How To Conduct Customer Segmentation Using Machine Learning?

Overlooking customer segmentation is one of the biggest mistakes you can make for your business. Effective segmentation requires resources and data analysis expertise. However, by leveraging machine learning, you can save time and accurately segment your customers.

In this blog post, we discuss the importance of customer segmentation, the main customer segmentation models, and the benefits of ML-based customer segmentation.

What Is Customer Segmentation?

Customer segmentation is the process of dividing your customers into distinct groups based on common characteristics, behavior patterns, or preferences. The goal of customer segmentation is to understand and categorize customers into homogeneous groups that have similar motivations and buying behaviors. Thanks to customer segmentation, businesses can personalize their offering and marketing efforts to better meet unique customer needs. With proper segmentation, businesses can optimize customer experience, enhance customer satisfaction, and boost loyalty. 

While segmentation requires data analysis and expertise, businesses shouldn’t make the mistake of overgeneralizing and assuming that one size fits all. The same product can perform different tasks in your customers’ lives, so customization is necessary. 

So, what is a customer segment? It is a group of customers within a larger customer base who share similar needs and preferences. Customers in each segment are united by a common attribute such as purchasing habits, psychographics, or geographic location.

Why Use Machine Learning for Customer Segmentation?

Customer segmentation can be beneficial for every business, but it carries certain challenges as well. Some businesses, especially small ones, may not have enough resources for segmentation. It is a process that takes a lot of time, so oftentimes companies are too busy with day-to-day operations and resolving customer issues and can't dedicate their time to customer segmentation.

Luckily, customer segmentation can be automated through machine learning. Not only can it save you a lot of time, but it can also help you analyze large volumes of data and uncover patterns that may not emerge through traditional customer segmentation methods.

Here are the biggest benefits of using ML for customer segmentation.

• Higher accuracy

Machine learning algorithms can process and analyze data more efficiently than manual methods, which leads to more accurate and granular customer segments. By handling data in a more exploratory manner, machine learning can reveal previously unknown customer segments or subgroups that have unique characteristics or behaviors.

• Dynamic segmentation

Machine learning models can update customer segments in real-time as new data becomes available. This allows companies to respond quickly to changing customer behaviors, market trends, or other external factors, ensuring that segmentation remains relevant and effective.

• Scalability

Machine learning techniques can handle large datasets and automate the segmentation process, which makes ML customer segmentation more scalable and efficient. Once a model is trained, it can quickly segment new customers without requiring additional work.

• Seamless integration

Machine learning models can easily be integrated with other technologies like customer relationship management (CRM) systems or marketing automation platforms. This integration allows businesses to seamlessly implement personalized marketing campaigns based on customer segments and consistently deliver targeted messaging across various channels.

Customer Segmentation Models

Customer segmentation models are used to divide a company's customer base into distinct groups or segments based on various criteria. Each business should choose a customer segmentation model that works best for its needs. Here are some commonly used customer segmentation models, along with customer segmentation examples.

Demographic segmentation

This model categorizes customers based on demographic factors such as age, gender, income, education, occupation, and more. It helps companies understand the preferences and needs of different age groups, genders, or income levels. However, it doesn’t take into consideration individual differences and specific reasons why people are using your product. 

Geographic segmentation

Geographic segmentation uses the customers’ geographical location as the key variable. Depending on your business, this can be a country, region, city, or even neighborhood. It allows businesses to tailor their marketing strategies to specific locations and take into account regional preferences of the customers. 

Psychographic segmentation

Psychographic segmentation digs deeper and focuses on customers' lifestyles, interests, values, attitudes, and personality traits. Through psychographic segmentation, companies can appeal to people's beliefs, focus on specific customer behavior, and appeal to unique customer motivations. 

Behavioral segmentation

This model divides customers based on their behaviors, including purchasing patterns, product usage, brand loyalty, engagement with marketing channels, and more. It enables companies to identify loyal and occasional customers and adapt their messaging to encourage the desired behavior. 

Hybrid segmentation

Hybrid segmentation combines multiple segmentation models to create more nuanced customer segments. For example, combining demographic and behavioral segmentation can help identify specific groups of customers who share similar characteristics and behaviors. Granular segments allow for a higher degree of personalization, which can boost customer retention and loyalty. 

Customer Segmentation Strategy

Customer segmentation strategy can be divided into 7 key steps. 

1. Clearly define the objectives of your customer segmentation strategy and identify what you aim to achieve through segmentation (improved targeting, personalized marketing, enhanced retention, etc.)
2. Determine the key criteria based on which you will segment your customers. Choose the segmentation model that is best aligned with your business goals.
3. Gather diverse customer data from various sources, such as CRM systems, website analytics, surveys, or social media platforms. Ensure that the data is accurate and complete. 
4. Apply appropriate data analysis techniques to conduct customer segmentation analysis. You can either take the traditional route or leverage machine learning algorithms to make the process faster and more accurate. 
5. Validate the identified segments by assessing their size, relevance, uniqueness, and potential profitability. To further refine your segments, conduct additional research or customer surveys.
6. Determine the most effective marketing strategies for each customer segment. Tailor your messaging, product offerings, and promotions to align with the pain points and preferences of each segment. 
7. Implement the segmented marketing strategies and monitor the results. Evaluate the effectiveness of your segmentation strategy and continuously refine it based on the insights and feedback. 

Customer Segmentation Software

To optimize and automate customer segmentation processes, businesses can use tools that are specifically designed to segment customers. These software solutions typically utilize advanced analytics, machine learning, and data visualization techniques to help organizations identify and target distinct customer segments.

There are various customer segmentation software solutions available in the market, ranging from platforms focused on segmentation to comprehensive customer analytics and marketing automation suites. 

With an AI-powered customer data analysis tool like Essense, you can identify key themes in customer feedback that can be used as a base for customer segmentation. By conducting targeted customer feedback analysis, you can cultivate a better knowledge of each segment, identify their pain points, and prioritize customer needs. 

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