Quantitative Analysis In Business

Quantitative analysis uses mathematical and statistical techniques to examine variables and understand patterns in numerical data. Business analysts use quantitative and qualitative analysis and data visualization to produce clear, actionable insights that drive informed decisions in areas including market research, financial forecasting, risk management, and operational optimization.¹

Read on to learn more about the benefits, risks, tools, and use cases of quantitative analysis in business.

In business, fortunes and legends are made by people who seize opportunities first. When data was written in ink and stored in paper ledgers, finding these opportunities seemed to be a matter of luck or innate brilliance.

In 1859, Edwin Drake drilled the first oil well in America, igniting the “black gold” craze that drew speculators from all over the country hoping to strike it rich. John D. Rockefeller recognized that the real opportunity was refining oil, not drilling for it, and launched Standard Oil, a company that would eventually become the largest and most profitable organization in the world.² Data is such a powerful driver of business intelligence that it’s been described as the “new oil.”³

Now that we create over 328 million terabytes of data daily and it’s readily available in the cloud, you don’t have to be lucky or brilliant to find and create business opportunities.⁴ However, as Rockefeller would surely appreciate, the true value of data can’t be realized until it’s refined through statistical models. Quantitative data analysis refines raw numbers and provides a number of benefits to business leaders, such as the following.

When businesses face choices, whether about marketing campaigns, investment opportunities, or how much inventory to carry, they often rely on hard numbers to guide them. By converting raw numerical values into meaningful metrics, quantitative analysis helps eliminate guesswork and subjectivity. For example, a company might use regression analysis to understand how different pricing strategies could affect sales. These data-driven insights can help leaders select a pricing model that maximizes revenue while maintaining customer satisfaction.⁵

Risk is an inherent part of business, whether related to market fluctuations, consumer behavior, or operational setbacks. Quantitative analysis offers a structured way to identify and assess these risks.⁶ A quantitative method such as the Monte Carlo simulation can model various scenarios, predicting potential outcomes and allowing the business owner or management team to develop comprehensive risk-mitigation strategies.⁷

Data analytics can streamline multiple aspects of business performance, from inventory management to employee performance and beyond. For example, airlines use quantitative models to analyze flight data and improve aircraft maintenance.⁸

Different types of quantitative business analysis are suited to answering different questions. The following are some of the most commonly used quantitative methods and models.

Descriptive statistics summarize and simplify large datasets into easily interpretable figures like mean, median, and standard deviation. In a business context, these statistics can provide an initial snapshot of a particular aspect of operations, such as sales performance or customer demographics. Descriptive statistics analyze what has already happened and are often the basis of other types of business analysis, such as predictive models.⁹

While descriptive statistics summarize what has already happened, inferential statistics allow you to make predictions or inferences about a larger population based on a sample. Techniques like hypothesis testing, chi-square tests, or t-tests provide ways to evaluate relationships between variables, test assumptions, and estimate parameters.¹⁰ Businesses often use inferential statistics to generalize consumer preferences, drive product development, or ensure product quality.¹¹

Predictive modeling uses historical data to forecast future events. Methods like linear regression, time series analysis, and machine learning algorithms fit this category. The purpose is to identify trends or patterns to inform future business decisions.¹² Streaming services use forecasting models to recommend content to users, while retailers may use them to identify buying habits based on past purchases. Predictive modeling can give businesses a competitive advantage by acting as a real-life, data-based crystal ball.¹³

Optimization models focus on finding the most effective ways to allocate resources or configure processes to achieve specific objectives. These models are often complex, incorporating advanced mathematical techniques like linear programming or integer programming.¹⁴ Businesses use optimization models for various purposes, such as maximizing profits, minimizing costs, or optimizing supply chain logistics. For instance, a manufacturing company might employ an optimization model to determine the most cost-effective distribution of resources across its factories, considering variables like transportation costs, labor, and materials.¹⁵

Depending on the use case, the business advantages created by quantitative analysis can be significantly enhanced by combining quantitative techniques with qualitative techniques. While quantitative analysis is based on numerical data and helps to answer questions about how volume, frequency, or size, qualitative analysis can provide insights drawn from non-numerical, and often unstructured, data about why or how something happens.

Learn the skills and tools you need to become a leader in business analysis with an Online MS in Business Analytics from Seattle University’s Albers School of Business and Economics. With a solid grounding in statistical modeling techniques, you’ll learn to use the power of big data to drive effective business strategies. Our program stands out from others by preparing you to contextualize data and communicate effectively with stakeholders and managers.

Contact an admissions outreach advisor today to learn more.