Applications of Predictive Analytics in Business

You’ll find plenty of characters with psychic abilities in popular culture, but no one can see the future in the real world. Predictive analytics, however, allows you to make educated guesses about future business trends and outcomes. This approach uses statistical models and machine learning to create predictions based on historical data.¹

Predictive analytics has become an essential tool in many industries. According to Pecan’s 2022 State of Predictive Analytics in Marketing, 95% of companies use artificial intelligence (AI)-powered predictive analytics to guide their marketing strategies.² This method also has applications in finance, healthcare, logistics, and numerous other sectors.³

Read on to discover how data-driven insights revolutionize decision-making, enhance strategies, and drive growth.

Predictive analytics uses statistics and modeling techniques to analyze historical data and forecast future outcomes. Businesses use this approach, and the actionable insights it provides, to make informed decisions and mitigate risk. For instance, investors can use predictive analytics to project future stock market trends and invest strategically.³

Predictive analytics is one of the most advanced types of business analytics. Professionals in this field typically have expertise in machine learning algorithms. These formulas can process vast data sets and uncover hidden patterns—in customer behavior, customer retention, the performance of marketing campaigns, and many other metrics. Analysts also use statistical modeling to analyze historical data and predict future outcomes.¹

Predictive analytics is one of four common business analytics techniques. Each method has specific uses.⁴

• Descriptive analytics is one of the simplest methods and interprets historical data to understand past events
• Diagnostic analytics investigates the factors that influenced past outcomes
• Prescriptive analytics suggests future actions to achieve desired outcomes

Data quality determines the accuracy of the predictions it can uncover. Even expert data scientists can't make reliable forecasts by analyzing outdated or irrelevant data.

Organizations collect data from many sources, such as sales transactions, social media platform interactions, and customer reviews. If you're looking for helpful answers, defining your questions can help you select appropriate data sources. Prioritize quality over quantity and focus on timely, relevant data.⁵

Before you can analyze data, you must clean and preprocess it. These essential steps in data management remove errors, irregularities, and outliers from the raw data. You can speed up this process with algorithms or open-source tools. Popular data cleaning platforms include Data Wrangler and OpenRefine.⁶

Business analysts use various predictive modeling techniques to gain insights and forecast trends.

Regression analysis uses statistical models to investigate the relationships between dependent and independent variables. This approach uses historical connections to extrapolate the future relationship between these variables. For example, you can use regression analysis to predict how hiring more sales representatives will affect future revenue.⁷

Business analysts train classification algorithms to sort new data into categories based on historical data.⁸ Commonly used algorithms include:

• Decision tree: Sorts data by making a series of decisions based on its features, and each node on the tree represents a choice⁸
• Random forest: Creates a series of decision trees based on randomized features and combines the results to make the most accurate predictions⁸
• Support vector machines: Uses lines or curves to sort data and aims to maximize the margin between categories to reduce errors⁹

These clustering algorithms divide data into natural groups based on similar features:⁸

• K-means: The algorithm groups data points into random clusters and adjusts these groups to minimize variance
• Birch classification: The algorithm clusters data into a hierarchical tree structure

Time-series forecasting models use historical data to predict future events over a period of time. For example, you can use sales data from the last ten years to forecast next year’s revenue.¹⁰

There are numerous business applications for predictive analytics tools. Business leaders use them to help incorporate data-driven insights into their decision-making processes. They also use this knowledge to boost efficiency and minimize risk. For example, retailers can use existing data to predict customer preferences and design products that meet these needs.⁴

The effective use of client data enables companies to plan marketing strategies. This information helps marketers predict customer churn and target the leads most likely to respond to advertising campaigns.³ Human resources professionals also use predictive analytics to forecast future staffing needs, reduce employee turnover, and analyze individual employees' performance and help them plan their career progression.³ Additionally, predictive analytics can forecast changes in the supply chain. Manufacturers use this information to anticipate disruptions and allocate resources efficiently.³

Business analysts use numerous tools and platforms to implement predictive data strategies. The programming languages Python and R have extensive libraries that analysts can use to clean, manipulate, and visualize data. Additionally, Statistical Analysis System (SAS) software enables users to create predictive models and apply advanced statistical methods.¹¹

Two emerging trends may soon revolutionize business intelligence and predictive analytics.

Machine learning has evolved rapidly as technology advances. Some analysts have begun using natural language processing to make forecasts based on unstructured data, such as emails and user-generated content.

Analysts will likely create predictive models that synchronize constantly to incorporate real-time data. These tools will allow businesses to make the most accurate and timeliest decisions.¹²

The field of data analytics has evolved quickly. Stay ahead of these advancements by earning the Online Master of Science in Business Analytics from Santa Clara University. Our experienced faculty teach cutting-edge predictive analytics techniques and technologies. Their classes and mentorship will help you develop expertise in business analytics, machine learning, and information technology. You’ll also learn how to use data-driven insights to lead innovation and foster organizational growth.

Balance your education with your professional and personal commitments. Designed for working professionals looking to expand their networks and level up their careers, our flexible online program delivers the foundational business knowledge and principled leadership skills to propel your career, in Silicon Valley and beyond.

The Leavey School of Business can help you reach your career goals. Start today by scheduling a call with an admissions outreach advisor.