Setting up of Customer Segmentation Models

Customer segmentation models are crucial tools to help you understand and connect with your customers more effectively. By identifying groups with similar needs and characteristics, segmentation models allow you to tailor your approach, making every customer interaction more relevant. They also help democratize customer insights across your organization, enabling teams to make better, data-driven decisions.

Effective segmentation not only drives better customer experiences but also boosts profitability by enabling precise targeting and engagement strategies.

In this article, I'll provide an example of a customer segmentation model using a clustering algorithm, discuss the steps involved, and highlight the tools that can assist you along the way.

Why Customer Segmentation Models Matter

You might think you know your "typical" customer, but focusing on just one characteristic offers a limited view. Customers are multifaceted, with varied preferences and behaviors that can define unique groups. Profiles like "age" or "promotional buyer" can give a sense of the size and significance of certain groups, but relying on these singular views limits your ability to create impactful strategies.

Customer segmentation models are about uncovering groups that are distinct from one another based on multiple characteristics. Your industry knowledge plays a crucial role here—you can try different combinations of features and end up with several useful segmentations. The key is finding the model that best suits your use case.

Various algorithms are available, and one of the most commonly used is K-means clustering, which I’ll be using as an example.

Highly Targeted Customer Segmentation Models

Depending on the product or service, industry, and available data, the customer segmentation models previously listed can sometimes be too general for highly personalized marketing. That’s where more specific segmentation models can be helpful. Here are some examples: 

Customer Lifecycle Segmentation

The customer lifecycle model maps how a customer journeys from considering a product to becoming a loyal customer. This segmentation includes segments for each stage of the customer journey.

For instance, one segment might include potential customers who are in the awareness stage and have never heard of your brand, while another might include repeat customers who are in the loyalty stage. By understanding these stages, you can tailor your marketing strategies to effectively nurture customers at each point in their journey.

Recency-Frequency-Monetary Value (RFM) Segmentation

RFM models consider how recently a customer made a purchase (recency), how often they make purchases (frequency), and how much they spend on each purchase (monetary value). This allows you to identify high-value customers (HVCs) and adjust your marketing efforts to maximize value from these relationships.

For example, customers who make frequent, high-value purchases could be segmented for loyalty rewards, while those with lower recency could be targeted with re-engagement campaigns.

Cohort-Based Segmentation

A cohort is a group of customers sharing a common characteristic within a specific time frame. It allows you to analyze their behavioUr over time. For example, you might create a cohort of customers who first purchased in December 2023.

Conducting a cohort analysis allows you to observe trends and changes in their behaviour over time, which can help you refine your segmentation model. This is particularly useful for understanding customer retention and tailoring long-term engagement strategies.

K-Means Clustering

K-means clustering is a statistical algorithm that groups customers based on similar characteristics. It automatically identifies patterns and places customers in clusters that share similar attributes. This type of algorithmic model can create unique groupings that may not fit within the standard segmentation models mentioned above.

For instance, K-means can help uncover unexpected customer groupings based on complex combinations of demographics, behaviours, and product preferences, offering insights into hidden targeting opportunities.

Getting Started with Segmentation Models

To begin, make a list of characteristics that are crucial to your customers and your business. These can span different dimensions, such as demographics, buying behaviour, product usage, channel engagement, and needs states. Select characteristics that represent what’s most important to your business for each dimension.

For example, a leisure or travel company might consider the booking timing (buying behavior), the type of accommodation and time of arrival (product), and the household size and composition (customer demographics).

Selecting Features for an Effective Segmentation Model

Choosing the right features is essential to creating meaningful segments. Pick the best features for each dimension while avoiding overlaps that might reduce their distinctiveness (e.g., holiday spend amount and family size).

It’s also important to consider which features you can influence, allowing you to take action on the segments you identify.

Start simple, with only a few features, and gradually refine them based on their power to form distinct segments. Remember that different features interact, so finding the best combination might take a few iterations.

For the leisure company example, six segments were optimal, including two prefer bookings for campsites during the high season. One of these segments had a higher budget and booked further in advance than the other.

Running the Clustering Algorithm

Once you’ve chosen your features, it’s time to run the clustering algorithm. Python is a great option, as it offers numerous libraries for clustering algorithms like K-means. I like the K-prototype algorithm (Huang, 1998) because it overcomes some of K-means' limitations by effectively handling categorical features, such as accommodation type in our example.

Using Google Cloud, you can leverage Vertex AI Workbench to run clustering algorithms, with seamless integration for storing results in BigQuery and automating workflows. Updating customer scores and segment memberships can also be automated, allowing you to keep your segmentation model current.

Summary

Identifying well-formed segments is a big step toward personalization. While finding the best approach might take several iterations, it will significantly enhance your understanding of your customers. The more you learn about your customers, the more precise your segmentation model will become, especially as new customer data becomes available.

Segmentation is an ongoing process, and adopting a data-driven approach can significantly enhance customer experiences. Discover more in our article on improving customer experiences with data-driven insights.

Start with a few core characteristics to find meaningful groups and expand from there. Once your segmentation model is in place, updating it to reflect new data is straightforward, helping you stay connected and relevant in every customer interaction.