And I’m not talking about reporting here. Sure, it’s interesting to know what happened in the past, and those monthly 50-megabyte Excel files might even get read once in a while, but the organizations that are using their data to look toward and predict future outcomes are leaping ahead and uncovering enormous value.

Now, don’t stop reading just because you don’t have a team of ex-NASA Ph.D.s on staff. While there is indeed some very sophisticated work going on in the data sciences these days, predictive analytics is something that’s within reach for just about everyone.

Predictive analytics is nothing more than a way to help identify the likelihood of future outcomes based upon historical data. It can be used to optimize just about anything you can measure or define, increasing your most important measures of success.

Predictive models are quite different from descriptive ones, which can tell you what happened in the past, or diagnostic models that help explain why something has happened.

If you’re a marketer, then you know the power of data. Never before have we had more access to more data than we do right now; and for many organizations, the struggle to collect it, integrate it and store it is challenge enough. But for those who can shift their focus to using that data to enable better decisions, it is an incredible competitive advantage.

Predictive analytics is very often used in better allocating marketing budgets. The latest tools and techniques, coupled with the swath of data being generated on every impression and click, provide a tremendous opportunity to get the most out of every marketing dollar you’re spending.

So, what are some of the applications of predictive analytics that you might be able to use right now? Well, here are five that can get you started in thinking about how you can use your data to boost performance across the different initiatives in your organization.

Audience Targeting. The “spray and pray” targeting tactic is becoming less and less useful as we gain more and more data about who is likely to become a customer and where we can find them.

Predicting the probability of an audience to become a customer — and the value he or she may provide over a lifetime — can identify the marketing dollars that are being wasted and focus on high value prospects.

Of course, if you’re investing in digital assets like websites and mobile apps, you’ll want to make sure you’re getting the most from them. Understanding what factors are likely to result in the best content, what areas can be customized to particular users and which areas of a digital experience are ripe for optimization can all be addressed through predictive analytics.

Content optimization. You’re spending time and resources to create, maintain and develop content, and you’ve likely got a lot of data around how it’s been performing.

Using that data to pull out the factors that are likely to result in success can help guide your content strategy so that you’re producing pages and experiences with a high likelihood of achieving your goals.

Personalization. When you combine digital experiences with customer data, you can start to segment and predict what groups of users are more or less likely to respond to different messages, offers, imagery and more.

Testing Strategy. While A/B and multivariate testing is certainly not a new phenomenon, the most difficult part of testing is figuring out what to test. Predictive analytics can help you understand what areas or processes of an experience are most likely to offer room for improvement, as well as help to define hypotheses.

Collection & Recovery. Making sure you’ve got a handle on accounts receivable has a direct impact on cash flows and an organization’s ability to operate.

Pricing. Putting a product out to market and maximizing value can be enormously influenced by price. Too high, and you risk acceptance and volumes; too low, and you sacrifice profitability.

Forecasting. Whether planning production runs, predicting demand for new products and services, estimating financial performance or anticipating hiring needs, historical data can be used to model probable scenarios and outcomes.

Network Optimization. Networks can mean a lot of things, including supply chains, fulfillment processes and just about anything else that has inputs, outputs and dependencies.