Don’t guess your customers, segment them - Customer Segmentation Analytics (Part 1)

Intro

Ever wondered why successful brands seem to know you better than you know yourself? They recommend products you didn't know you needed and their advertisements speak directly to your heart?

It's a well-known notion that for any business to be successful, it needs to put its customers first and modify products/services as per a customer's changing requirements. As it's popularly said, "The customer is always right." Hence, we see multiple organizations spending millions of dollars over customer research and development, looking to crack the code on how to get people hooked onto their brand for a lifetime. And some of them actually do. One of their secret weapons used - Customer Segmentation Analytics.

Customer Segmentation Analytics is defined as techniques and methods used to split your customer base into distinct groups based on shared characteristics such as age, interests, income, location, device used and how often they shop with you. Armed with this knowledge, businesses are able to create products and services specific to each of these segments. The practice of customer segmentation began well before people put a label on it or thought of it as a strategy. It was unknowingly practiced since the 16th-17th century, when traders couldn't survive by catering to just one type of clientele in the Industrial world. So they started catering to the middle and lower class by showcasing goods through a built-in window from their shops to discourage them from entering; and the wealthier class were invited into traders' private & well-decorated homes to show the goods on constant display. This was a clear case of segmenting clients based on financial wellness and status in society.

Cut to the 21st century and Customer Segmentation is taught as a whole new subject at organizations. With the advent of technological advances such as AI/ML, it has taken on a new dimension.

The reason businesses/organizations spend a substantial amount of time and money over customer segmentation is because the benefits seen are paramount and the effort spent to increase their customer base is reduced when a strategy is put in place-- building customer loyalty by understanding their purchasing behaviors/frequency, optimizing prices of products/services offered by getting to know the income range of different classes, creating targeted marketing/sales campaigns by dividing customers based on psychographic traits and pin-pointing customers who are churning out & not interacting with your business as effectively, are some of the advantages to name a few.

Models of Customer Segmentation Analysis

As we continue to live in a rapidly changing world, customer trends are constantly evolving, causing businesses to modify techniques used to segment and understand their customers better. So how do businesses exactly decide which customer segmentation techniques are best to use? How do businesses change their models based on a customer changing their likes and dislikes? In this section, we will explore the top techniques and models developed till date to make sure businesses are able to target their customers effectively using segmentation.

This process is iterative with a lot of trial and error involved when grouping customers that bring in the most value, insight and ROI.

To start with, customers can easily be split based on shared characteristics called:-

1. Rule-Based Customer Segmentation :-

The above approach is fairly straightforward and intuitive, wherein the audience is grouped into specific buckets based on attributes similar amongst them, and a threshold is decided for each of the buckets.

But what if a customer starts to exhibit characteristics that don't fall into any of the pre-defined buckets? Or what if a customer changes behavior and has to be transferred to another segment?

For example, in the early days of a business, top-tier customers might have spent over 100 dollars on your product. But as the business grew and brand awareness increased, customers spending 100 dollars would be categorised as a mid-tier segment, whereas top-tier customers would be spending over 500 dollars. This would require constant intervention from business teams to make changes to customer data.

2. Dynamic Customer Segmentation

In order to deal with this unpredictability, Dynamic Customer Segmentation comes into play. Dynamic customer segmentation is a strategy that involves continuously tracking and analyzing customer behavior, to create highly accurate and evolving customer segments. Unlike static segmentation models (Rule-based segmentation), dynamic segmentation adapts to changes, ensuring that businesses always have a real-time understanding of their audience. With Dynamic customer segmentation, we can create micro-segments based on multiple customer attributes. This helps to create personalized customer profiles and generate valuable insights.

1. Clustering Customer Segmentation

A type of Dynamic Segmentation method applied is called the Cluster-Based customer segmentation, which is achieved by applying one of the well-known machine learning algorithms, the K-Means clustering algorithm. K-means clustering is a popular machine learning algorithm that can be used to segment customers based on their similarities. This method identifies patterns in the existing data itself, based on similarity across different dimensions. The algorithm aims to find 'k' clusters in the data, where each cluster represents a group of customers that are similar to each other. K-means clustering relies on the concept of centroids, which are the center points of each cluster, and uses distance metrics to assign customers to the appropriate clusters.

We will see this algorithm in action in Part-2 of this series,

2. RFM Customer Segmentation

Yet another popular dynamic segmentation model used is the RFM Segmentation, it's a powerful method to categorize customers based on their purchase/transaction history. This model can be applied to almost all businesses, as a particular business will always have customers purchasing some type of its product/service, hence creating a history of transactions. This is a less sophisticated and more manual approach as compared to Dynamic customer segmentation — which uses specific algorithms and includes many other attributes to group the audience. RFM is based on three key factors –

• Recency - How recently a customer made a purchase.
• Frequency - How often a customer makes purchases.
• Monetary - this factor reflects how much a customer has spent on products of the business during a particular time period.

RFM is popular and effective for the following reasons:-

• It uses an objective, numerical scale known as the 'RFM Score' which measures the above 3 factors at different weights, depending on the business type (usually the Monetary factor has a weightage of over 40% to the overall RFM Score). This yields a concise and informative high-level depiction of customers.

• It can be easily used and calculated by business teams without the need for sophisticated software or coding.

• The resulting output is intuitive and easy to understand.

Best Practices for Customer Segmentation

Here are some best practices to ensure effective customer segmentation. By following these, businesses can create more targeted and effective campaigns, improve customer satisfaction, and drive business growth:-

1. Define Your Goals: Clearly outline what you aim to achieve with segmentation. Are you looking to improve customer retention, increase sales, or enhance customer satisfaction? This will guide your segmentation criteria and analysis.

2. Choose Relevant Segmentation Methods: There are various methods, including K-means clustering, RFM segmentation and value-based segmentation. Select the most appropriate method(s) based on your goals and available data.

3. Gather Comprehensive Data: Collect a wide range of customer data, such as demographics, purchase history, website behavior, social media activity, and customer surveys. Ensure data accuracy and completeness.

4. Analyze Data Thoroughly: Utilize data analysis techniques to identify patterns and group customers with similar characteristics.

5. Prioritize Segments: Focus on the most valuable segments based on factors like revenue potential, customer lifetime value, and engagement levels.

6. Personalize Your Approach: Tailor your messages, product offerings, and customer service to each segment's unique needs and preferences.

7. Continuously Monitor and Refine: Customer behavior and preferences evolve over time. Regularly review and update your segmentation model to ensure its accuracy and relevance.

Conclusion

All in all, the impact Customer Segmentation techniques have on businesses and organizations are profound, providing teams with enlightening insights into what their customers like and dislike from time to time. Customer segmentation also provides a quicker way to understand our customers better, without having to reach out to each customer individually. Instead allowing us to work with the data we have and group them on similar characteristics.

The various methods used to determine customer segments can be selected depending on our use-case and level of depth needed. These techniques are backed by tangible data that we can collect using a customer's basic details after they purchase a product or service. Whether it's the K-Means clustering algorithm, which can accommodate additional attributes automatically, or the RFM Segmentation which can determine whether customers are providing your business the value it expects. The results are data-driven and can be tracked back to each unique customer if needed. By embracing customer segmentation, businesses can unlock the potential to build stronger customer relationships, drive revenue growth, and gain a competitive edge in the marketplace.

(To explore Clustering segmentation and RFM Segmentation in Action, please move to Part 2 of this Blog Series.)