Predictive Analytics in Business: Forecasting, Risk Management and Strategy

Predictive analytics allows business leaders to identify likely outcomes based on historical data and statistical models. Armed with these tools, businesses can implement a data-driven business strategy in areas such as market research, product development and marketing.

This post will explore the skills you can develop with a predictive analytics MSBA, including business forecasting, risk management analytics and strategic decision-making analytics.

Predictive analytics examines data to gain insights into future trends, challenges, and events.¹ In the business world, this process shapes strategies and helps organizations gain a competitive edge.

One of the main reasons small businesses fail is a lack of planning.² It’s crucial to have detailed plans in place for each department–that way, new business owners can prepare for and manage challenges. Predictive analytics allow business owners to make informed decisions based on historical data, current trends and future projections. Let’s examine these uses in greater detail.

One of the most useful applications of predictive analytics in business is forecasting future trends. Future trend forecasting can be used across industries to make better decisions and improve efficiency.³

Analysts use statistical algorithms and machine learning to uncover the trends hidden within complex datasets and use them to predict future customer behavior and potential events. These future trends can relate to almost any aspect of business performance, from marketing campaigns to supply chain management.³

Demand forecasting is a branch of predictive analytics that businesses can use to predict future demand for their products or services. Accurate demand forecasting can help businesses prepare for seasonal highs and lows, handle their money effectively, and understand how various internal and external factors can influence sales. There are several different types of demand forecasting, and most businesses will benefit from performing more than one type of analysis.⁴

Passive demand forecasting is one of the simplest methods. It uses past sales data to predict future demand. If a business has a steady sales history and is seeking stability, passive demand forecasting can be an effective model. It relies on the assumption that sales in previous years are good predictors of future sales. To be effective, this model requires analysts to use previous data from a similar time frame.⁴

Active demand forecasting relies on external factors, such as market research, the economic outlook and growth projections, rather than historical sales data. This option is good for businesses that are just starting and those that want to grow since they don’t have an established track record to use as a base for predictions.⁴

In times of wider economic volatility, external macro forecasting can take into account how broad economic trends will affect an individual business’s sales. External macro forecasting is particularly useful for determining how market forces, such as inflation, will affect aspects of the supply chain such as raw material availability, and how that, in turn, will affect future sales.⁴

Financial forecasting is the process of predicting future financial performance and using those business prediction models to assist with financial planning through budgeting and financial modeling.

Some types of financial forecasting include:⁵

• Top-down forecasting, which calculates market share based on total market size
• Delphi forecasting based on expert opinions, which can be used to forecast stock market trends, economic growth rates or other financial indicators
• Statistical forecasting based on historic statistical models, which can predict a wide range of financial performance factors and outcomes
• Bottom-up forecasting, which predicts a company’s broader performance based on narrow, ground-level data—the smallest units of business activity, such as individual product sales

Predicting customer behavior can help businesses connect with their customers. They can use the information they gain to better understand their customers and create programs that help them attract new customers and retain existing ones. For example, businesses can create customer profiles and use statistical methods to predict which customers will continue to use their product or service and which will not. With this information, it’s possible to change their offerings or their marketing campaigns to retain more loyal customers.⁶

Managing regulatory, environmental and economic risks is an inherent part of business operations. Risk management analytics can improve this process by allowing businesses to proactively identify, analyze and mitigate risks. While it’s impossible to prepare for every type of risk, predictive models in business use big data to protect against financial and compliance risks.⁷ Effective business prediction models offer insight into potential crises and help inform decision-making when problems arise. By identifying risks and planning for various outcomes, new businesses can avoid catastrophes as they grow.

Financial risks are those that can cause businesses to lose money or investments. These can include interest rate fluctuations, extended credit and excessive debt. Identifying and correcting these risks can improve a company’s financial health and bottom line.⁷

Regulatory compliance used to be primarily the responsibility of the healthcare and finance industries. However, businesses in other industries are increasingly tasked with complying with regulations ranging from data protection to the anticipated European Union AI Act. Businesses that fail to comply with these regulations can face expensive fines and other legal ramifications.⁸

Compliance risk management is a continuous process of identifying and tracking changes in the regulatory landscape and an organization’s activities to comply with them. Mitigating compliance risks can prevent financial and reputational damage that can result from regulatory non-compliance. Experts can use analytics for business strategy to create an automated risk identification and management model that drives efficient compliance processes.⁹

Data analytics can expose avenues for increasing profitability by managing risk and reducing waste, bottlenecks and other inefficiencies in business operations. With the massive amounts of data available in all areas of operations, businesses can use the data to drive improved performance by managing their people, money, and equipment more effectively.¹⁰

The core that underlies all methods of predictive analytics is strategic decision-making. Predictive analytics tools and models put the power of big data insights in the hands of business leaders. Analysts can evaluate different scenarios using data science, machine learning and statistical models. The insights they glean from their analysis allow leaders to make informed decisions about business strategy with more accuracy and confidence. Predictive analytics drives continuous improvement that results in better overall performance.¹⁰

What do predictive analytics look like in practice? Here are a few examples.

Marketing analytics look into key data metrics such as customer engagement, website traffic, seasonal trends, and survey responses to find patterns and improve future marketing strategies.¹¹ Marketing teams also use predictive analytics to establish their budgets.

Marketing analytics platforms such as HubSpot, Google Analytics, and SEMrush offer scalable plans to help small and even single-person marketing teams harness predictive analytics and make informed decisions.

Analytics help organizations develop predictive models to facilitate strategic decision-making. Predictive models in business are shaped by historical and current data and offer a practical look into the future.¹² Modern software helps business owners generate models based on real-time data while comparing different scenarios. For example, a finance team can create budgetary models for scenarios such as natural disasters that could lead to supply chain disruptions or staffing shortages.

A rigid budget can stifle creativity and hinder business growth.¹³ Budget forecasting allows teams to look into the future and adjust their budgets throughout the year. With predictive analytics, marketing teams–and other departments–can predict challenges that may arise, analyze their current data, and allocate resources to account for new opportunities.

Supply chain issues and staffing shortages are two of the biggest challenges facing businesses today. Predictive analytics help business owners prepare for these challenges and appropriately manage their resources.

For example, analyzing historical supply chain issues can help business owners predict the next slowdown and stock up on their inventory ahead of time. Similarly, if predictive models show that staffing shortages are likely around the holidays, businesses can prepare by offering incentives and boosting their recruitment efforts.

William & Mary’s Online Master of Science in Business Analytics will position you to become a globally-minded, sought-after business leader. You’ll develop the expertise you need to transform data into opportunities in as little as 15 months with our innovative curriculum led by top-ranked faculty. Courses in predictive analytics, machine learning and artificial intelligence applications for business provide the foundation you need to derive strategic insights from data and persuasively communicate with stakeholders.

Reach out to one of our admissions outreach advisors today to learn more about W&M’s Online MS in Business Analytics.