How to Improve Customer Segmentation Using Machine Learning

In any business, understanding customer behavior and needs is essential for driving revenue and growth. However, simply having a large customer base with a lot of information isn’t enough. A key way to profit from this data is through customer segmentation. And here lies the real challenge: how to transform this data into actionable insights for effective marketing strategies.

This is where machine learning comes into play. Leveraging ML in customer segmentation enables companies to be more strategic, efficient and customer-centric in their operations. So this article will explain how to improve customer segmentation using machine learning, discuss its benefits and limitations, plus outline the key steps for its successful implementation.

What is customer segmentation with ML?

First of all, customer segmentation is the process of dividing a company’s customer base into particular groups or segments (or clusters) based on similar behaviors, preferences, and characteristics. Machine learning uses advanced supervised\unsupervised algorithms and data analysis techniques to automate and significantly improve this process.

Using ML models, businesses can analyze vast datasets with high precision, uncovering patterns, trends, and relationships that traditional methods might miss. This helps to create more actionable and accurate customer segments. For this process, ML models use diverse data about customers, such as:

• Geographic: country, city, state;
• Demographic: age, income, gender;
• Behavioral: purchasing habits, brand loyalty, churn rate;
• Psychological: personality traits, lifestyle.

The goal of customer segmentation is to help businesses make informed decisions about their marketing, sales, and service efforts and effectively address the needs of each customer group. Companies must consider all the segments mentioned above to take advantage of segmentation. Therefore, doing this with traditional analysis methods may be difficult, and below is why.

Limitations of traditional methods of segmentation

While traditional segmentation methods can be effective, they often struggle with the complexity and scale of modern data. Traditionally, manual segmentation involves grouping customers based on predefined criteria without in-depth analysis. It usually overlooks complex customer details, leading to inefficient segmentation. Among the other limitations of manual segmentation are:

Static nature

Traditional segmentation methods often rely on static data, such as demographics, which may not reflect evolving customer preferences over time. For example, a customer’s buying habits may change depending on the season, time of year, and other complex factors to capture. This can result in outdated or irrelevant data and, ultimately, ineffective segmentation.

Lack of personalization

By grouping customers into broad categories, traditional methods often overlook customers’ unique characteristics and preferences. This results in marketing strategies that are too generic, failing to resonate with customers on a personal level, and missing out on opportunities to build solid and long-term customer relationships.

Data silos

Traditional methods often use separate data sources, where different types of data (e.g., transaction records, customer feedback, website interactions) are stored separately and not integrated. This dispersion limits the ability to gain comprehensive insights and hinders the creation of a holistic view of the customer.

Time-consuming

Implementing and maintaining traditional segmentation methods can be time-consuming and resource-intensive. These methods often require extensive data collection, manual analysis, and frequent updates to stay relevant. This makes it difficult to quickly adapt to changes in market conditions and customer preferences.

How do you address these limitations? Obviously, turn to machine learning. 

Why should you use ML for customer segmentation?

As we said above, ML automates the segmentation process by using advanced algorithms to analyze large datasets with multiple variables and complex relationships. It enables companies to uncover meaningful patterns and insights about customers or trends that might be difficult, missed, or time-consuming to identify manually.

Other benefits of using ML for customer segmentation include:

• Improved marketing: by using ML models, companies can plan personalized marketing campaigns for different customer segments, using the channels that they use the most;
• Improved accuracy: ML models analyze large datasets to identify complex patterns, resulting in more accurate and relevant customer segments;
• Improved customer satisfaction: ML enables businesses to create highly personalized marketing strategies based on customer preferences and needs, enhancing customer satisfaction and brand loyalty;
• Real-time flexibility: ML models can continuously learn from new data, allowing businesses to update customer segments in real-time and respond quickly to changing customer behaviors;
• Predictive analytics: ML models can predict future customer behaviors and preferences based on historical data, allowing businesses to adapt their marketing strategies to emerging trends accordingly;
• Automation and efficiency: by automating the segmentation process, ML models help companies save time, reduce human error, and scale efficiently to handle large datasets.

