AI Predictive Analytics: Benefits, Examples & More

While we can’t accurately predict the future just yet, artificial intelligence (AI) predictive analytics can get us pretty close. Its power lies in the ability to quickly sift through vast amounts of data to reveal meaningful patterns, trends, and anomalies. 

But predictive AI isn’t only applicable to the tech space. 

Whether pinpointing the best time for heavy machinery maintenance or building customized learning paths, AI predictive analytics has incredible applications for nearly every industry. Here’s how it works and what makes it such a powerful tool for everything from logistics to finance. 

What is AI predictive analytics?

Predictive analytics uses data, mathematical models, and statistical methods to forecast the likelihood of future outcomes by examining past data. These statistical methods often involve AI, deep learning, and machine learning. To accomplish this, predictive analytics relies on data science to analyze and extract insights from large quantities of historical data from a variety of sources such as databases, videos, images, logs, and more. 

What makes AI predictive analytics different from standard predictive analytics? Essentially, AI predictive analytics uses AI to make predictions about future events and answer questions about how likely (or not) it is for something to happen.Traditional predictive analytics leans on methods and techniques such as regression analysis, data mining, and modeling.

This predictive technology helps organizations anticipate customer behavior, prepare for demand, mitigate risks, and adapt quickly to market changes. It’s an essential tool for modern businesses that want to quickly seize emerging opportunities and stay ahead of their competitors. Now, with the advent of AI, predictive analytics usage has soared, allowing organizations to analyze large quantities of data from diverse sources in a matter of seconds. What used to take countless hours of research, compilation, and analysis can now be done almost instantly.

Differences between artificial intelligence and predictive analytics

Is predictive analytics the same as AI? Not exactly. Predictive analytics involves using statistical models and historical data to forecast future trends and behaviors. It is a subset of AI and one of AI’s many applications. On the other hand, AI uses machine learning models and computers to process language, recognize patterns, and make decisions. 

While predictive analytics helps people make informed, data-driven decisions, AI can take it a step further by operating autonomously, learning from new data, and make decisions.. This is not possible with predictive analytics, which is a manual process that requires human intervention and analysis. 

How predictive analytics and AI work together

Predictive analytics and AI work together in several ways, especially when it comes to using data. Gathering and analyzing data is essential for predicting future outcomes, and AI supercharges the process. It can analyze and understand large volumes of data to predict the probability of future events. Plus, it can bring in data from multiple sources and provide a complete picture of that information.  

Another key application of AI is modeling, where it is used to build predictive models trained on historical data. These models can then be used to make predictions about future events, trends, or outcomes. AI-powered predictive analytics models can help provide essential insights to make decisions around those future events. The automation behind AI also streamlines operations and processes related to creating predictive models. 

Speaking of decisions, AI and predictive analytics work together to provide real-time predictions and recommendations. Organizations can use these insights to power strategies and tactics used to enhance operations, convert customers, allocate resources, and more. 

Benefits of AI predictive analytics  

AI-powered predictive analysis can benefit just about every area of a business can benefit from predictive analysis in AI. Here are some of the top benefits it provides organizations:

• Improved operational efficiency by identifying areas to improve, automating manual tasks, and streamlining processes. 

• Greater cost efficiency as improved operations reduce costs and resource usage. 

• Informed decision-making through the insights gathered during data collection and analysis. 

• Greater visibility into key organizational data across multiple sources and formats. 

• Personalized customer experiences by using customer behavioral data to increase engagement.

• Increased productivity by using AI predictive analytics to cut down on  tedious, manual tasks and provide insights to power more strategic work. 

Examples and use cases of AI predictive analytics 

Just as AI predictive analytics provides many benefits, its application is suitable for a variety of industries. Ultimately, any industry that can benefit from data-driven insights and information regarding the prediction of future outcomes and behaviors should explore using AI predictive analytics. 

Marketing

AI predictive analytics can transform marketing in several exciting ways. One of the top use cases applies to customer data, as marketers can pull insights and information from vast amounts of information regarding customer preferences, interests, behaviors, and patterns. Here are some key benefits:

• Personalization: Marketing departments can create personalized product recommendations and tailored campaigns using historical data on purchases, engagements, and browsing habits. 
• Behavior prediction: AI predictive analytics can be used to predict future buying habits and build related promotions and campaigns to drive more sales. 

• Customer churn prediction: By using AI predictive modeling, businesses can project which customers are likely to churn or leave a business. Marketing teams can respond by creating targeted, personalized campaigns to reduce customer churn rates. 

• Segmentation: Marketers can more effectively segment customers based on various factors, such as behaviors, demographics, and interests. Doing so allows companies to create hyper-personalized, targeted content for specific customer segments. 

