What is predictive analytics? Importance, benefits, & examples

How does predictive analytics work?

Predictive analytics starts with data mining – researching and gathering useful and relevant data.

Then comes data management, which is the process of organizing and cleaning the data for statistical modeling. 

In predictive analytics, a data scientist inputs several datasets into a machine learning algorithm that sifts through the data and creates various types of predictive models.

Here are a few of the predictive modeling techniques data scientists use when analyzing data:

• Linear regression. This is one of the simplest machine learning techniques. It uses a linear model to show the relationships between one or more independent variables and the target response.
• Logistic regression. This statistical technique explains the relationships between two binary independent variables with one or more nominal independent variables.
• Decision tree. This algorithm displays the likely outcome of different actions by graphing structured or unstructured data into a tree-like structure.
• Neural networks. These complex algorithms are used in deep learning and allow for pattern recognition within large data sets.

With so much data, it is no surprise that artificial intelligence and machine learning techniques play a large role in predictive analytics models. Using AI and ML has broadened the field of data science and allowed data analysts to find and recognize patterns they would otherwise miss.

What are some benefits of predictive analytics?

Let’s examine some benefits of predictive analytics to solve several kinds of business problems.

• Improved customer targeting: Analyzing data can help businesses identify and target their ideal customers more effectively.
• Increased customer retention: Data can help businesses identify customers at risk of churning and take steps to prevent them from leaving. 
• Reduced fraud: Data can help businesses identify fraudulent transactions and prevent them from occurring in the first place.
• Optimized operations: Data can help businesses optimize their operations, such as supply chain and inventory management. 
• Improved decision-making: Data can help businesses make better decisions by providing actionable insights into their customers, operations, and the market. 
• Increased revenue:Data can help businesses increase revenue by identifying new opportunities, such as upselling and cross-selling to existing customers.
• Gained competitive advantage: Data can help businesses gain a competitive advantage by providing them with metrics and insights their competitors do not have.

What are the common uses of predictive analytics in business?

Let’s look at some examples of how businesses use predictive analytics.

1. Customer segmentation

Businesses can use data on customers’ interests, demographics, and purchase behavior to segment them into specific groups. 

Once customers are segmented, businesses can use this information to effectively target marketing campaigns and product offerings. A great example of this is with email marketing and automation tools. A business might create a segmented group for mothers aged 25 to 35 who own their own homes, then create an email marketing campaign targeting them for housecleaning services.

2. Fraud detection

Credit card and insurance fraud are harmful practices impacting many businesses. Predictive analytics helps businesses detect and prevent fraud and avoid financial losses. 

For example, many credit card companies have fraud detection built into their machine learning algorithms. With machine learning, the algorithm knows your buying patterns and can identify suspicious activity. This triggers a transaction rejection and alerts you of the activity.

3. Risk assessment

Risk affects all businesses. There’s the risk of customers defaulting on loans or customer churning, in which customers leave a brand.

Businesses can use analytics tools to collect historical data involving loan defaults and customer churning and use the information to make better decisions about lending, pricing, and marketing.

Many banks use a loan application to collect data. Based on past data collection and analysis, they can look at a person’s credit history and score to assess how likely they are to miss payments or default on the loan. They also use it to determine how much they are willing to loan that person.

4. Demand forecasting

With predictive analytics, businesses can forecast demand for their products or services. This basically predicts how likely businesses are to sell certain products or services at certain times of the year or after certain events.

Demand forecasting, in particular, uses pertinent information to optimize production, inventory, and staffing levels.

Many large companies use demand forecasting in their inventory management. For example, a construction store in hurricane-prone areas knows product demand will skyrocket during and after hurricane season. 

This is when many people do storm preparation or repair their homes after a hurricane. Because of this, the store must ensure they order enough stock in advance for their high customer demand.

5. Operations optimization

Businesses can also use predictive analytics to optimize their operations, such as supply chain management and inventory management. With this information, they can reduce costs, improve efficiency, and improve customer service. Delivery companies are a great example of this. They can use past and current data analysis to predict future trends (e.g., the busiest delivery periods of the year), which helps them increase staff to meet the demand. They can also analyze fuel consumption and driving route data to optimize their routes for lower fuel costs and faster delivery.