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What Is Predictive Analytics?

Updated Oct 23, 2023

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Shannon Flynn
Contributing Writer at
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Every business has a treasure trove of data, from customer and transaction information to manufacturing and shipping statistics. The key is figuring out how to use it to better the business’s future.

One strategy is for companies to use predictive analytics. This involves combing through past information to derive models and analyses that help project future outcomes. The goal is to learn from past mistakes and successes to know what to change and what to replicate.

Predictive analytics can be applied to all aspects of an organization. It can determine what customers want and don’t want and help a business maximize efficiency. It can help a company identify and deal with problems when they occur.

What is predictive analytics?

Predictive analytics uses artificial intelligence (AI) to make accurate predictions based on digital information. Advanced algorithms make connections between data points far more quickly and accurately than a human could, leading to reliable, actionable insights.

Eric Siegel, former Columbia University professor and founder of Predictive Analytics World conference series, defines the data analysis method as the power to predict who will click, buy, lie or die.

“Predictive analytics is the technology that learns from data to make predictions about what each individual will do – from thriving and donating to stealing and crashing your car,” Siegel said. “For business, it decreases risk, lowers cost, improves customer service, and decreases unwanted postal mail and spam.”

Key TakeawayKey takeaway

Predictive analytics uses digital data to offer actionable predictions to help businesses optimize their operations with minimal time, money and risk.

Predictive analytics tools and software

Applying predictive analytics to a business or organization requires specialized software. It’s offered by several vendors, including IBM, SAP and SAS. It crunches the collected data to determine the specific answers a business is looking for.

While each software offering has different capabilities and user interfaces, the premise is the same. They all work by first analyzing all the information a company collects. This includes sales and customer statistics, employee productivity, and social media data.

They then plug that data into predictive models. They can project future trends and problems based on that past behavior by using specially created algorithms.


Be sure to use relevant, accurate data with a considerable sample size. Otherwise, predictive analytics may not produce reliable insights.

These models can help businesses predict various consumer trends, as well as employee productivity shifts, to help drive supply and marketing decisions and improve efficiency.

While predictive analytics software used to be an option for larger organizations only, recent developments have made it more accessible to small businesses. This type of software, which is available from vendors such as Emanio and Angoss, is now sold at more affordable prices. It can be run from any personal computer instead of needing to be installed directly on a company’s server.

Examples of predictive analytics

Predictive analytics was originally used by large retailers and financial institutions. Today, businesses in every industry and of all sizes employ it to get a jump on the competition.

According to IBM, businesses can use predictive analytics in many different ways, such as these:

  • Uncovering hidden patterns and associations
  • Enhancing customer retention
  • Improving cross-selling opportunities through personalized offers and experiences
  • Maximizing productivity and profitability by aligning people, processes and assets
  • Reducing risk to minimize exposure and loss
  • Extending the useful life of equipment
  • Decreasing the number of equipment failures and maintenance costs
  • Focusing maintenance activities on high-value problems
  • Increasing customer satisfaction

For example, Sephora analyzes customers’ purchase histories and preferences to predict which products will most appeal to them. These tailored recommendations have led to 80% of its customers being completely loyal to the company. Similarly, Harley-Davidson uses predictive analytics to highlight potential high-value customers whom marketing agents and salespeople can target.

The popularity of predictive analytics with businesses has led to other types of organizations using the software. For example, healthcare firms use it to predict how certain drugs and therapies will be received by patients and help doctors better detect early warning signs for life-threatening diseases and illnesses.

Government bodies use predictive analytics software to help prevent crime, deliver social services and better serve residents. For example, more than two dozen U.S. cities use predictive analytics to determine where different crimes are most likely to occur. They then use this data to allocate resources appropriately, fighting crime while reducing costs.

Moving forward, businesses that don’t use predictive analytics software to drive their decisions will find themselves in the vast minority.

Pros and cons of predictive analytics

While predictive analytics holds vast potential, according to BDO Digital, just 19% of midsize companies are actively planning analytics initiatives. Part of that is because the technology comes with some potential downsides. Here’s a look at the benefits and drawbacks of predictive analytics today.


  • It provides actionable insights to help you get ahead of the competition.
  • It saves time that would otherwise be used for manual research and testing.
  • It can lower ongoing expenses through workflow optimization.
  • It may reduce wasted capital on ineffective marketing campaigns.
  • It becomes more reliable as time goes on.


  • It takes time to produce meaningful results.
  • It requires considerable data-gathering efforts and preparation upfront.
  • It may come with high upfront costs and initial disruptions.

Making the most of predictive analytics

Given the potential drawbacks, you need to apply predictive analytics correctly to experience its benefits. One of the most important considerations is to use reliable, clean data.

If these algorithms don’t have high-quality data, they won’t produce accurate results. Consequently, organizations believe that bad information is costing them $15 million a year in losses, according to research by Gartner. You can avoid this by collecting data from reliable sources and cleansing it before feeding it into predictive models. That includes verifying it against other sources, removing redundancies and standardizing its format.

As with any new technology, it’s also best to start small. You can minimize the initial expense and disruptions by applying predictive analytics to one area first, then slowly expanding it as your company learns to manage it. This will also help your employees understand how to work with these technologies more effectively.

Finally, you should regularly review your predictive analytics data to ensure it remains reliable. As situations change, algorithms will likely need tweaks and adjustments. Monitoring their performance can help your business experience the benefits without assuming too much risk.

Predictive analytics revolutionizing business

Predictive analytics has changed the way many businesses operate. Companies across virtually every industry have seen remarkable improvements after implementing this technology. It could become the norm as more people realize these benefits.

Like any technology, predictive analytics is not a cure-all. It won’t solve every problem a company faces, especially without careful planning and implementation, but it can offer substantial help. It will undoubtedly change the way business works.

Chad Brooks contributed to the writing and research in this article. 

Shannon Flynn
Contributing Writer at
Shannon Flynn is a writer who has spent five years covering all things technology, including business technology tools and software, cybersecutiry, IoT, cryptocurrency and blockchain. She is the Managing Editor at ReHack and a contributor at MakeUseOf, LifeWire and SiliconAngle.
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