• Predictive Customer Lifetime Value (LTV): AI predictive analytics can be used to predict the value of a customer over their lifetime of engagement with a company taking into account past purchases, buying habits, engagement levels, and more. Marketers can then prioritize efforts for customers with the greatest potential LTV.

Healthcare

AI predictive analytics is revolutionizing healthcare by improving patient outcomes and enhancing decision-making processes. Here are several applications:

• Disease detection: AI predictive analytics can analyze patterns and anomalies that may indicate the presence of certain diseases and illnesses. This method is often faster and more accurate than traditional detection methods, allowing for earlier intervention and better patient outcomes. 
• Readmission risk prediction: Similarly, these predictive models can read through and analyze pertinent data to identify patients who are at greater risk of hospital readmission. This allows providers to intervene as necessary and adjust the post-case plans for patients. 
• Healthcare fraud detection: Patterns of healthcare fraud are often difficult to detect manually. AI predictive analytics can make all the difference by analyzing patterns to uncover anomalies . This ability helps providers and organizations reduce financial losses. 

Retail

AI predictive analytics is highly applicable in the retail industry, particularly in how to manage  inventory, supply chain, and pricing. For example:

• Inventory management: Retail companies can use AI predictive analytics to analyze customer buying habits and market trends. Retailers can then make more informed decisions about purchasing and managing inventory. 
• Supply chain management: AI predictive analytics can analyze historical data, market conditions, and external factors to predict potential changes in the supply chain. Retailers can use this information to mitigate risks and strategize accordingly. 

• Dynamic pricing: Retailers can adjust prices in real time based on inventory, demand, seasons, and competitive factors through the use of AI predictive analytics.This allows retailers to optimize their pricing strategies and stay competitive in the market. 

Finance 

• Fraud detection: Financial organizations can use AI predictive analytics to identify and analyze data for unusual patterns that could indicate fraud. These irregularities might include frequent returns or orders from multiple individuals at the same address, etc. By uncovering these fraudulent activities, organizations can safeguard their profits and protect legitimate customers. 

• Credit risk assessment and scoring: AI predictive analytics analyzes data to determine the credit risk of individuals and businesses. What makes this predictive technology a game-changer is its ability to consider non-traditional data, such as employment history, utility payments, and behavioral patterns.

Manufacturing

AI predictive analytics fits into manufacturing processes and operations by supporting predictive maintenance, quality control, and resource management. 

• Predictive maintenance: Regularly maintaining appliances, tools, equipment, and machinery is essential in manufacturing. However, it can be difficult to determine when maintenance is required, which can lead to excess downtime. AI predictive analytics can tap into equipment data to determine when maintenance is needed and avoid failures. 

• Quality control: Likewise, predictive analytics can monitor data patterns to identify quality issues. This information is then used to adjust processes to improve quality and avoid defective outputs. 

• Resource control: As manufacturing companies improve quality and rely on predictive maintenance, they can optimize the allocation and usage of their resources to generate cost savings. 

• Process improvement: Predictive analytics helps to optimize processes by uncovering areas for improvement and identifying bottlenecks. 

Transportation and logistics

Organizations in the transportation and logistics industry lean on AI predictive analytics to optimize operations, lower costs, and reduce inefficiencies. 

• Predictive maintenance: AI predictive analytics can determine when maintenance should be performed on machinery and equipment to minimize failure and downtime. 

• Last-mile delivery: By analyzing real-time conditions such as demand, traffic, or weather, transportation and logistics companies can optimize routes to get deliveries to customers faster while safeguarding resources. 

• Transportation Management Systems: To better plan for capacity, AI predictive analytics can be used to forecast future demand. Companies can use this information to prepare personnel, fleets, routes, and other resources. 

Education

AI predictive analytics is equally applicable in the educational sector, from elementary to higher education. 

• Early intervention: Educators can identify at-risk students and plan for early intervention through AI predictive analytics.

• Personalized learning: AI predictive analytics can be used to personalize educational content and learning paths so educators can focus on the individual needs, preferences, and learning styles of students. 

• Administrative planning: Administrators can use predictive technology to study trends and patterns in scheduling and enrollment. 

In summary, AI predictive analytics is not just a powerful tool; it’s a game changer. By harnessing the capabilities of AI, businesses can gain deeper insights into their data, anticipate future trends, and make informed decisions that drive growth and innovation. As industries continue to evolve, embracing AI predictive analytics will be essential for staying competitive and meeting the ever-changing needs of customers. Organizations that invest in this technology today will position themselves for success in an increasingly data-driven future. Find out how Domo can fit into your analytics strategy.