Customer Segmentation

Why Segment Customers? #

The segmentation of customers is a crucial step in developing marketing strategies that can maximize customer engagement, retention, and spend. By grouping customers based on shared characteristics, such as their purchasing behavior, demographics, and psychographics - CRM and marketing teams can better understand their customer needs and preferences allowing for a more tailored marketing approach to meet those needs.

Moreover, segmenting customers can help companies optimize their marketing budgets by allowing them to focus their resources on the most promising customer groups. By understanding the needs and behaviors of each segment, companies can develop targeted marketing approaches that speak to the unique interests and motivations of each group.

Why Segment Loyalty Customers? #

When we talk about segmenting customers - we refer to dividing a company’s customer base into different groups with the goal to better understand each group’s needs, preferences, and behaviors, and to tailor marketing efforts to meet those needs.

But loyalty customers are customers who have repeatedly purchased from a company and have hopefully demonstrated a strong preference for the brand over time. These customers are typically more valuable to the company, as they are more likely to continue purchasing in the future and may also recommend the brand to others.

And because companies are likely to have additional data points on their loyalty members as they signed up for the program, they offer better segmentation results. That means personal data points such as Name, Address, Age, Phone Number, E-Mail, Work Affiliation, etc. can be used in segmentation algorithms and to ease marketing.

Segmenting loyalty customers specifically can help a company identify the unique characteristics of their most valuable customers. This can help the company develop targeted marketing and retention strategies to better meet the needs of these loyal customers, further strengthening their loyalty, and ultimately their spend. It also allows you to track these customers to identify and address potential problems, such as customers who may be at risk of defecting to competitors.

Machine Learning Methods to Segment Customers #

There are several machine learning methods that can be used for segmentation.

1. Clustering Clustering algorithms, such as K-Means, Hierarchical clustering, or DBSCAN, group customers based on their similarities in behavior or characteristic. Clustering is an unsupervised learning technique so it does not require prior knowledge or a dependent variable to create segments. It is a useful method when there is no clear definition of segments or when the objective is to discover new insights about the data.

2. Decision Trees Decision trees are a type of supervised machine learning algorithm that can be used to segment customers based on their responses to specific questions or characteristics. Because it is supervised, it requires a dependent variable or target variable to predict. This algorithm splits customers into subgroups based on specific criteria, such as age, location, or purchase history. Decision trees are useful when you have customer answers around specific questions or preferences.

3. Collaborative Filtering Collaborative filtering is a type of machine learning algorithm that can be used to segment customers based on their preferences or purchase history. This algorithm identifies customers who have similar preferences or purchase patterns and groups them into segments. It is often associated to music/video streaming and social media platforms; picked as a tool for making personalized recommendations. It is useful for loyalty customer data as you are more apt to have a long history of customer interactions which allows for a greater diversity of preferences and behaviors to be captured.

4. Association Rule Learning Association rule learning is a type of unsupervised machine learning algorithm that can be used to segment customers based on their like purchasing behavior. This algorithm identifies patterns in customer purchases, such as which products are frequently bought together, to group customers into segments based on their purchasing preferences. This information can be used to optimize product placement, to suggest complementary products, or to identify potential cross-selling opportunities.

Marketing Examples Using Customer Segmentation #

Here are some examples of how customer segmentation can be used in marketing:

1. Personalized E-mail Campaigns Segmenting you customer base allows marketers to better personalize their e-mail messaging and content to each segment. This can improve open rates, click through rates, and conversions.

2. Product Development Segmentation helps companies identify the needs and preferences of different customer segments. For example, a hotel company may segments its customers based on their travel reasons (leisure, business, health, etc.) to better create product offerings that are tailored to the specific needs of each segment.

3. Pricing Strategies By segmenting customers based on their willingness to pay, companies can set prices that maximize revenue. For example, a company can offer discounts or promotions to customers who are price sensitive, while charging higher prices to customers who are willing to pay more.

4. Loyalty Programs Segmentation can help companies create more effective loyalty programs by offering rewards and incentives that are tailored to each customer segment. For example, a company may offer discounts or early access to new products to its most loyal customers, while offering other incentives, such as free shipping or samples, to less loyal customers.