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What’s the Value of Predicting the Future?

The promise of an accurate prediction is incredibly intoxicating. What if you knew what would happen next? Knowledge of the future would definitely change the way you’d act in the present. You’d certainly approach decisions differently if you knew the outcomes in advance.

The value of understanding the future is high – and that holds true for businesses, too. And, while forecasting everything perfectly is impossible, we can use data to determine what is more likely to happen – and that knowledge can (and should) inform how decisions are made.

Welcome to the world of predictive analytics.

What is Predictive Analytics?

Analytics has been a big buzzword for a while now in the business world – but what exactly does it mean? And where do predictive analytics fit in?

To help clarify, let’s take a brief look at the four types of analytics:

  • Decision Analytics: This is the process of using functions to determine the values of decisions. The elements of a decision have already been discovered and the process helps determine which choice to make.
  • Descriptive analytics: This practice involves the analysis of data that has already occurred, and is used to describe patterns and relationships that have already occurred – think of reports.
  • Prescriptive analytics: Think Minority Report – people being arrested for crimes they haven’t yet committed based on how they’re predicted to act. Ethically, prescriptive analytics is troublesome because it assumes certainty of actions based on data.

And then there’s predictive analytics.

Simply put, predictive analytics is the technique of analyzing data to make predictions about the future. Unlike prescriptive analytics, it doesn’t prescribe definite action, but it does suggest likelihood. Technically, there’s a lot that goes into this – from data mining, to database management, to statistical modeling, and even machine learning – but it all comes down to using data to predict patterns.

How can senior marketers use predictive analytics?

So, predictive analytics are pretty cool – but how can businesses actually put them to a practical use?

Well, predictive analytics allow senior marketers to reach their audiences like never before – and, as you may have guessed, there are a lot of possibilities. Let’s take a look at three.

1. Predictive Analytics Can Help to Optimize Messaging

By predicting user behavior, businesses can reach individuals with messages that are most likely to resonate with their unique needs. You’re likely familiar with this concept, because major brands are undoubtedly using it to market to you.

There’s one famous story of extremely optimized messaging, involving a man who lived at home with his daughters. His household was receiving an inordinate amount of diaper coupons – so much so that the father actually called the store to complain, because he didn’t understand why his household would need diapers. As it turns out, one of his daughters was pregnant and hadn’t told him. The store had correctly identified her life stage using predictive analytics – even before she’d told her father.

Of course, this technique is everywhere online. You’ve probably seen Amazon ads in your web browser for products that are related to your last purchase, or Facebook ads that are related to your last profile update.

For consumers, this means receiving messages are actually relevant – and for marketers, it means the ability to create more engagement than ever before.

For example, you can actually run hundreds of tests on your website, and serve users specific content based on what audience segment they belong to, or even based on what their actions suggest they’ll be most interested in.

2. Predictive Analytics Inform Probability of Stakeholder Behaviors

The power of predictive analytics expands into more than just marketing, though; wherever prediction is helpful, analytics can be used.

For example, for one of our clients, we used a regression analysis to identify behaviors closely tied to the onset of early-stage dementia. Based on an analysis of a series of variables, we were able to determine that increased purchases of Pedialyte and dryer sheets closely correlate to the early stages of dementia.

That’s because people experiencing early-stage dementia more often forget to drink regularly, which results in increased consumption of Pedialyte. They may also neglect to bathe, so they’ll place dryer sheets under their clothes. These behaviors are early signals that can be used to predict future behavior – and without predictive analytics, they would have gone unnoticed.

This is obviously a fairly narrow application of predictive analytics, but the implications are broad. By understanding and predicting stakeholder behavior, businesses can begin to optimize everything from their internal processes and communication to the way they provide customer service.

3. Lead scoring

Finally, businesses can use predictive analytics to improve their efficiency in sales with a process called lead scoring.

Essentially, as contacts come in, businesses can use predictive analytics to identify which of those contacts will be more likely to make a purchase. Using factors like demographic data, company information, and user interaction with pages online, contacts can be assigned a lead score to help the sales team understand their readiness to buy.

And, thanks in part to the evolution of today’s marketing databases, even if a lead comes in with incomplete data, you can use a data append to fill in missing fields and make your picture more accurate.

At the end of the day, using lead scoring to inform your sales team is cost-efficient. It helps sales people to act based on data, and creates a higher probability of success.

What do I need to get started with predictive analytics?

As we’ve seen, the possibilities of enacting predictive analytics are pretty expansive, and that can make getting started a little intimidating. So, what exactly do businesses need to get into the world of predictive analytics?

Well, although there’s admittedly a lot of technical knowledge that goes into predictive analytics, at the foundation of the practice, you really need three things.

1. Data
Fortunately, there is a lot of data out there. If the data set you need isn’t accessible, though, you’ll need to go get it.

2. Tools to manage data
Today, there are a host of tools available to perform predictive analytics. On the web side, one of the leading options is Google Optimize, which has the ability to run hundreds of tests on a website and then serve users specific content based on the audiences Google determines that they belong to.

Aside from Google, though, there are plenty of additional options to assist in the management of data – from IBM’s SPSS software to RapidMiner.

3. People to manage the tools
Tools are great, but the reality is that you’ll have a ton of data coming in from all kinds of sources – and the usefulness of your algorithms and tools will be limited by the data sets they have access to.

Remember, the environment isn’t static. This is a constant process. Patterns and behaviors constantly change.

You need someone who is aware of all data – someone to aggregate and prepare the data set by removing duplicates, normalizing it, weighing outliers appropriately and ensuring that different sources are valid.

Often, intelligence is based on existing data sets – meaning the data is already out there, but it needs to be refined and analyzed. This is certainly true of the senior and mature markets. Having the right people to manage data ensures that you’ll be using the right data sets to make more accurate predictions.

Next Steps for Predictive Analytics

Hopefully, you’ve begun to grasp the incredible power of predictive analytics. The ability to predict likely patterns can be incredibly useful.

To get started with the data, tools, and people you’ll need to implement predictive analytics for your business, get in touch with us. At Immersion Active, our expertise in senior markets puts us in a unique position to offer insight – not just into how to implement predictive analytics from a technical perspective, but also into what insights you should be looking for in the first place.

Want to learn how predictive analytics could benefit your business, and what it might look like to get started? We’d love to hear from you. Get in touch with us online, or at 301-631-9277.

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