So, with machine learning, companies can segment customers more accurately, efficiently, and quickly. But as every coin has its flip side, this process also comes with some challenges and limitations that companies should consider in advance.

Challenges and limitations of customer segmentation with AI

Some of the key challenges of using machine learning to segment customers related to: 

Data security

Handling customer data requires strict adherence to privacy regulations, such as GDPR or CCPA, to avoid legal and reputational risks. Ensuring compliance with data privacy laws is challenging since it refers to monitoring how customer information is handled, which can be difficult to manage while keeping operations running smoothly. 

Integration with existing systems

It may be difficult to integrate data from legacy systems with AI technologies because of their incompatibility. To be able to use AI effectively, companies need to make significant changes or upgrades to their existing infrastructure that might be difficult. Without proper integration, businesses may face issues with data consistency, accuracy, and overall operational efficiency.

Lack of domain expertise 

Implementing AI in customer segmentation requires specialized skills and expertise in both machine learning and data analysis. Companies often face challenges in finding or developing in-house tech specialists to effectively deploy and manage these advanced technologies. A lack of expertise in AI for customer segmentation can result in unsatisfactory results and missed insights.

Lack of quality data

ML models require large volumes of high-quality data to produce accurate results. However, many companies struggle with collecting enough data or maintaining its quality because it requires the right infrastructure for data collection (data lake or data warehouse). Inaccurate, incomplete, or outdated data can significantly impact the effectiveness of AI models, leading to ineffective segmentation.

Lack of interpretability

Some advanced models work like the black box; for example, deep neural networks can produce segments that are difficult to interpret. So it is hard for business users to understand why customers are grouped in a certain way and how to act on those insights.

By addressing these challenges head-on, companies can better prepare their businesses for implementing ML for segmentation. 

How to build an AI/ML model for customer segmentation

Building effective machine learning (ML) models for customer segmentation requires a strategic approach that integrates technical expertise, careful planning, and ongoing monitoring. Here’s a step-by-step guide to help you navigate the process:

Designing a proper business case 

First of all, you will need to establish a clear business strategy. For this, you should:

• Identify objectives: determine your specific goals of customer segmentation, such as improving marketing strategies or enhancing customer retention. 
• Identify critical stakeholders: determine which teams, such as marketing, sales, and customer support, will benefit from the segmentation process and involve them during the process.
• Collect needed data: gather data about your existing customer base, analyze the context in which segmentation will be used (retail or ecommerce), and any constraints (budget, data availability);
• Perform data analysis and cleaning: analyze the collected data to understand its structure and content, identify and address any inconsistencies, to ensure the data is accurate and reliable;
• Align with business strategy: consider the long-term implications and scalability to create a segmentation model that is both purposeful and supportive of organizational goals.

Data collection and preparation

After setting your goals, the next step is gathering and preparing data for analysis. Here’s how to proceed: 

• Data collection

Collect detailed customer data, such as spending, age, and transaction history, from various sources, such as CRM systems, transaction logs, and social media analytics. The more data you collect, the more patterns and trends ML models will uncover.

• Data preparation

After data is collected, it should be cleaned and organized for analysis. This includes removing duplicates, handling missing values, and ensuring data consistency across various sources. Correct data preparation is crucial to enhance the accuracy and efficiency of ML models, leading to more reliable analysis and segmentation.

Feature selection

Next, you will need to define different features for your data (measurable variables or attributes) based on the most critical metrics for your business. These may include customer acquisition cost, retention rate, net profit, etc. This is a necessary step, as the visualization process will be based on these features later.

Algorithm selection

Before selecting an algorithm for customer segmentation, it is essential to understand the primary technique used for this process. This is a clustering analysis. It is an unsupervised machine learning method that groups customers based on their similarities, ensuring that customers within the same group (or cluster) are more alike than those in other groups.

