What Is Clustering, and What Are Its Benefits for Marketing?

Clustering isn’t a new or trendy concept, but it is becoming increasingly important in the world of marketing and advertising, due to the growing amount of data on users, audiences, and customers, and the opportunity that exists today to create more precise and effective clusters for campaigns. Still not sure what clustering your database can do for you? In this post, I’ll explain what it is and what benefits it offers for marketing. Let’s get started.

Companies and marketing experts often talk about segmentation as a way to fine-tune their communications, since it allows them to tailor their messages based on the common characteristics of that segment.

We could say that customer segmentation consists of a set of actions through which common characteristics within the customer base are identified and the base is divided into different groups: each of these groups is a customer segment.

However, clustering—which is often confused with segmentation or seen as no different from it—involves identifying groups of similar customers based on similar variations within each group. This is all done using mathematical models and machine learning.

What is the main advantage of clustering over segmentation? Well, basically, with segmentation, you only group based on business criteria, which—once they are no longer relevant to defining that group—can make that segmentation less effective. With a cluster, by using mathematical machine learning criteria, the groups are constantly being updated, making them much more effective for any subsequent actions you take with them.

Today, this method is used primarily in computer science, marketing, the business world, and the arts, and it is extremely effective.

In our field—marketing—this is particularly important and relevant because, thanks to these clusters, we can group our target audience or our customers—if we’re working on loyalty initiatives—based on complex variables such as motivations, purchasing behavior, interests, and so on…

Ultimately, the goal of cluster analysis is to accurately segment customers in order to achieve more effective data clustering through personalization.

An example that everyone is sure to understand is Netflix. As you know, this platform uses a recommendation system based on clustering algorithms. In fact, the company has about 2,000 clusters of communities with similar tastes, which creators use to gain firsthand insight into viewers’ preferences and develop new original series for that audience. And doesn’t it work?

 

What is clustering used for?

 

Clustering is essential if your brand is truly customer-centric—that is, if the decisions your business makes regarding product or service design, customer experience, and communication and marketing are based on customers’ needs, motivations, interests, and behavior.

Broadly speaking, clustering helps you segment or categorize a group of customers based on certain characteristics they have in common. This allows us to create subcategories within our target audience to better tailor our message and increase the efficiency of our campaigns. You can do the same with your points of sale, products, and more—since clustering can help you separate and better understand whatever you group together.

The first thing you need to consider is data quality. Remember that, unlike segmentation, clustering uses mathematical models and machine learning. If the data isn’t good, the results will be inaccurate; therefore, you’ll likely need to normalize the data before you begin.

To do this, it is essential to have an effective data strategy that takes the following into account:

• What data architecture are you going to use (based on an analytical database)?
• Design a data governance framework tailored to your organization.
• Determine how you will manage the data you collect.
• As well as how you’re going to present that data or prepare it so that it can be easily analyzed.

Once you have that structure in place—one that allows you to easily access the data you need for analysis—you can then implement clustering, which will enable you to understand your database and target the data effectively.

Using cluster-based segmentation as part of a customer loyalty and incentive strategy for any brand can offer you the following benefits:

• Identify behavioral patterns, such as what they buy, how much they buy, how they buy, what factors influence them in a given situation, when they consume or use our service, etc.
• Understand customer profiles based on homogeneous consumption patterns: by spending levels, CLV (Customer Lifetime Value), location, purchasing habits, sociodemographic variables, age, etc.
• Create and develop ad hoc campaigns and promotions, as well as sales policies, based on the trends revealed by the data, thereby improving their efficiency.
• Prioritize customers: Focus specific customer loyalty initiatives on high-value customers.
• New Customer Acquisition (Identical): Develop targeted acquisition strategies aimed at customers with the same characteristics as the cluster.
• Redirect customer flows toward higher-value clusters. It’s even a way to develop up-selling techniques.
• Increase retention: Predicting clusters at risk of churn allows for the development of targeted actions to reduce the churn rate.

 

What are the two most common clustering techniques?

 

There are several, but without a doubt, the two most commonly used techniques are these:

 

#1. K-means cluster analysis.

K-means is an unsupervised classification (clustering) algorithm that groups objects into k clusters based on their characteristics.

 

 

 

 

This method helps improve customer profiling and predictive analytics, and is also used to target your customers with personalized offers and incentives based on their desires, needs, and preferences.

Once you’ve identified all the variables that make up a customer’s cluster, you can engage with them in a personalized way, communicating what’s most relevant to them, thereby achieving greater effectiveness.

 

#2. k-nearest neighbors analysis.

Unlike the previous one, this is a supervised learning algorithm; that is, based on an initial dataset, its goal is to correctly classify all new instances. It is used, for example, to create customer-specific recommendation engines.

 

 

The design of these types of algorithms allows for greater accuracy by including various parameters that are important to business logic, such as average user ratings, visit counts, and purchases…

 

Do you need to refine your communications and truly personalize your messages?

 

At MioGroup, one of our main goals for our clients is to optimize their investment in advertising and marketing. To that end, we offer them the solution that will deliver the best results in terms of achieving tangible benefits from every initiative we carry out with them.

One of them is segmentation, and of course, clustering through machine learning, developed by our experts at artyco, one of the companies in the group. Shall we talk?