Getting Personal with Machine Learning: New Tech & Advanced Segmentation

Here at Clutch, we spend a lot of time thinking about what customers want. Competitive pricing? Of course! More rewarding loyalty programs? Definitely! A seamless omni-channel experience? That, too! More than anything else, however, customers want to feel like they have a personalized relationship with your brand.

A study conducted by Salesforce revealed that 84% of customers say a brand wins their business by treating them like a person, not a number. The same survey showed that customers are 2.1 times more likely to see personalized offers as important. In addition, 59% of those surveyed said tailored engagement was key to winning their business. Those statistics clearly show that companies need innovative ways to make their customer communications individualized and responsive. Luckily, advanced segmentation techniques, supported by increasingly sophisticated machine learning and artificial intelligence, are helping businesses create customer experiences that are more personalized and effective than ever before.

What IS Segmentation?

Before we dive into the latest technology, let’s take a step back with a quick definition. Segmentation is the practice of dividing customers into groups based on common attributes, such as demographic data, shopping behaviors, or prior interactions with your brand. Marketers can send tailored messages to the members of each group, and drill down into increasingly specific segments using combinations of various attributes. When used effectively, segmentation allows businesses to achieve the kind of personalized, responsive marketing that modern customers demand.

Traditional Segmentation Models

Based on the definition above, segmentation is one of the most important tools in the marketer’s arsenal, particularly if you’re looking for better ways to personalize your customer communications. However, it might also be one of the most frustrating and under-utilized marketing tactics. Why? Because manually creating and managing segmentation requires a huge investment of resources. In fact, 42% of US and UK marketers say that a lack of key resources—like time, people, and money—prevented them from using segmentation to achieve their personalization goals.

Even with ample resources, human-driven segmentation must overcome several challenging hurdles. First, segmentation is incredibly complex. Demographic data, purchasing patterns, browsing history, email and social media interactions—with so many criteria available to define a segment (alone or in combination), it’s almost impossible for humans to manage the near-infinite number of possible customer groups. In addition, segmentation quickly becomes outdated, remaining static as customers enter new demographics or interact with your brand in different ways. Finally, most traditional segmentation models are too broad, and can’t deliver the level of personalization demanded by today’s customers.

How Machine Learning Can Help

The issues above make traditional segmentation techniques inconsistent, inefficient, and expensive. But that doesn’t mean brands need to simply give up on their goals for more personalized marketing. Through innovations in automation and advanced analytics, machine learning is transforming segmentation into a tool that’s more powerful and valuable than ever.

New machine learning and AI tools are able to analyze the historic customer data and segmentation models you already have, and then build upon that information every time a new customer interaction occurs. With algorithms that far exceed any human capabilities, machine learning technology creates complex, dynamic, and accurate segmentation models that can tailor your customer interactions down to the individual level. Best of all, this work becomes automated over time, so you can achieve your goals for more personalized marketing without an astronomical demand for human resources.

Advantages of Advanced Segmentation Techniques

How are machine learning and artificial intelligence helping marketers build stronger, more personalized relationships with their customers? Here are just a few ways these technologies can capitalize on your customer data with advanced segmentation.

Highly Personalized Communication: In a human-driven segmentation model, the segments themselves tend to be broad strokes that cover a large portion of your customer base. Even though there’s an immense amount of data available about each customer, it’s difficult for humans to harness that information in a meaningful way. With machine learning, all that data can be used to achieve greater understanding on an individual level and create a perfectly tailored segment for every customer.

With more finely tuned segmentation, you’ll be better able to offer customers the type of personalized, highly relevant interactions that build brand loyalty. When customers feel valued, they’ll interact with your brand more often—thus offering up even more data that machine learning technology will use to further hone the segmentation process.

Hidden Preferences, Valuable Interactions: The latest artificial intelligence tools don’t just tell you the right kind of message to send to a particular customer; they can also tell you the best way to send that message. Innovative AI technology is able to uncover hidden preferences—like a customer’s responsiveness to text messages over email—that traditional marketing techniques may overlook, leading to communication that’s more effective in delivery method as well as content.

Predictive Analytics: Artificial intelligence and machine learning tools don’t just have the power to harness more data than human marketers—they have the power to use it for a comprehensive view of the past, present, and future. When linked with the abundance of existing data in your CRM, AI tools have robust predictive powers. Predictive analytics offer suggestions on how to meet the future needs of customers, allowing you to tailor a segmented and largely automated messaging strategy.

Social Integration: Social media has revolutionized how marketers view data and customer relationships. Segmentation once relied heavily on direct customer interactions with your brand: think email click throughs, site browsing data, and previous purchases. By contrast, social media makes it possible to see how your customers behave when they’re not on your website. However, the amount of data coming in from each social network might be overwhelming in a traditional segmentation model, and the landscape of social media is constantly changing. Powerful machine learning tools make it possible to effectively gather and analyze the abundance of data from customer social media interactions, and then use that data to further enhance your segmentation strategy.

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