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What Is Predictive Analytics and How Can It Help You?Reading time 13 minutes
Organizations have more data under their control than ever before. But as the Internet of Things starts growing and 5G networks come online, the age of “big data” might end up seeming relatively small in comparison. According to IDC, the world will have 175 zettabytes of data by 2025, up from the 33 zettabytes of data that exist today.
At the same time, today’s customers have more options at their disposal than ever before. Following a bad experience with a brand, they can easily start looking for a substitute. All it takes is a quick Google search. And that’s exactly what many of them do. In fact, customers are four times more likely to stop doing business with a company following a bad experience.
Suffice it to say that, organizations have less room for error than ever before. Accordingly, they can’t afford to make any unforced errors.
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This is where all of the data comes in handy. While every business will make mistakes every now and again, every business should strive to minimize those mistakes. What’s more, they should also strive to ensure exemplary customer experiences in every interaction.
The good news is that, thanks to an emerging practice called predictive analytics, it’s easier than ever for companies to anticipate the future. As a result, these predictive capabilities enable them to increase the likelihood they are meeting customer expectations while preserving and improving the customer experience.
Before we dive into exactly why that is, let’s take a step back and get our definitions straight.
What Is Predictive Analytics?
In a nutshell, predictive analytics is the practice of analyzing data in order to predict future events. With that information on hand, organizations can figure out better ways to serve their customers. What’s more, they can also determine how many items to keep in inventory and even detect fraud as it’s occurring, among other things.
Predictive analytics combines several data analysis techniques, including machine learning, data mining, statistics, and artificial intelligence. These are just a few of the practices required to analyze data and develop an understanding of how past actions and behaviors can impact future outcomes.
In the business world, predictive analytics is rapidly gaining momentum. That’s because when businesses execute predictive analytics effectively, they become more agile, more efficient, and most importantly, more profitable.
The State of Predictive Analytics: Big Demand, Low Adoption
So, predictive analytics sounds pretty great.
But how many companies are actually using this relatively new form of analytics?
Not many. According to a recent study, only 23 percent of businesses are deploying predictive analytics. What’s more, 26 percent of businesses have no intention of using predictive analytics anytime soon.
These numbers might surprise you. So, let’s take a look at some of the obstacles that are preventing predictive analytics from entering the mainstream.
As we mentioned earlier, companies have more data under their control than ever before. In the age of cloud computing, they’re able to collect and store more data than ever before. As a result, they have much more data to run through analytics programs to answer questions and discover new opportunities.
While you might think that more data means better analytics, that’s not always the case.
In reality, few companies are able to successfully make sense of all the data they’re collecting. In large part, that’s because they cannot interpret it because the technology required to do so is complex and resource-intensive. As such, it’s out of the reach of most mid-market businesses.
Beyond that, as you might have heard, many organizations also struggle with data silos. A company might have the perfect data set. But if it’s hidden away in some unknown repository, what good is it?
Still, shrewd companies are moving forward with predictive analytics. In fact, that same study indicates that both large and small companies are most likely to use predictive analytics.
This is perhaps due to the fact that larger companies have more resources to invest. On the other hand, smaller companies might have an easier time using predictive analytics because they have fewer employees and fewer customers—and therefore much less data.
How Does Predictive Analytics Work?
Predictive analytics begins with a predictive model that uses an understood outcome (for example, a customer purchase) to determine what might happen in the future. This model inspires the development of a new model—one that might predict future customer purchase behaviors, for instance. Next, the model is used to predict future outcomes in relation to additional input variables (such as time of day or weather).
Now that you understand the basics of how predictive analytics works, let’s take a look at some of the more popular types of techniques and models:
1. Decision Trees
A decision tree is a visual chart that resembles a tree. It demonstrates every potential outcome of a decision.
While decision trees are often used in business and everyday life, they can answer much more complex questions in the field of predictive analytics.
