Predictive Analytics: How Businesses are Leveraging Data to Forecast Trends
At its core, predictive analytics utilizes historical data and analytics methods like machine learning and statistical modeling to predict future outcomes. It can process and examine large amounts of data to find future trends, probabilities, and behaviors.
According to The Insight Partners, the global market size of predictive analytics is anticipated to surpass USD 38,038 million by 2028. In predictive analytics, the primary techniques used are neural networks, regression, and decision trees.
Predictive analytics uses a wide variety of data, such as feature data, historical data, external data, text and unstructured data, sensor data, time series data, geospatial data, and more, to make accurate forecasts. Let’s explore the use of predictive analytics in different industries and the benefits of accurate forecasting.
How Industries are Using Predictive Analytics
Every industry uses predictive analytics to detect future trends and outcomes, from healthcare to energy and finance to manufacturing. Here are some common ways different sectors use predictive analytics.
Retail
The retail industry is one of the biggest users of predictive analytics. Retailers can learn about customers’ buying patterns and future market trends. Predictive analytics can also help retailers analyze product demand, provide information about stock, and optimize the supply chain.
Healthcare
Predictive analytics can process demographic data, health records, and past admission data to detect health risks for individual patients. They can also schedule regular check-up dates, prevent human errors, provide healthcare tracking, help with personalized treatments, and reduce healthcare costs.
Insurance
Predictive analytics help insurance companies recommend personalized policies to customers. Some firms are using analytical models to review policy applications and understand policyholder profiles. This helps businesses adjust their policy terms and risk management techniques.
Hospitality
The hospitality industry is using predictive analytics to determine supply and demand. For example, hotels use predictive analytics to understand when there will be high demand for accommodation and price accordingly. Also, hotels use the data to understand the likes and dislikes of recurring customers, enhancing customer satisfaction.
Manufacturing
In the manufacturing sector, businesses can use predictive analytics to schedule equipment maintenance and receive alerts about potential machine breakdowns. This helps reduce costs and improve customer satisfaction.
Sports
Even the sports industry is leveraging predictive analytics. For example, professional basketball and football teams employ data analysts to analyze the players’ performance, like scoring, speed, health, time, etc. Using off-field and on-field data, predictive analytics can make predictions about the value and regression of all players.
Aviation
Predictive analytics can give forecasts about the potential failure/breakdown of the machines and handle the maintenance schedule of the airplanes, including the engine, fuel use, and more. In addition, predictive analytics can forecast weather and the demand for tickets.
Human Resources
As the human resource department handles large volumes of data, predictive analytics can process and analyze it. These algorithms can provide predictions related to employee performance, employee engagement, staff turnover, and more.
Energy
Predictive analytics can predict future energy needs by analyzing previous energy demands and influencing factors. With advanced predictive analytics, companies can avoid power equipment failures, handle potential risks, and ensure regular maintenance and replacement of the components.
Predictive Analytics Real-Life Use Case Examples
Businesses in different sectors are using predictive analytics to detect issues early on, enhance the efficiency of their services, and more. Here are a few examples of how big organizations leverage predictive analytics and greatly benefit.
PepsiCo
How: Using predictive analytics, PepsiCo handles the global supply chain.
PepsiCo developed the Sales Intelligence Platform, which merges supply chain data and retailer data in order to know when the product will be out of stock. The platform will send alerts, making it easy for the retailer to reorder early. Therefore, with the help of predictive analytics, PepsiCo is rearranging its field sales teams and ecommerce sales.
Nike
How: Nike used predictive analytics to enhance its digital marketing strategies
Nike acquired predictive analytics firms and analyzed data from IoT devices and apps to identify customer behaviors and purchasing patterns. They used predictive analytics to develop digital marketing strategies, which enhanced their ROI by 20% and customer engagement by 10%.
Rolls-Royce
How: Using predictive analytics, Rolls-Royce enhances maintenance schedules
Carbon footprint has become a major problem in the automotive and airline industry, so Rolls-Royce is using predictive analytics to decrease carbon footprint. Rolls-Royce created the Intelligent Engine Platform, which allows them to monitor the engine’s condition, how it flies, etc. With predictive analytics, Rolls-Royce optimizes the maintenance schedules of the engines, minimizing carbon footprint.
Benefits of Accurate Predictive Analytics
There are plenty of advantages of predictive analytics for organizations, which include:
- Helps detect fraudulent transactions and alerts about suspicious activities, enhancing the safety of financial transactions.
- It can predict the customers’ buying behavior, allowing companies to customize their marketing strategies and pricing of the products or services.
- Analyzes patient data and helps doctors provide customized treatments.
- It gives predictions about potential machine failure and provides a maintenance schedule.
- Helps in categorizing buyers depending on their preferences, buying patterns, and other metrics.
- Can identify the demand for certain things, such as accommodation, flight tickets, and consumer goods.
- Analyzes the sales, trends, and stock levels to give alerts about the stock of the products for retail stores and manufacturing businesses.
- Process the crime data to predict the risk of crimes happening in a certain place or during a specific period.
- Analyze the applicant’s credit history to forecast whether the applicant can repay the loan.
Wrapping Up
Using advanced predictive analytics models and tools, it has become easy for businesses to predict behaviors and trends by leveraging past and present data. Organizations can use predictive analytics to predict future outcomes and make better decisions. Almost all sectors, from retail to automotive, leverage predictive analytics to forecast trends and benefit significantly.