There are several algorithms for performing clustering analysis, each with its strengths: 

• K-means clustering

It is the most popular method of grouping customers into K clusters based on similarity. It is highly efficient for large datasets and provides clear, distinct clusters, making it ideal for simple segmentation tasks.

• Hierarchical clustering

It builds a tree-like structure, offering insights into hierarchical relationships without needing a specified number of clusters. This method is flexible and helpful in visualizing segment relationships, especially when the data’s natural structure is unclear.

• DBSCAN (Density-based spatial clustering of applications with noise)

It groups customers based on data point density in a region, effectively handling varying cluster shapes, noise, and outliers. This method is ideal for complex datasets with uneven density or unknown cluster numbers.

• Gaussian Mixture Models (GMM)

It is a probabilistic model that estimates the likelihood of a customer belonging to each segment rather than assigning them to just one. This method is especially useful for complex segmentation tasks where segment boundaries are unclear and flexibility is needed.

The choice of customer segmentation algorithm will dictate how your model analyzes and groups customers based on their characteristics. Each algorithm is suited to different types of data and segmentation needs, but K-means continues to be the most used method due to its simplicity and effectiveness.

Model training and hyperparameter tuning

After selecting the algorithm for customer segmentation, the next step is to train the model with your prepared data. During training, the model learns to identify patterns that distinguish customer segments. To improve performance, you need to fine-tune the model’s hyperparameters – a process called hyperparameter tuning or optimization.

Hyperparameter tuning involves adjusting the algorithm’s settings to enhance its accuracy in identifying meaningful customer segments. For example, if you select K-algorithms for your model, you need to set the number of clusters (k) in K-Means. This process includes:

• K-means optimization: it means determining the optimal number of clusters, known as k. The goal is to minimize inertia, which measures how tightly the data points are grouped within each cluster.
• Elbow method: it is used to determine the optimal number of clusters by plotting the inertia against various values of k. The “elbow” point suggests a suitable number of clusters, beyond which adding more clusters yields diminishing returns.
• Average Silhouette method: this method assesses how similar a cluster is to its cluster set compared to other clusters, using a silhouette score from 1 to 1. High scores indicate that clusters are distinct, which helps in determining the optimal number of clusters.

Since hyperparameter tuning is an ongoing process, you may need to adjust it as customer data evolves or business goals shift.

Interpretation 

Analyzing the resulting clusters is fundamental to deriving actionable insights. Here’s how to effectively interpret them:

• Understand the clusters: analyze the characteristics and patterns of each cluster to determine what differentiates it.
• Analyze characteristics: examine the characteristics of each segment to understand the distinct needs and preferences of each group;
• Assign meaningful labels: label each cluster with descriptive names like “High-value customers” or “budget shoppers” to clarify their characteristics.

By understanding the characteristics and patterns of each segment, businesses can make more informed decisions and develop targeted strategies.

Visualization and insights

Once clusters are identified, it is essential to visualize them to make them actionable and align them with business objectives. For this, you can use visualization tools (such as Seaborn, Matplotlib, or Plotly Express) to not only gain deeper insights about customers but also effectively share the segments’ results with stakeholders.

Clear, well-designed visualizations make complex data patterns easier for companies to understand and assist in making better business decisions. By leveraging visualizations, companies can refine marketing strategies, personalize customer interactions, and enhance their business’ overall success. 

Conclusion

As customer data becomes more complex and markets evolve, ML in customer segmentation becomes a powerful tool with endless growth opportunities. Using deep learning for customer segmentation, companies can create detailed segments with remarkable speed and accuracy. It leads to more personalized marketing, improved product offerings, customer experiences, and increased revenue. Although implementing ML can be challenging because of data quality, security, and integration concerns, with careful planning and strong expertise, these obstacles can be effectively overcome.

This is where SoftTeco is ready to help. We offer a full range of ML development services, from data preparation, model selection, tuning to ongoing implementation and support. Our AI and data analytics experts will work closely with you to develop customized solutions that align with your business needs and ensure you take customer segmentation using machine learning to the next level.