Predictive Analytics Explained: Models, Tools & Business Impact

There are five primary types of models or methods included within predictive analytics. They are classification models, clustering models, time series models, regression models and ensemble methods. Below we dive deeper into each. 
 

Classification models

These models categorize data based on historical information and function to describe relationships within a data set. Using machine learning, a classification model learns the relationship between data and its given labels so can classify new data as it is added. 
 

Clustering models

These models group data points based on similar characteristics. Using a data matrix, they connect similar data points and find patterns that may not have been obvious in other modeling techniques. 
 

Time series models

These models use data points that are captured over a period of time, whether that be daily, weekly, monthly or annually. This type of modeling is the most commonly used form of predictive analytics. It assesses seasonal changes, cyclical customer behaviors and trends over time. 
 

Regression models

These models function to decipher the relationship between two variables over a period of time. They can look at customer data and predict how much revenue that customer will bring in over their lifecycle. These models show how specific actions can impact overall business outcomes. 
 

Ensemble methods 

Ensemble methods work to bring all these models together to create a more holistic and informative set of insights. These help to make sense of complexity and provide more unified information for business leaders’ decision-making. 

Using predictive analytics can help inform organizations on the health of their business and give them relevant information to move forward. Organizations can review data patterns to forecast future performance, which can help them make better decisions. 

With predictive analytics, organizations can also improve their customer journeys by developing personalized pathways that better cater to the needs of their customers. Predictive analytics can also detect anomalies, such as patterns of fraud, and remedy them before they get out of hand. 

Overall, organizations can improve their operational efficiencies. Predictive analytics take some of the guesswork out of which campaign types or program types to launch, based on past performance.

There are a handful of commonly used techniques in predictive analytics. Below we break them down.
 

Regression analysis

This form of analysis focuses on predicting the relationship between variables. In data sets, it is used to find patterns and find correlations between data points. By creating a given formula in a data set, you can track correlation. 
 

Decision trees

This technique helps map a series of decisions made by putting data into various categories based on variables. It can be used to indicate the various outcomes that could occur if different choices are made. Since these models are easy to review and understand, they can help expedite decision-making. 
 

Neural networks

This technique was created to identify non-linear relationships within data sets. Using AI and pattern recognition tools, it mimics how the human mind works, associating seemingly disconnected data points. It can analyze multiple forms of data, including text, image, audio and more. 
 

Time series forecasting

Time series forecasting is a vital technique because organizations often look at data points over a period of time. This type of forecasting focuses on daily, weekly or monthly iterations of data. By focusing on behavioral trends over time, they can predict outcomes. 
 

Clustering algorithms

Clustering connects similar data points. Via clustering, organizations can use data, such as a past purchase, to predict which product they're likely to purchase next.

Across industries and various arms of businesses, predictive analytics can be an asset. Below we break down some common use cases in prominent industries. 
 

Finance

Looking at a customer’s credit history, a financial organization may be better able to predict the likelihood of a customer repaying a loan. Predictive analytics can also help mitigate fraud. Financial institutions can look for irregularities in transactions, flag concerns in real time and put guardrails in place.
 

Healthcare 

Predictive analytics can have significant impacts for patients. Healthcare organizations can use analytics to manage care for patients by tracking data points that signal the start of a more serious problem, such as sepsis in patients who are in long-term hospital care. Organizations can also review patient health records and provide preventive treatment, including recommending that patients improve their diet, given a family history of developing diabetes. 
 

Marketing

Predictive analytics can more accurately assess the success of future marketing campaigns. Marketers can use historical data to determine which types of campaigns their target audiences engage with and adjust strategies as needed. 
 

Supply chain 

Predictive analytics can help manufacturers better manage and distribute their inventory. Using historical data, suppliers can anticipate which customers will need which supplies and when, and they can better manage their own inventory. They can also proactively suggest re-orders to their customers based on the historical data on customer needs and demands.

Businesses can realize many benefits from implementing predictive analytics. Key advantages include:
 

1. Improved forecasting and planning

To stay competitive, organizations must be forward-thinking by predicting outcomes, taking advantage of opportunities and protecting themselves from potential losses. 
 

2. Enhanced customer targeting and retention

Using predictive analytics, business leaders can determine customer behaviors. By analyzing their customer bases and applying predictive analytics to customer personas, organizations can tailor marketing campaigns toward specific audiences and have greater insights into the higher likelihood of engagement. 
 

3. Reduced operational risk

Organizations can better detect fraud and financial risk by using predictive analytics. Companies can flag questionable or abnormal purchases earlier if the nature of the purchase falls outside of typical scope. Additionally, organizations can more easily review data points like credit scores and insurance claims to predict the likelihood of a customer defaulting on a payment. 
 

4. Increased ROI and efficiency

Overall, predictive analytics can improve an organization's efficiency and increase its return on investment. Predictive analytics helps companies forecast their inventory and better manage their internal resources. They also help organizations predict the performance of a product in development by looking at data on the needs and wants of their customers. Organizations can get ahead of production delays too by monitoring routine equipment maintenance. 
 

5. Better decision-making with data-driven insights 

Using predictive analytics, organizations have more information to make better decisions. Organizations can be proactive by looking at historical trends to support future decision-making. This can include decisions on how much inventory to have in stock of a certain product, which types of marketing campaigns to develop and what ways to improve productivity in their operations.

Predictive analytics is essential to any organization looking to make informed decisions based on historical data. By looking back at patterns associated with past performance, organizations can alter their strategies to make better-informed decisions and hopefully achieve better outcomes.