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Keeping the Pace of Real-Time Marketing in The AI Era

September 06, 2018
4 min reading time

Modern consumers have evolved to anticipate instant gratification.

Whether we’re scrolling through our social media feeds, shopping online, or checking the news, we assume that what we’re looking at is immediate and relevant. And when something no longer meets our present needs, we’re on to the next thing.

Until recently, the standard marketing practice was to recommend products using pre-configured rules gleaned from user data and demographics. While this method was cutting-edge 10 years ago, advances in cloud infrastructure, machine learning, and data processing have far surpassed what was possible in the past.

Machine learning and cloud computing can now deliver the horsepower necessary to determine consumer patterns in real time.

Thus, delivering real-time personalization at scale is a possibility.

Here’s a breakdown of the new marketing landscape.

Machine learning technology holds consumer attention.

A recent study found that 90% of Americans use multiple electronic devices per day. In fact, many are toggling between screens simultaneously. They’ll check Instagram while watching TV, or shop online while texting.

This means marketers can and should engage with users across multiple platforms, from web to email to push notifications. Marketers are now able to reach potential customers on their phones or their computers, and even on airplanes, when they’re 30,000 feet in the sky.

“Omni-channel marketing” is the buzzword marketers use to describe this approach. While being in an omni-channel world means that marketers now have more ways than ever to reach customers, it also puts increased pressure on marketers to provide a seamless user experience across all of those channels.

The fact that people are constantly switching devices means it has never been harder to hold consumers’ attention.

Marketers need to move from asynchronous-based marketing strategies — such as mass email and re-targeted ads — towards more real-time personalized communication with customers. At LiftIgniter, for example, we use machine learning technology that observes users’ actions and behavior in the moment, capturing the subtle cues as they click from one item to another and as their wants and needs shift. We then use these cues or signals to provide recommendations for what is relevant to the user in the here and now.

To keep customers’ interest in an omni-channel world requires new strategies that prioritize personalization.

Evolved marketing tools imitate real-life experiences.

Even as consumers turn from physical to online retailers, digital marketers can benefit from looking to real-life shopping experiences. Some of the best marketing tools today create a digital experience that closely imitates reality.

Let’s say you walk into a store without a clear decision on what to purchase. When you walk into that store, the clerk makes an initial assessment of what you might need based on external clues. The clerk doesn’t ask you to fill out a piece of paper with your preferences. Instead, he or she might note that your shoes look a little worn and suggest a few options.

Rather than relying on declared preferences or on those tracked by activities across multiple stores, the clerk can look at you and perceive, understand, and read your body language.

Similarly, a customer today might buy himself a tie from JCrew.com, and then quickly shift to buying his son an Xbox on the same browser. Based on the latter purchase, older marketing strategies would start sending him lots of targeted ads about gaming, which would be completely irrelevant to his present interests.

More refined and newer approaches instead focus on increased personalization to zone in on what matters to each customer in the moment.

Machine learning technology provides a nuanced approach to recommendations.

Digital tracking has historically been unable to provide a real-time perception of consumer needs. Until now, tracking was mostly focused on where the customer has been by using previously sourced data, like browsing history, psychographic and demographic data, and previous purchases.

While user segmentation is certainly still relevant, because of the ever shortening attention span of consumers, it’s even more crucial for marketers to have a more in-the-moment snapshot of their users, and to be able to predict where they’ll be in the future.

In a brick-and-mortar store, a clerk can suggest items that you may need in the future based on what’s happening that moment. For instance, an umbrella when it’s about to rain.

The aim of today’s most successful customer-centric companies’ emerging digital marketing technology is to be similarly predictive.

Machine learning technology allows for a more nuanced approach to recommendations, because you no longer need a user’s entire history in order to engage with them. AI can pick up on very subtle signals from a user, enter them into that calculus, and imitate or create a digital user experience relevant to their present physical reality.

In today’s competitive market, pre-programmed rules no longer cut it. Customers expect an experience relevant to each passing moment. Anything less, and they will move elsewhere.

This article first appeared on the Deep Tech Medium Blog.

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