Are you targeting the right customers? Customer Segmentation might be just what you need.
Updated: Oct 19
Business leaders today strive to better understand their customers. The ability to deliver personalized messaging for specific products and services allows for upselling and cross-selling opportunities as well as ensuring a higher ROI (Return on Investment) from targeted marketing and sales activities. However, you will need to identify which customers are the right customers to contact. It sounds like a Customer Segmentation Analysis might be in your future.
What is Customer Segmentation
Customer Segmentation or Market Segmentation is the process of segregating an organization’s customers into distinct groups. Conducting a Customer Segmentation analysis empowers organizations to design customized marketing campaigns and target customers effectively based on demographic, geographic, psychographic, behavioral, and other characteristics.
Importance of Customer Segmentation
Customer segmentation helps companies focus on a specific subset of customers rather than spending a fortune on a generic marketing campaign for all customers. The analysis reduces marketing costs and increases ROI because the company’s campaigns focus on a particular customer segment which has a higher propensity to buy the company’s product or service.
The customers can be segmented on the following characteristics:
Demography: Age, Gender, Household Income, Marital Status, Education, Location, Language, etc.
Geography: Country, State, City, Zip Code, etc.
Behavior: Purchasing Patterns, Spending Patterns, Frequented Brands, etc.
Psychography: Lifestyle, Interests, Values, Political Affiliations, etc.
Customer Segmentation is the first step of STP Marketing (Segmentation – Targeting – Positioning). This marketing technique is extremely effective because it helps break the universal set of customers into smaller subsets or subgroups. The marketing teams then design personalized campaigns to cater to each set of audiences. These personalized marketing campaigns then yield far better results than a generalized advertising strategy. According to Harvard Business Review, the revenue of the product grows by 38% when customers receive ads based on their activity on a site. According to Statista, 90% of U.S. consumers find marketing personalization appealing. This amalgamation of Segmentation and Targeting helps in creating the product Positioning. In addition, it helps to classify the product or the service apart from its competition.
Machine Learning and Customer Segmentation
Machine learning algorithms can be employed to identify customer segments. More specifically, data scientists leverage clustering algorithms to analyze patterns in customer data which then helps identify segments or clusters. One of the most commonly used clustering techniques is the ‘K-means’ clustering which partitions the customer data into pre-defined ‘K’ number of segments. Each data point in these clusters or segments are as similar as possible, all of this while the algorithm maintains a respectable distance between each segment. There is one drawback, the K-means algorithm can be used only for numerical data. Depending upon the type of data in the dataset, different algorithms can be used for segment identification. The most basic ones are as follows:
K-Means Algorithm: For Continuous or Numerical Data. (Budget, Revenue, Profit, etc.)
K-Modes Algorithm: For Categorical Data. (Name, Hair Color, Gender, etc.)
Prototype Algorithm: For both Categorical and Numerical Data.
The scalable nature of these algorithms combined with the elastic nature of cloud computing services can help us revise our models with time. A company with a data of say a thousand customers can easily scale up these models to handle the data of even a million customers in the future.
[Note: Throughout this article the words ‘clusters’ and ‘segments’ will be used interchangeably]
Interpretation of Segments/Clusters
Identifying the clusters is not the end of the story. Interpreting the meaning of each cluster is also a key component of the cluster analysis. Some of the most used methods to interpret the attributes/features involved in the cluster make-up is as follows:
1. Visualizing the Clusters with the Attributes
The data being fed to train the clustering algorithms can be visualized with the clusters that are identified. A quite easy way to identify the composition of each segment would be to create a pair plot of the data with the pair plot hues as the labeled cluster segments. This pair plots help in understanding which features are contributing most to each segment/cluster.
2. Using Machine Learning methods to interpret the Clusters
Classification Algorithms such as the Decision Tree Classifier, LGBM Classifier, etc. are excellent tools to understand the meaning of each cluster. Now that the clusters are identified, you can append the labelled clusters to the training data-frame and use these labels for the classifiers. With this method, we hit two targets with one arrow. The process is as follows:
The performance metrics are calculated after training the classifiers. If the results are good, then it can be concluded that the clusters are distinct.
The informativeness of each cluster can be measured by using various feature importance methods.
Customer Segmentation empowers companies to understand different customer segments so that they can target a specific customer with a particular product or service. With specific customized marketing campaigns and sales activities tailored to the correct customer group, companies can maximize their sales activities and advertising dollar spend on the right customers. In addition, by employing proper data collection, customer segmentation and machine learning methodologies, companies can generate insights and detect patterns in the data. These insights not only empower companies to make the right decisions about marketing and sales activities, but about creating new products and services.
Interested in learning more? Contact us at Scalesology. We are eager to see how we can work with you and your data to gain insights into your business.