Successful marketing has always been about offering the right product to the right person at the right time. Not so long ago, brands relied on intuition for customer segmentation, separating customers into groups for targeted campaigns.

Not only was it unscientific, but it was also tedious and time-consuming. Nevertheless, it provided marketers a way to target their audiences more effectively. 

Today, the advent of cloud computing has given rise to machine learning, which has exponentially sped up the process of grouping customers into more precise segments for behavioral targeting.

Despite the highly publicized success of enhanced customer segmentation by machine learning, relatively few brands are taking advantage of it.

The reason for this appears to be equal parts affordability and unfamiliarity. This article addresses both of these issues in an attempt to show brands of all sizes how they can benefit from the power of machine learning today.

Why do customer segmentation?

Marketers cannot treat every customer the same way, sharing the same content on the same channels, with the same level of priority. Every individual has different needs and their own unique profile.

If marketers want to acquire more customers and lower their cost of acquisition, they must adapt their actions according to their prospects’ needs and preferences.

It is possible to do many different segmentations according to the outcomes you are trying to achieve.

Common methods are based upon RFM values, which stands for Recency, Frequency, and Monetary. In this case, customer segments are divided into one of three classifications:

Low-Value:   Customers who are less active than others; infrequent buyers/visitors; and generate almost no – or even negative – revenue.

Mid-Value:   Purchase often, although not as frequently as High-Value customers; marginally active with your brand; and generate moderate revenue.

High-Value: The group you don’t want to lose. High revenue, high frequency, and low inactivity.

Recency, Frequency and Monetary values must be calculated to determine which of these segments best represents each customer.

Beyond this, behavioral targeting requires adding more data into the mix from other sources that can provide demographic and attitudinal information where available.

And how about channel preferences from all the connected device IDs, streaming habits, email addresses and more?   Increasing the amount of data necessitates a better way of analyzing these multiple data variables and the simplest and most effective way to do these calculations is to take advantage of the power of machine learning.

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that focuses on the development of computer programs that can automatically learn and improve from experience without being programmed.  

It relies upon processing large volumes of data and therefore requires vast amounts of storage and processing power. As a result, the rise of cloud computing and customized chips has exponentially expanded the power and applications of Machine Learning.

Why Use Machine Learning for Customer Segmentation?

Machine learning can make sense of multiple dimensions beyond human imagination, find similar characteristics of customers based on their information, and group similar customers together.

It doesn’t eliminate the role of the data scientist. Rather, it enables data scientists and analysts to use clustering and classification algorithms to group customers into personas based on specific variations.

These personas consider customer differences across multiple dimensions such as demographics, browsing behavior, and affinity. Connecting these traits to patterns of purchasing behavior enables data-savvy companies to roll out highly personalized marketing campaigns that are more effective at boosting sales than generalized campaigns.

As the data available to businesses grows and algorithms become more sophisticated, personalization capabilities increase, moving businesses closer to the ideal customer segment of one.

Choosing Proper Customer Segments

Beyond the typical RFM calculations, data analysts and data scientists also leverage advanced analytical tools to extrapolate key customer characteristics into the general population. 

You see this in the analog and digital world with common statistical applications like multi-linear regression or clustering that go beyond the typical “best prospect” scenario.  There really isn’t a silver bullet approach to identifying that single prospect who shows a propensity toward your product or service. 

Rather, it’s a combination of many factors that create tiered groups that share high numbers of common characteristics.  Identifying who and where they fall into the ranking order is an important step where machine learning helps sort through the data clutter.

DXM Makes Machine Learning Accessible to Many

Due to its expense, machine learning today seems to be almost exclusively available to enterprise marketers.

For one segment of growing brands, however, DXM has an affordable solution. Through its custom marketing platform, DXM is able to apply a proprietary methodology for its brand marketing clients that provide them access to the most sophisticated advanced analytics available today powered by machine learning. 

Our solution is most effective for needs-based brands, or as we like to call episodic brands due to the sporadic nature of when and why purchases are made.

Examples of episodic industries are urgent care, real estate, financial services, education, and wireless services. In each of these cases, customers almost always purchase only when they absolutely need the product or service. 

If you manage marketing for an episodic brand that is struggling to keep pace with customer segmentation, contact DXM to discuss whether our advanced analytics solution powered by machine learning can benefit you. There is no cost to exploring and the results may surprise you.