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Updated May 24, 2024

What Is Predictive Analytics?

Learn the pros and cons of predictive analytics and tips for how to use this technology for your business.

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Shannon Flynn, Contributing Writer
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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 impact the business’s future. Predictive analytics is one strategy companies use to harness data to improve business operations. 

Predictive analytics can be applied to all aspects of an organization. It can help you manage customer relationships and maximize business efficiency. Plus, it can help companies identify and deal with problems when they occur. Read ahead to learn the ins and outs of predictive analytics and how applying this technology can help your business thrive. 

What is predictive analytics?

Predictive analytics is a business analytics method that 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, author, former Columbia University professor and founder of the 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 explained. “For business, it decreases risk, lowers cost, improves customer service and decreases unwanted postal mail and spam.”

Did You Know?Did you know
Predictive and prescriptive analytics both provide valuable insights. Predictive analytics uses digital data to offer actionable predictions while prescriptive analytics helps you draw specific recommendations.

Predictive analytics tools and software

Applying predictive analytics to a business or organization requires specialized software. Several vendors offer it, including IBM, SAP and SAS. The software crunches the collected data to determine the specific answers a business seeks.

While each software offering has different capabilities and user interfaces, the premise is the same:

  • They analyze a company’s data, including customer relationship management analytics, customer statistics, sales analytics, employee productivity data, social media data and more.
  • Next, they plug that data into predictive models and project future trends and problems based on past behavior via specially created algorithms.

Predictive analytics models help businesses predict various consumer trends and shifts in employee productivity to drive supply and marketing decisions and improve efficiency.

Predictive analytics software was once a server-installed option only for enterprises. However, it’s now more accessible and affordable for small businesses from vendors like Emanio and Angoss and can be run from desktop computers.

TipTip
Use relevant, accurate data with a considerable sample size. Otherwise, predictive analytics may not produce reliable insights from the business data you collect.

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 gain an advantage over the competition.

Businesses can use predictive analytics to do the following:

  • Uncover hidden patterns and associations
  • Enhance customer retention
  • Improve cross-selling opportunities through personalized offers and experiences
  • Maximize productivity and profitability by aligning people, processes and assets
  • Reduce risk to minimize exposure and loss
  • Extend the useful life of equipment
  • Decrease equipment failures and maintenance costs
  • Focus predictive maintenance activities on high-value problems
  • Increase customer satisfaction and better manage customer relationships

For example, Sephora’s data-driven approach analyzes customers’ purchase histories and preferences to predict which products will most appeal to them. These tailored recommendations have led to 80 percent of its customers staying loyal to the company. Similarly, Harley-Davidson uses predictive analytics to highlight potential high-value customers for marketing agents and salespeople to target.

The popularity of predictive analytics with businesses has led to other types of organizations using the software:

  • Healthcare firms use predictive analytics to predict how patients will receive specific drugs and therapies.
  • Medical practices use predictive analytics to help doctors detect early warning signs for life-threatening diseases and illnesses better.
  • Government bodies use predictive analytics software to help prevent crime, deliver social services and better serve residents. For example, more than two dozen United States cities use predictive analytics to determine where different crimes are most likely to occur. They 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.

TipTip
AI is improving human resources processes by driving predictive analytics algorithms that analyze resumes, pinpoint ideal candidates and help hiring managers make better decisions.

Pros and cons of predictive analytics

While predictive analytics holds vast potential, it has yet to be adopted on a large scale. Part of that is because the technology has some potential downsides. Here’s a look at the benefits and drawbacks of predictive analytics.

Pros of using predictive analytics

  • Provides actionable insights to help businesses gain a competitive edge
  • Saves time that would otherwise be used for manual research and testing
  • Can lower ongoing expenses through workflow optimization
  • May reduce wasted capital on ineffective marketing campaigns
  • Becomes more reliable as time goes on

Cons of using predictive analytics

  • Needs time to produce meaningful results
  • Requires considerable data-gathering efforts and preparation upfront
  • May come with high upfront costs and initial disruptions

Tips for using predictive analytics in business

Given the potential drawbacks, you must apply predictive analytics correctly to experience its benefits. Here are some tips: 

  • Use accurate and updated data: Using reliable, clean data is crucial to gaining actionable insights from predictive analytics. If the algorithms don’t have high-quality data, they won’t produce accurate results. Bad information costs organizations up to $12.9 million a year. Avoid these losses by collecting data from reliable sources and cleansing it before feeding it into predictive models. Ensuring accurate data includes verifying it against other sources, removing redundancies and standardizing its format.
  • Test predictive analytics models: As with any new technology, it’s best to start small. You can minimize initial expenses and disruptions by applying predictive analytics to one area first, then expanding it slowly as your company learns to manage the technology. This approach will also help your employees understand how to work with these technologies more effectively.
  • Conduct regular quality assurance checks: Review your predictive analytics data regularly 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 of predictive analytics without assuming too much risk.
Did You Know?Did you know
If you want predictive analytics efforts to succeed, you need top-notch talent. Assemble a resilient and adaptable team of data science and machine learning experts who understand your objectives and can interpret your results clearly.

Predictive analytics is revolutionizing business

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

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.

Natalie Hamingson contributed to this article.

author image
Shannon Flynn, Contributing Writer
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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