You ready yourself for an oncoming storm because predictive analytics advised you it was approaching.

 

You stand at bat against a pitcher and swing at a certain area of the strike zone because predictive analytics advised you that’s where they’re most likely to throw.

 

You defend a basketball player and force them to drive left because predictive analytics advised you that’s where they’re weaker.

 

Predictive analytics are integral to providing a company, an athlete, or a storm-prepper with crucial info to get a forewarning. You use them “to identify the likelihood of future outcomes based on historical data.” Without them, you’re preparing with the storm on the horizon or guessing your way through at-bats and defensive possessions.

 

They’re a necessity in a digital marketing, where success is contingent on analyzing data, before, during, and after a campaign.

 

All data analysis begins with predictive analytics; targeting groups based on variables, predicting customer behavior, and recommending certain products and services they’d be most prone to buying.

 

This is the most important segment of the analytical stage. It’s how and where you find your audience. You can have the Ernest Hemingway of copy and the Basquiat of graphic design on content. It won’t produce nearly the same results without segmenting, predicting, and filtering beforehand.

 

Utilizing predictive analytics is where retail giants like Amazon and eBay excel. They target groups based on numerous variables, including behavioral clustering, product-based clustering and brand-based clustering.

 

From there, they evolve from the segmenting phase to the prediction phase, utilizing propensity models. This is where customer behavior is predicted; variables such as engagement likelihood, and their propensity to unsubscribe, convert, or buy.

 

Then begins the filtering phase. This is where eBay and Amazon earns their notoriety. They’re always seem to know just what you want to buy and when you need it. They know this because of your past buying behavior. It allows those retail giants to predict what you’re likely to buy next will be in the same vein.

 

And it works.

 

It comes down to understanding people:

 

“Knowing the customer type or behavior you want to replicate, the predictive modeling starts with a sample of the consumers you want more of, otherwise known as seed. The predictive model is then able to create an audience that is tailor-made to your business and objectives.”

 

To reach the point of understanding your customer’s behavior, your predictive model must first identify “consumers based on who they are rather than exclusively focusing on a recent behavioral signal, thus exponentially expanding your pool of potential prospects”, based on the predictive model.

 

Furthermore, “predictive modeling evaluates all available data to classify the relative importance of each point in identifying your target audience. The resulting formula pinpoints which consumers to target, allowing you to capitalize on both scale and precision.”

 

The underlying current of predictive analytics is tracking the online behavior that takes them from point A to B. It’s focused on pinpointing who’s most likely to buy, when they are at their most willing to buy, what product or service they’re most likely to buy, and what’s going to prompt them to buy.

 

Even more important, however, is differentiating between high-value customers and those you might suspect of just browsing. Again, predictive analytics can aid in qualifying and prioritizing leads based on their likelihood to take action.

 

This is possible by “identifying and acquiring prospects with attributes similar to existing customers”. If your online patterns and behaviors are similar to the majority of customers on that website, you’ll be treated as a high-priority lead.

 

Here’s an example from Marketing Land on how this works:

 

“Applying predictive and analytics on a range of digital and offline data sets, we were able to identify just how valuable different online behaviors were to an offline, in-store transaction and activation later in the purchase cycle.

The data told a story with many elements we might have expected: Add-to-cart actions and beginning a checkout process were indeed predictive of an impending offline purchase, and locating a nearby store also showed up as an action predictive of purchasing intent. But browsing device galleries and using the chat feature were among the more valuable actions, and the single most important factor in purchase intent was interacting with the current special offers.”

 

Sounds like a science experiment, right? You lay out a couple of variables that act as triggers for your candidates and then wait for the results to play out. From this particular experiment, it was clear that offers were the trigger that turned the most potential customers into actual customers.

 

Notice how many variables were weighed as well. It goes so much further beyond whether or not a potential customer clicks through your ad. It comes down to what type of ad they’re clicking on, what they’re leaving behind in their cart, how far they went out in the checkout process, what they were browsing, and if they were using the chat feature.

 

The analysts went as far as tracking if their candidates were searching for stores nearby.

 

Ushering your potential customer is a delicate process that requires extremely precise timing, a task which links back to customer segmentation and leads to personalized messaging.

 

Predictive analytics also greatly assists in the fact that “73% of consumers prefer to do business with brands that use personal information to make their shopping experiences more relevant.” So not only are you helping yourself in the long run, you’re also assisting in directly getting sales through re-targeting efforts.

 

In fact, personalization overall greatly assists in drumming up more sales:

 

  • “86% of consumers say personalization plays a role in their purchasing decisions”
  • “45% of online shoppers are more likely to shop on a site that offers personalized recommendations”
  • “40% of consumers buy more from retailers who personalize the shopping experience across channels”
  • “80% of consumers like when retailers emails contain recommended products based on previous purchases”

 

This shouldn’t be surprising. At every juncture of an Amazon transaction, the website is listing ‘Top Picks for You’ or ‘Recommendations for You’ or ‘Customers who bought this also bought…'”. All of these tactics are naturally going to elicit more orders. Your interest is already piqued in your purchase and you’re likely excited about it, too.

 

It’s kind of like a checkout line at a grocery store. You think you got everything, but don’t you need some gum once you finish eating? And how about one of those magazines with the big headlines to relax with after?

 

Those weren’t put there by accident. Stores analyze their customers’ buying habits to see what they were buying at the end of a checkout line. Just like Amazon and eBay places certain recommendations before, during, and after your transactions, it’s all based on using predictive analytics to forecast what you’re most likely to buy along with that item.

 

In the same vein as any marketing agency’s work, predictive analytics is utilized to get that extra sale that would have never been found without discovering who your customer is and how they behave beforehand.