Types of Business Analytics: Descriptive, Predictive, Prescriptive with Real-Life Examples

The environment in which businesses operate today relies heavily on data, with most businesses generating large amounts of data through their transactions, customer interactions, and digital platforms. The value of this raw data comes from its use in generating insights about a business through the application of business analytics techniques.

Business analytics utilizes a mixture of statistical techniques and analytical models to provide a systematic approach for analyzing and interpreting data. It also helps determine how well an organization is performing and predicts future performance.

In this article, we will look at three types of business analytics methods along with their real-world examples.

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Descriptive Analytics: Understanding What Happened

Descriptive analytics is the most basic form of business analytics. It basically involves summarizing historical data to better understand past performance and identify industry trends. So the main question it answers is: “What happened?”

Descriptive analytics enable organizations to convert their raw data into useful forms of information that can help guide decision-making. For example, some commonly used techniques of data analytics include data aggregation, data visualization, and basic statistical analysis.

For example, a retail company uses descriptive analytics to analyze monthly sales across different branches and regions. By examining sales numbers and product-wise revenue, management can identify which stores performed well. Similarly, financial statements, income statements, and balance sheets are the outputs of performing these analyses. They offer a snapshot of a company’s financial health over a specific period.

A very common example of this is banks. Banks generate daily operational reports with total deposits, withdrawals, loan disbursements, and transaction volumes. These reports help in routine monitoring and ensure that targets are being met. Descriptive analytics does not explain why events occurred or predict future outcomes; however, it provides a strong foundation for more advanced analytical approaches.

Predictive Analytics: Forecasting What Is Likely to Happen

Predictive analytics uses historical data to make informed predictions about future events. It answers questions like: “What is likely to happen next?” This method basically relies on statistical models, machine learning algorithms, regression analysis, and pattern recognition.

By identifying relationships and trends within past data, predictive analytics allows companies to predict future behavior, risks, trends, and opportunities. It is widely used in areas such as demand forecasting, risk assessment, customer behavior analysis, and fraud detection.

A common example can be found in e-commerce and online retail. Using machine learning algorithms, companies such as Amazon evaluate the browsing histories, buying habits, and product preferences of potential buyers to determine which products a buyer is most likely to purchase in the future. This information is then used to create unique advertising campaigns and offer product suggestions with the goal of maximizing customer interaction and sales.

While predictive analytics does provide some future insight, it does not necessarily identify the exact action that needs to be taken. Additionally, the data quality and the quality of underlying predictive models impact the level of success in using predictive analytics.

Prescriptive Analytics: Deciding What Should Be Done

The most sophisticated set of business analytics tools is prescriptive analytics. This set of tools uses predictive data, as well as providing a way to analyze all possible outcomes and recommend the fastest route to reach the desired outcome.

The main question prescriptive analytics will ask you is, "What are my options?" Prescriptive analytics uses predictive analysis, optimization, simulation modeling, and decision analysis to evaluate all of the options available based on projected outcomes. All options will be evaluated, and the best will be determined based on a variety of factors, such as what the business's goals and constraints are and the type of resources that are available to the organization.

A good example of how prescriptive analytics can be found in supply chain management. Companies that manufacture large quantities of products have begun using prescriptive models to determine the most effective means to maintain optimal amounts of inventory, create schedules for when to make the products, and identify optimal routes to transport the finished products. If demand forecasts show increased demand for a specific product, prescriptive analytics can help companies determine whether they should increase production, adjust their warehouse stocks, or change the route that they will deliver products to customers to save money and avoid stockouts.

Airlines optimize pricing of tickets and allocation of seats based on demand, competitor prices, trends, and customer preferences via the use of prescriptive analytics in the Airline Transportation Industry. By using this approach, airlines develop a real-time pricing strategy that maximizes revenue generated from ticket sales. Ride-hailing companies (e.g. Uber) also utilize prescriptive analytics to recommend dynamic pricing and allocate drivers based on demand, traffic conditions, and availability.

While prescriptive analytics offers significant strategic value, it requires advanced tools and skilled professionals. For this reason, it is typically adopted by organizations with mature analytics capabilities.

Comparing the Three Types of Business Analytics

Although descriptive, predictive, and prescriptive analytics serve different purposes and are performed differently, they are not mutually exclusive. Instead, they complement one another and are often used together.

Descriptive analytics is primarily concerned with providing an overview of previous trends and what data the company has collected over time. Through the use of descriptive analytics, organisations are able to identify potential future trends or outcomes based on their previous performance. It then uses this information as input into algorithms that help it predict future customer demand. It can also be used in conjunction with Prescriptive Analytics to provide insight into the best way to price products or market to specific audiences to maximise business operations again.

Feature	Descriptive Analytics	Predictive Analytics	Prescriptive Analytics
Purpose	Summarizes past data	Predicts future outcomes	Suggests the best actions
Purpose	What happened?	What will likely happen?	What should we do?
Time Focus	Past	Future	Future with action
Data Used	Historical data	Historical data and patterns	Data plus predictions
Methods	Reports, dashboards, basic statistics	Statistical models, machine learning	Optimization and simulations
Output	Reports and summaries	Forecasts and risk scores	Action recommendations
Complexity	Low	Medium	High
Example	Monthly sales reports	Demand forecasting	Inventory optimization

Conclusion

Utilizing business analytics can assist your organization in gaining a competitive advantage and increasing revenue through developing insight regarding potential trends in business, and utilizing business analytics to assist the organization in determining the best course of action.

Organizations that successfully employ descriptive, predictive, and prescriptive analysis in their processes tend to improve their overall efficiency and effectiveness.