How Predictive Analytics evolved

How Predictive Analytics evolved

From decoding war messages to fostering super personalized experiences

We all love to make predictions and when they come true (by chance), it brings a huge smile to our faces (and sometimes cash rewards too).

Human predictions, if driven by intuitions (not historical data), are good for fun and entertainment, but can’t be applied to businesses to make decisions affecting sales and revenues (risky, isn’t it?)

That’s where Predictive Analytics rises as a savior.

Predictive Analytics is the ability to forecast future outcomes and trends in real-time by extracting information from current and historical data sets such as customers’ personal information, browsing habits, and buying history.

It’s a mainstream AI application today that businesses widely implement to deliver super personalized experiences to customers across platforms.

The evolution of predictive analytics

However, the use cases of Predictive Analytics date back to the 1940s when a British scientist formulated a clever computer model to decode German messages during WWII.

Later in the 1950s, the use of predictive analytics was commercialized as companies started applying the tech for weather forecasting and solving transportation problems such as finding the shortest route in air travel.

In 1998, Moneyball showed us how Oakland A utilized computer-generated analysis of historical data to assemble a champion baseball team with a fairly low budget.

But, the biggest boom in predictive analytics happened when Amazon and eBay started using this technology to personalize online experiences for eCommerce customers.

And the emergence of Big Data, Machine Learning, and Natural Language Processing completely transformed how Predictive Analytics was perceived and used by businesses across the world.

The importance of predictive analytics in eCommerce

Today, not only enterprises but even small to midsize eCommerce stores can easily adopt or integrate AI-enhanced eCommerce solutions to leverage the benefits of proven machine learning models that can offer predictive analytics using your customer data.

This forecasting superpower enables you to reveal products and product categories a customer is interested in and, consequently, create highly personalized offerings instantly.

Additionally, these predictions allow you to segment and analyze customer behavior to detect potential churn, prevent losses, and offer business value.

So far, we have just tapped into some of the benefits that Predictive Analytics can bring to your business.

As the trilogy of AI, Machine Learning, and Big Data evolve, you will see path-breaking advancements that can shape the way people buy – online as well as offline.

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