actionable output. 

  2. Gather the data you need: To ensure that your model generates accurate results, the data must remain clean and relevant. To do this, you must remove data with missing values, include external information, and label the datasets clearly. 

  3. Develop the model: Next, we will develop the model and input the information we’ve collected so far. Developing the model requires coding and analytical expertise. It is also critical to integrate machine m learning algorithms, especially for complex and dynamic problems.

  4. Testing and training: After development, data duplication and inconsistency are common performance issues that may occur. During such events, it is critical to tweak the model and adjust parameters to optimize its performance. 

  5. Deploy the model: Once you are done with testing and are confident, you can deploy it in your operating environment. Also, make sure to integrate the model within existing systems.

  6. Map the model outcomes: After deployment, it is critical to create a strategic mapping process to ensure that the model outcomes align with your business objectives. By doing so, you can leverage prescriptive analytics to not only identify the optimal course of action but also ensure a direct link between the model's insights and the desired impact on the business.

  7. Adjust and monitor: After successful deployment, you should continuously monitor the accuracy of the model for improved results.  Collect feedback about its performance and use this information to update and improve it over time.  

Build a competitive advantage with ThoughtSpot

While data plays a crucial role in decision-making, the choice of the right analytics software is equally important. ThoughtSpot’s AI-Powered Analytics puts users in the driver’s seat with a search-based algorithm that allows you to ask questions in natural language, explore hidden insights, and also model data to predict future outcomes. 

With ThoughtSpot, Austin Capital was able to democratize data, empowering everyone to gain real-time insights. The result is a staggering 15% improvement in customer retention with a 30% boost in profit margin. Here’s what Ian’s team from Austin Capital has to say about ThoughtSpot Sage and its ability to help teams find easy-to-understand insights: 

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