For example, a business might try to figure out when the best time to launch a new product line is. They might also want to know how much to sell it for and what market to sell it in. Based on these factors, a decision tree can convince them to sell product x in market y at price point z.
2. Regression Algorithms
A regression algorithm can predict a numerical value—like how many days will pass before a customer makes a repeat purchase. Regression algorithms can also be used to guess how much money a customer will likely spend on a future purchase or over the course of a certain period of time (e.g., six months).
Linear regression is a popular regression technique that detects patterns between two variables. Specifically, linear regression can be used to predict the future value of a target variable, such as customer spending as it relates to how much time is spent in a store.
3. Neural Networks
Neural networks—also known as Artificial Neural Networks (ANN)—are highly advanced data processing techniques modeled after the human brain. ANNs are behind today’s most advanced technologies, including facial recognition and text-to-speech software.
It remains to be seen what new use cases neural networks will introduce in the coming years. But the technology is still in its infancy. As such, we can expect a number of incredible applications to emerge as we move further into the future.
4. Classification Algorithms
Classification algorithms predict whether the subject in question is a member of a specific group or not. For example, a classification model could be used to predict whether a website visitor is going to be an immediate buyer or are they just browsing.
As such, classification algorithms can help businesses operate more efficiently. On one hand, that could mean devoting human resources effectively. On the other, it could mean making sure enough computing resources are available for specific applications.
What Are the Benefits of Predictive Analytics?
Businesses are racing to implement predictive analytics because it provides a number of benefits that fast-moving organizations can’t live without.
Let’s take a quick look at some of the more compelling ones.
1. Lower Costs
Businesses that can predict customer demand more accurately can reduce costs by having the right amount of inventory on hand. Furthermore, when a business understands who its ideal customer is, it can use that data to better target its marketing campaigns toward that ideal customer.
Beyond that, companies can use predictive analytics to lower their e-discovery costs for future litigation. For example, a predictive model can help organizations determine which documents are most likely to be needed in future legal cases. As a result, management could determine which documents to keep.
At the same time, they could also figure out which documents they could get rid of. With fewer documents packed into repositories, discovery would be much smoother. Clutter causes fatigue, after all.
2. Better Fraud Detection
In an age of ever-increasing cybersecurity threats, predictive analytics helps businesses detect malicious activity across its web platforms. Without delay, businesses can react to these threats and protect themselves before damage is done.
This is a major deal. One recent study found that the average data breach costs $3.9 million, and few businesses have that kind of cash to spare. What’s more, no business wants to endure the massive amounts of negative PR that stem from getting hacked.
Add it all up, and predictive analytics can save a considerable amount of money while keeping customer data security and brand integrity intact.
3. Increased Efficiency
Predictive analytics can also be used to increase business efficiency. For example, an airline can use predictive analytics to optimize ticket prices based on anticipated demand. By analyzing seasonal and historical data and considering other influential factors (e.g., the economy and the weather), the airline would have a fairly solid idea of what the busiest times of the year are.
Further, the airline could also use predictive analytics to have a better idea of what wait times and arrival times might look like. This could help them deploy their assets optimally.
Better Customer Understanding Means Happier Customers
A business that can predict what customers want can have their products ready for their clientele the moment those folks want them. As a result, businesses don’t have to worry about having to turn any sales away. And on the flip side, since they bought what they wanted to buy when they wanted to buy it, customers are more likely to buy again.
Real World Examples of Predictive Analytics
When you think about the term predictive analytics, it might be hard to think of a real-world example that impacts your everyday life.
Good news: You’ve come to the right place.
By the time you finish reading this piece, you will know what predictive analytics is. In fact, you could even be the center of attention at every cocktail party you go to as you opine about this cutting-edge technology and tell unsuspecting souls how it relates to their lives.
Without further ado, here are four real-world use cases for predictive analytics that affect most of our lives.
