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.
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.
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Today, companies are looking for better ways to use data to boost customer experiences, improve their products, and cut costs. While data analytics helps turn raw data into useful insights, choosing the right type of analytics makes sure those insights have the greatest impact.
Consider this scenario–You manage a retail company that wants to reduce inventory costs while meeting customer demand more efficiently. To make data-driven decisions, you can leverage various types of analytics in the following way:
Types of data analytics
Descriptive analytics will help you understand: What are the current inventory levels, and how have past inventory levels fluctuated based on customer demand?
You’ll get a detailed snapshot showing that, over the last quarter, there were frequent stockouts of certain popular items, while other items sat in excess for weeks.
Diagnostic analytics will help you understand: Why did certain items stock out while others remained overstocked?
It might reveal that stockouts were caused by supply chain delays, and overstocking occurred due to inaccurate demand forecasts or changes in customer preferences.
Predictive analytics will help you understand: What trends can we expect in future inventory demand?
Based on historical sales data, predictive models might suggest that customer demand for certain items will surge during the holiday season, while others will see a decline.
Prescriptive analytics will help you understand: What specific actions should we take to optimize inventory levels and reduce costs?
Prescriptive analytics could recommend adjusting order quantities for high-demand items ahead of the holiday season and offering promotions to move slow-moving inventory. It might also suggest refining supplier relationships to avoid future stockouts.
By leveraging these different types of analytics, your retail company can not only understand past performance and current trends but also take proactive steps to streamline inventory management, reduce costs, and ultimately boost profitability.
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.
This type of data analytics utilizes 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.
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.
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.
For example, Loan Market Group leveraged ThoughtSpot Everywhere to empower brokers with real-time insights and interactive reports. By integrating ThoughtSpot with Snowflake and dbt, they delivered a revamped reporting experience to over 4,000 users in under three months.
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.
MDaudit used ThoughtSpot to transform healthcare compliance and revenue integrity. This empowered 1,000+ users to self-serve insights, resulting in 25%+ business growth in 2023. Healthcare providers optimized audits, streamlined revenue, and made decisions with ease, improving both outcomes and efficiency.
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.
For instance, Frontify utilizes ThoughtSpot to gain actionable insights into their marketing data, enabling them to optimize lead generation and resource allocation. In just five months, 75 users gained instant insights, transforming marketing strategies and boosting ROI.
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.
Fabuwood struggled with outdated BI tools and slow reporting. By integrating ThoughtSpot, they gained real-time insights into sales and manufacturing workflows. The solution provided actionable recommendations for optimizing production and supply chain operations, leading to a 300% increase in query efficiency and enhanced decision-making across the organization.
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.
By adopting ThoughtSpot and Snowflake, Northmill now uses advanced analytics to improve customer conversion rates by identifying drop-off points and implementing targeted strategies. This shift from a slow legacy BI tool to a robust, self-service platform has not only streamlined report generation but also empowered staff across departments to make data-driven decisions swiftly.
“What moves the needle is turning insight into actions. To run a business, the ability to produce nice graphs and monitor interesting data is not even half the story—it's what you do with it that's important.”
Tobias Ritzén
CFO
Northmill Bank AB, Stockholm
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.
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.
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.
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.
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.
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.
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.
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.
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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