What is Prescriptive Analytics?

Can you predict the future? 

Most people can’t, but with enough data, you can make fairly accurate predictions about what will happen. Prescriptive analytics is a part of data analytics that lets you learn about future opportunities, risks, and outcomes to determine the best course of action for your business.

But what does that mean in real life? Let’s define and explore prescriptive analytics and how it can be used for decision-making in various industries.

What is prescriptive analytics?

Prescriptive analytics is a form of business analytics that suggests options on how to take advantage of future opportunities or risks. Information from descriptive analytics and prescriptive analytics suggests the best course of action for the future.

It is considered the third or final phase of business analytics. Before you can use prescriptive analytics for making smarter business decisions, two other types of data analytics need to happen first:

Descriptive analytics: looking at past performance to look for the reasons behind your previous results. For example, you can use your analytics software to determine why a certain campaign was a success while something else was a flop.

Predictive analytics: analytics solutions now take the data from the previous step to predict what is likely to happen. Past data is combined with artificial intelligence algorithms, predictive models and rules, and sometimes external business intelligence data to predict likely future scenarios.

PS. We have some great predictive analytics examples in another blog.

Prescriptive analysis is the final and most actionable step. Instead of merely predicting what (may) happen, it gives you suggestions on how you can achieve the best results.

How prescriptive analytics works

Prescriptive analytics uses data sets, machine learning algorithms, and advanced computational models to recommend the best decisions and moves to make for future business outcomes.

You don’t need a data science degree to understand how prescriptive analytics works. It includes the following steps:

1. Data collection: from relevant sources such as historical data from business and analytics tools, real-time data feeds, customer interactions, or external data such as market trends or economic indicators.
2. Data processing and analysis: the data is now cleaned, processed, and analyzed using predictive algorithms or models. This can include machine learning, statistical models or optimization techniques.
3. Scenario generation: your selected tool predicts possible future outcomes and scenarios, and evaluates them to understand their implications. E.g., it could play out different scenarios for testing out product pricing.
4. Optimization algorithms: these assess multiple options and constraints (such as time or budget) to recommend the best possible action to achieve your desired outcome in real-world scenarios.
5. Recommendation output: the system or tool generates specific recommendations on what (not) to do to achieve your business goals, e.g. lowering churn.
6. Feedback loop: over time, you can refine prescriptive analytics by feeding it new data for improvedaccuracy of the process.

The benefits of prescriptive analytics in a modern business

Forecasting the future and getting actionable insights sounds like a winning combination for any business. Let’s explore what you get with this type of business analytics.

Informed decision-making: beyond predicting possible outcomes about future events, prescriptive analytics gives you actionable insights and recommendations on what not to do to achieve the desired results.

Optimization of operations: by feeding data into prescriptive analytics engines, businesses can optimize processes such as supply chain management, resource allocation or workforce planning.

Increased profitability: prescriptive analytics can highlight opportunities for cutting costs and making better decisions for generating revenue, as well as helping with fraud detection.

Risk management: this type of analytics helps you make data-driven decisions by recommending strategies to cut or minimize potential losses.

Personalized customer experiences: based on information such as customer behavior and in-app analytics, you can create a more effective sales and marketing collateral.

Competitive advantage: companies that use prescriptive analytics adapt to changing market conditions more easily, and they can optimize strategies in real-time.

Examples of prescriptive analytics you can learn from

Not everyone is a data scientistso, perhaps, it would be easier to understand prescriptive analytics through examples and use cases. Here are some potential ways you can use this strategy.

Supply chain optimization in retail

You run a large retail store chain and want to use data to have the right inventory levels at all times. In other words – to avoid stockouts and excess inventory. 

Prescriptive analytics can be used to analyze sales trends, supplier performance, and lead times to recommend optimal inventory levels for each of the stores you manage. It can also give suggestions for changes for things such as delivery schedules or inventory stocking.

Personalized marketing campaigns for an e-commerce store

An ecommerce platform gets hundreds of thousands of visits and wants to use machine learning models to determine how to generate more sales and increase customer lifetime value.

With prescriptive analytics, you can gather and analyze a large number of data points: customer purchase history, browsing behavior, demographic data, and more. Based on these data points, prescriptive analytics suggests the optimal marketing strategies for your most important target audience.

Healthcare treatment plans

A private clinic needs to determine the best treatment plans for their patients based on medical history, symptoms, and currently availabe treatments.

Prescriptive analytics takes into consideration the available patient data, compares it with previous patient outcomes for patients with similar cases, and suggests the most effective options for treatment.

Dynamic pricing in airlines

If you’ve noticed that the same plane ticket is more expensive if you check it a few days later, this is not an accident. It’s based on mathematical models and prescriptive analytics. 

Airline companies consider data points such as booking trends, seasonality, competition, and fuel costs. Based on these data points, they get information about possible future trends so they can price tickets to remain competitive or to make maximum profit at the same time.

Portfolio management in financial services

A financial advisor wants to find out the best ways to invest a client’s portfolio, so that they balance risk and return. They don’t need to be a big data expert, since they can trust prescriptive analytics.

With these models, financial experts can analyze market conditions, historical investment performance and the individual goals of their clients. This gives them the optimal allocation suggestions for their clients’ resources, taking into account the client’s goals, their risk tolerance, and economic forecasts.

Route optimization for logistics

A logistics company needs to find out the optimal route for their drivers to save time and cut fuel costs.

Prescriptive analytics factors in real-time traffic data, fuel prices, vehicle capacity, and more to suggest the best routes to save time for drivers and ensure high customer satisfaction of end users.

Energy use optimization for smart grids

A utility company wants to maximize their efficiency and energy distribution across a city, all the while reducing peak load providing reliability.

Prescriptive analytics lets you factor in for energy consumption patterns, weather forecasts, energy storage levels, and more. All of this lets the company find the best strategies for energy allocation.

Conclusion

Using prescriptive models to improve your decision-making processes does not have to be super complex. With the right tool by your side, you can do data analysis, and visualization and prescribe actions for future outcomes. At Luzmo, we do just that for software companies.

We give you and your end users tools for analyzing and visualizing data so you can make better business decisions in real time. With embedded analytics and AI features, the future of your software is in safe hands with Luzmo.

Book a free demo with our team so we can show you around the platform today.