1. Netflix’s Recommendation Engine
In 2009, Netflix offered $1 million to any development team that could improve its recommendation engine by 10 percent. It’s safe to say that, at the time, Netflix considered solving the predictive analytics puzzle a big deal.
Fast-forward to today, and Netflix is now able to collect more data than almost any other business. Since the company has over 150 million subscribers spread out across the world, this should come as no surprise.
Each time a subscriber interacts with the Netflix platform, their data is continuously fed into Netflix’s personalized recommendation engine. When a user watches content, whether in full or partially, Netflix logs that data and uses it to inform their recommendations. If someone watches Happy Gilmore, for example, the engine might suggest the user watch movies starring Adam Sandler or other comedies next.
Simply put, Netflix’s goal is to better predict which videos its subscribers will enjoy. And it wants users to find content they will enjoy as quickly as possible. When customers don’t have to spend a seemingly endless amount of time browsing Netflix’s library, they’re happier. It sure beats curling up with your loved one to watch a movie only to find yourself scrolling through titles for an uncountable amount of time and arguing over what’s good and what isn’t.
(Of course, we’re just speaking hypothetically here.)
On the other hand, failure to understand what else your customers might like can come at a high cost. In the age of Netflix and Amazon, customers have grown to expect personalized recommendations. Businesses that aren’t capable of offering similar experience can very well turn customers away, as customers will be frustrated.
When it comes to collecting data to use for predictive analytics, Netflix unquestionably has one of the most ideal platforms in existence today.
2. Estimated Arrival Times on Your Phone
Another real-life example of predictive analytics is found in the navigation devices you use everyday. For example, your car’s GPS system, your smartphone’s navigation app, and your favorite airline’s in-flight computer system (assuming you’re a regular jet-setter).
When any of these devices offers a prediction of your arrival time, that’s predictive analytics in action.
Smartphones, for example, continuously analyze incoming data in real time. They measure things like speed, traffic conditions, and the speed of other vehicles. Taking these different variables into account, their prediction engines determine when you’ll arrive at your destination. And with a pretty high degree of accuracy to boot.
3. Better Healthcare Outcomes
The healthcare industry is already using predictive analytics to improve health outcomes. As time goes on, this practice will become even more common.
According to Deloitte, there are three main benefits to using predictive analytics in healthcare are as follows:
- Improving healthcare operational efficiency
- Improving the accuracy and diagnosis of patient treatments
- Improved insights into cohort treatment
Who knows? Predictive analytics may end up being one of the major drivers of affordable healthcare. With the right tools in place, providers can detect patients who might be more susceptible to certain diseases. Then, they can take proactive steps to prevent them from developing those conditions.
More Effective Law Enforcement
Police departments across the world can also benefit from predictive analytics.
In fact, a recent study by the Rand Corporation suggests that predictive analytics can help law enforcement officers in the following areas.
1. Crimes That Might Happen
Predictive analytics can be used to predict the probable locations and times of crimes. With this information, police departments can ensure officers are positioned nearby, ready to respond in the event a crime occurs.
2. People Who Might Break the Law
Predictive analytics can be used to identify who might be more susceptible to committing a crime in the future
3. Creating Perpetrator Profiles
Predictive analytics can use crime data to develop perpetrator profiles. These profiles can then be matched against likely offenders that have committed similar crimes in the past.
4. People Who Might Be Victims of Crimes
Using a range of variables—such as location, age, and gender—predictive analytics can help departments forecast who is most likely to become a crime victim. They can use this information to protect these vulnerable citizens.
Is It Time to Give Predictive Analytics a Spin?
Companies can’t make the best decisions when they’re basing them on gut instinct.
The good news is that, In today’s data-driven word, you don’t have to.
Nobody knows what the future holds. But with predictive analytics, we can make a good guess at what tomorrow might look like.
To learn more about how your organization can use predictive analytics to work smarter, look into some of the tools available from Plutora.