Using Machine Learning for Customer Segmentation in Digital Marketing

Before understanding why and how to use machine learning for customer segmentation, let’s understand why businesses implement customer segmentation, and how it helps companies to create and sell products/services more effectively. After identifying different types of customers, their needs, and desires, businesses develop a better understanding of what new products and services they must launch and how to sell them. 

Why Businesses Need Customer Segmentation?

Effective customer segmentation helps businesses to increase customer lifetime value and make them spend more. This means that instead a customer visits a brand once a year to get more products, with customer segmentation and personalized recommendations, a brand can make them come 5 times a year. Each time they visit to spend it boosts their interaction with that brand.

Smaller yet frequent purchases are more effective as they indicate the predictive behavior of customers. This will not only improve loyalty but also help businesses to stay informed for future decisions.

A report in 2022 about global consumer trends indicated that customers are seeking mutual consultation experience at scale. They want to be cared for. This is where customer segmentation saves you. You need to understand what each customer wants and how to target them effectively. With customer segmentation, you can understand current and high-value customers better.

What Exactly is Customer Segmentation? 

In customer segmentation, you divide your customers based on their different characteristics such as demographics or behaviors. This division in segments enables your marketing or sales team to reach out to customers with a better outcome. These segmented groups are also used in building a marketing persona for effective customer analysis. Companies after implementing customer segmentation uncover ways to improve their sales and what new products or services they need to invest in. 

Types of Customer Segmentation

There’s no one-size-fits and therefore customers are divided with careful considerations. These segmentations are broken down into two types.

1. Based on who customers are. In the process of understanding customers companies focus on psychographics, demographics, and B2B firmographics. This includes their

• Age
• Income
• Relationship Status
• Family
• Job Type
• Geography
• Urbanization

1. Segmentation is based on what customers do or how they spend. Contrary to the first one, this segmentation is based on customer behavior while shopping. This segmentation is based on their

• Long Term Loyalty
• Share of Wallet
• Basket Size

Customer segmentation strategies vary differently depending on the size and industry. Let’s take the example of a cosmetic store where it is unusual to get a 100% share of the wallet. That’s why they focus on customer behavior and what products they buy. Either customers tend to buy greater quantities or more premium products. Different approaches are used to target different people or companies. 

Why Use Machine Learning for Customer Segmentation?

Machine learning MI is a critical branch of Artificial Intelligence that enables systems to learn without benign programming. It is a type of computer program that makes the computer learn itself. From sales to healthcare, manufacturing to finance, Machine learning is exposed to lots of data and processing time. Companies need data scientists to understand their customers’ lifetime value and the optimal number of customers they need. But with Machine learning and predictive analysis, they don’t need data analysts on board.

Machine learning for customer segmentation enables the identification of distinct groups and customer groups who act differently from others. After the segmentation is completed it helps in personalizing digital marketing strategy towards defined customer segments. 

Personalized customer experience is a priority of digital marketers. By implementing machine learning for customer segmentation companies approach all customers individually with personalized ad campaigns, email marketing, and products. 

Machine learning for customer segmentation is done by using several algorithms to categorize customers based on their features. This segmentation includes;

• Hierarchical clustering which organizes customers into a tree-like hierarchy of clusters
• K-mean clustering divides customers with similar features into clusters
• DBSCAN identifies the segmented clusters based on the density of their points in the data space
• Principal Component Analysis or PCA reduces the capacity of data to preserve important information.
• Neural networks work by learning complex patterns in data through interconnected layers of nodes.

Benefits of Using Machine Learning for Customer Segmentation

We all know that manual methods often lack the precision needed to produce actionable insights. In the past, this process required populating data tables manually and analyzing them in a detective-like manner. In contrast, machine learning has transformed the process of customer segmentation. Along with that, there are major benefits of using Machine learning for customer segmentation as below;

Ease of Retraining

Customer segmentation models are not static and need to be updated regularly to stay effective. Customer behavior and trends change over time, making the refinement of models essential. Two common approaches to retraining include:

Retraining the Old Model: Using the existing model as a foundation and updating it with new data.

Combining Models: Retaining the old model and merging its output with a new model to improve performance.

As more labeled data becomes available after deployment, these updates can significantly enhance the model’s accuracy and overall usefulness.

Scalability

Machine learning models deployed in production are designed to be scalable, often supported by robust cloud infrastructure. For instance, a company serving 10,000 customers can use a segmentation model effectively. If the customer base grows to 1 million within a year, the model can easily handle the increased data volume without requiring a complete redesign. This scalability ensures businesses can adapt to growth seamlessly.

Higher Accuracy

Machine learning techniques, such as the elbow method, help determine the optimal number of clusters for any dataset. These methods consistently outperform manual segmentation techniques in accuracy, enabling businesses to achieve more precise and effective results. This precision allows for better targeting and the creation of highly impactful marketing strategies.

Conclusion

Machine learning-based segmentation is particularly effective in allowing customers to find definitive patterns in large sets of data. Major advancements in using machine learning for customer segmentation helps companies in retaining customer satisfaction and profitability.