Predictive analytics is the study of historical data to make future predictions. It is a data analysis method that uses past information to identify patterns and make educated guesses about future events.
Ilya Smirnov, Head of the AI/ML Department at Usetech explains predictive analytics as a method of analyzing data to make successful decisions in the future based on the results of past events. “By interpreting data, businesses can make decisions that will improve their performance,” says Smirnov.
Predictive analytics has been used since around the 1950s in the public sector and in industries like the medical field, but had a huge explosion in usability for other fields around 1990, says Josh Meier, Senior Generative AI Author at Pluralsight.
He reasons this is because the internet and computer development made it much easier to get and hold large amounts of data. “So, predictive analytics is nothing new,” says Meier. “Even with the release of GPT and other large language models, predictive analytics has remained relatively unchanged since its inception.”
Crunching big data
Predictive analytics represents a fundamental evolution beyond traditional business intelligence (BI) and reporting, explains Yigal Edery, SVP of Product & Strategy at Sisense. While traditional BI tells you what happened in the past, predictive analytics uses historical data patterns, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.
The key strength of predictive analytics lies in its ability to turn massive amounts of data into actionable insights. This helps businesses anticipate opportunities and risks before they arise.
According to Rogers Jeffrey Leo John, co-founder and CTO of DataChat, analysts use special algorithms, called models, to uncover these patterns. The process of teaching a model to recognize these patterns from historical data is called training.
Once trained, the model can be used to make predictions. “Often, analysts create and test multiple models to determine which one performs the best in capturing insights from the data,” says John.
How can PA help businesses
John says predictive analytics allows companies to anticipate trends, minimize risks, and optimize operations. Here are some of the key ways it benefits businesses, according to John:
- Improved Decision-Making: Using predictive analytics, businesses can lean on data-driven insights to guide strategic choices.
- Risk Management: It helps businesses identify potential risks and mitigate them in advance.
- Cost Optimisation: It can help predict demand to avoid overstocking or understocking.
- Personalization: It helps tailor products, services, and marketing to individual customers.
- Operational Efficiency: Businesses can also use it to forecast equipment failures and streamline maintenance.
John also shares some of the real world examples where predictive analytics is already being used today:
- Detecting fraudulent transactions and predicting loan defaults.
- Forecasting sales trends and managing inventory levels.
- Predicting customer churn and personalizing campaigns to increase retention.
- Identifying patients at risk of chronic conditions or readmission.
- Anticipating equipment breakdowns for predictive maintenance.
- Optimizing delivery routes and forecasting demand fluctuations.
- Personalizing product experiences, such as song recommendations on Spotify or movie recommendations on Netflix.
“By proactively addressing challenges and seizing opportunities, predictive analytics enables businesses to stay competitive and agile in a rapidly changing environment,” says John.
PA use cases
Arunkumar Thirunagalingam, Manager of Enterprise Data Management at Santander Consumer USA, agrees. He asserts predictive analytics streamlines operations, reduces costs, improves customer experiences, and enhances decision-making across various industries.
For instance, he says in risk management, particularly in insurance and banking, predictive analytics helps detect fraud and assess risks more accurately, enabling better pricing and decision-making.
It also helps stores like Walmart optimize supply chains by forecasting demand and managing inventory. This helps ensure the stores have the right products available at the right time.
Even in healthcare, predictive models are used to identify at-risk patients, optimize resources, and reduce hospital readmission rates, improving both patient care and cost-efficiency, says Thirunagalingam.
Kaveh Vahdat, Founder and President at RiseOpp shares a couple of interesting use cases for predictive analytics.
He says in the digital marketing space, predictive analytics can significantly enhance SEO and content marketing by forecasting trends in search behavior, consumer preferences, and the effectiveness of marketing campaigns.
“For businesses, this means understanding not just what customers are doing now, but what they are likely to do next,” says Vahdat. “This allows companies to preemptively adjust their strategies, optimizing content and ad placements for maximum engagement and conversion.”
Vahdat says predictive analytics is equally crucial in the gaming industry. His company, RiseAngle leverages generative AI to create personalized gaming experiences. Predictive models help forecast player behavior, helping game developers understand player preferences, anticipate drop-off points, and optimize in-game monetization strategies.
For instance, he says, predictive analytics can identify which game features are most engaging or which users are likely to make in-game purchases, allowing developers to tailor experiences that keep players engaged for longer periods.
“In generative AI gaming, this technology helps balance the development of new content, ensuring it aligns with what players are most likely to enjoy, while simultaneously optimizing business outcomes like user retention and lifetime value,” says Vahdat.
Predictive analytics and AI
Predictive analytics, artificial intelligence (AI), and even machine learning (ML) are interconnected fields but distinct.
John explains that AI is a broad field focused on creating systems that mimic human intelligence, including decision-making and learning. Predictive analytics, he says, is a practical application of AI, which uses techniques like ML to analyze historical data and forecast future outcomes.
Furthermore, while ML, itself a subset of AI, builds models that learn patterns from data, predictive analytics uses these models to enhance predictions.
“Predictive analytics often uses ML algorithms to enhance its forecasting capabilities,” adds Vahdat. “Machine learning allows predictive analytics models to continuously improve as they process more data, making predictions more accurate over time.”
Essentially, predictive analytics is a subset of AI that leverages ML to refine its predictions.