If you could have just one super power, what would it be? Some people might opt for superhuman strength, speed, or agility. Maybe you might want to have the ability to manipulate magnetic fields or to stretch your limbs in extreme ways. These may all be great, but arguably one of the most useful super powers you could have is the ability to see into the future.
Imagine if you had the ability to accurately foresee the winning numbers for tomorrow’s lotto drawing—and the week after that, and the week after that. While such abilities are probably only contained to the confines of comic books, we do have unprecedented access to increasingly advanced predictive analytics that really can “see” into the future and empower us to make more informed business decisions.
What is predictive analytics?
Put simply, predictive analytics describes a whole set of statistical techniques and analyses that can make predictions about the future. Predictions are based on both current trends and historical facts; data is analyzed with machine learning and data mining to make the most accurate predictions possible.
Traditionally, predictive analytics has been the job of human analysts who manually go through mountains of data. You’ve likely either read or heard news stories containing quotes from analysts as they pertain to a variety of industries. For example, analysts in the technology field may release predictions of the next iPhone—how it might look or when Apple will formally announce it.
The biggest challenge is we are still relying on humans, who are understandably very expensive and comparatively very slow. By leveraging the power of machine learning for predictive analytics, predictions can be made much sooner and more accurately, too. Also understandably, there are some very big players in this space with very deep pockets to invest in this technology.
IBM offers services that can “analyze big data to gain predictive insights and build effective business strategies.” This is partly fueled through model building that, in turn, is based on information gathered through data mining. Another major player is SAS, a company that specializes in analytics and business intelligence. It’s developed increasingly complex algorithms that can “identify the likelihood of future outcomes based on historical data.”
According to SAS, some of the most common uses for predictive analytics include fraud detection (it can be used to spot abnormalities that may be indicative of fraud, for instance), marketing campaign optimization, operation improvement, and risk reduction. Another use is identifying and testing opportunities to upsell or cross-sell to customers. For example, if customer A is browsing the page for product X, predictive analytics and machine learning can help to determine whether it is more advantageous to promote product Y or product Z for the best shot at conversion.
This isn’t quite the same as peering into the future, per se. Rather, predictive analytics can be used to determine which data is the most relevant to your current goals and objectives, crunching through that data to arrive at different probabilities. It might not occur to a human analyst that interest in a certain genre of entertainment could predict whether a person is more likely to be on an iPhone or Android, but machine learning and predictive analytics may reveal that relationship with a certain level of confidence.
By taking “big data” into account and weighing through innumerable variables that might be related to your desired outcome, predictive analytics can enter that information into an algorithm or equation to extrapolate the best marketing strategy for your business to optimize subscriptions or conversions.
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But what about the rest of us?
You’ve certainly seen examples of predictive analytics at work in your day-to-day activities on the internet; you just might not have known they were happening, because they’re typically seamless and behind-the-scenes. This is particularly true for big corporations and organizations that can invest large sums of money into predictive analyses, but not everyone has that kind of budget.
But just as advances in technology are leveling the playing field in other ways, Endor, an Israeli predictive analytics company, could be helping deliver predictive analytics to the masses, too. The vision for Endor is to “make artificial intelligence predictions accessible and useful to all,” and not just to a select few with deep pockets. Remember what it was like trying to find information on the internet before search engines arrived on the scene? Even early search engines weren’t so great, but Google was a total game changer for most of us.
Suddenly, it became easier to find almost anything we needed to know on the web. Endor aims to have the same game-changing impact on the world as it strives to be the “Google for predictive analytics.” It works in fundamentally the same way as other systems, but it is able to provide “automated accurate predictions, fast, with no data science expertise required.” In short, it makes this sort of technology accessible to everyone by reinventing what predictive analytics and machine learning are and can be.
Get the answers you need
As Endor explains, despite incredible advances in predictive analytics, the current process takes a lot of time and money, with only limited access to data. A model must be created for each prediction, and this can take a couple of months. In addition, the scale is limited and expensive.
Endor, on the other hand, is powered by social physics technology from MIT. Combined with massive computing power, it can empower business users—even small business owners—to ask predictive questions in plain language, and receive answers in just one day. In fact, it can receive up to 20 predictions a day for even better scalability.
Who will most likely purchase product X when they’re sent a marketing message via SMS? Which demographic should be targeted if we want to increase our average revenue per user? Which promotional gift would be most effective in converting browsers into subscribers?
Through a combination of private proprietary data and public shared data, your business can cash in on the “long tail” of the market in powerful (and profitable) new ways. And as data management and tracking companies grow, all of its users can benefit from the accumulated wealth of data and experience the engine develops. In the end, this means more efficient and more effective decisions for all who leverage the power of predictive analytics for their business strategy.