A common scene plays out day after day in companies across the globe: The sales team grumbles that it’s being hindered by a lack of effective marketing, and the marketing department argues that it lacks fresh, comprehensive data from which to work.

Big data is only as useful as the opportunities your organization can leverage from it. Today’s businesses collect an unprecedented amount of data on their current and potential customers, but how much of it can be converted into increased revenue? According to the International Data Corporation research firm, big data and analytics are set to drive global business revenues to $187 billion in 2019, an increase of 50 percent in 2015.

Predictive analytics plays a large role in this growth. It works by helping companies utilize their data more effectively, allowing them to use information about consumers’ previous actions and behaviors to learn:

  • Who will be most interested in their offerings.
  • What they’re likely to buy.
  • Where they’re making purchases.
  • When they’re most likely to make a purchase.
  • Why they’re motivated to buy.
  • How much they will spend.

Companies are being driven to not only understand customer behavior, but also harness this data into a strategic, comprehensive plan. In response, an increasing number of brands are looking to predictive analysis to boost their bottom lines. Why now? Because analytics has established itself as the new differentiator for businesses.

What Is Predictive Analytics?

Predictive analytics is the use of data, statistical analytics and machine learning to assess the probability of future outcomes based on data from the past. These models use known results to develop an algorithm that renders predictive scores — the golden eggs of predictive analytics.

These scores are numerical representations of the likelihood that a customer will have a particular interaction with a company, such as clicking an ad or making a purchase, thus informing the organization of the most effective course of action to take with that customer. It is the discovery and communication of these meaningful patterns in data that can positively affect your business at the corporate, product, customer and channel level.

To be clear, predictive analytics can’t divine exactly how a consumer will respond to a particular marketing campaign or piece of sales collateral. Instead, ever-evolving statistical models identify and track patterns in consumer demands and behaviors and then calculate the likelihood of various outcomes. Predictive analytics provides businesses with highly accurate forecasts, allowing for more effective decision-making and resource investment.

Applications of Predictive Analytics

Predictive analytics can optimize central business functions across marketing, sales and beyond, both online and off. The most common uses for predictive analytics include:

  • Marketing: Determining customer behavior and purchase patterns to attract and retain the most fruitful customers and make the most of marketing spending.
  • Cybersecurity: Detecting fraud before it occurs by using functions such as business rules, anomaly detection and link analytics.
  • Risk analysis: Credit scoring and assessing buyers’ likelihood of default.
  • Inventory management: Examples include airlines using predictive modeling to decide the price and availability of tickets and hotels predicting the number of guests to expect on any given night and adjusting rates accordingly.

The core difference between each mode of application is what’s being predicted. Which application is best for your business is a strategic question that depends on which predictive scores will best serve to drive decisions within your particular organization.

But beyond these basics, predictive analytics can also help your business overcome a few key challenges.

Eliminating Disparate Data

A common scene plays out day after day in companies across the globe: The sales team grumbles that it’s being hindered by a lack of effective marketing, and the marketing department argues that it lacks fresh, comprehensive data from which to work. This leaves IT holding the bag, responsible for shortcomings that are often outside the scope of the role.

However, the actual culprit usually isn’t a dearth of data. It’s actually how the information is being shared within the organization. In many cases, various pieces of customer data are “owned” by any number of departments in separate data silos throughout the company. This setup makes it nearly impossible for any one department, such as marketing or sales, to get a holistic view of customers’ previous, and future, activities. Predictive analytic systems are designed to connect these disparate collections of information, becoming a key solution to this age-old aspect of data-driven organizations.

Increased Cost-Effectiveness

Tougher economic conditions and heightened competition are also pushing organizations toward solutions that can convert their growing volumes of data into solid predictions and actionable insights with the least amount of resource expenditure. In short, today’s marketing solutions must be leaner and meaner.

Predictive marketing algorithms allow companies to target consumers with laser accuracy, turning shoppers into buyers and buyers into loyal, lifetime customers. Predictive analytics shortens and sharpens the sales cycle and increases your company’s cross- and up-selling opportunities. By fully embracing predictive analytics, your organization can predict trends and reveal insights that help you better understand your customers, improve business performance and drive strategic decision-making — all without increasing the budget. The diminishing cost of predictive analytic technology and related tools such as cloud storage add to these savings. Eighty-six percent of executives acknowledge a positive ROI after implementing predictive analytic technology and nearly half report at least a 25 percent ROI increase, facts that speak volumes about the powerful affect these tools have on your bottom line.

Reporting on what is happening in a business right now, in real time, is only the first step to making better business decisions. Organizations must be willing to spend time changing both their business processes and the technologies underpinning them to take full advantage of the new insights predictive analytics delivers.