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Customer Segmentation Using Machine Learning


As businesses strive to get new customers and retain them, personalization avails a space for creativity and room for businesses to have healthy competition.

Customer segmentation is an important step towards personalization. If done well, it helps in decision making especially regarding new products, new services, pricing, and technology and marketing strategies. Machine learning in customer segmentation simplifies the otherwise tedious process.

Defining Customer Segmentation

Customer segmentation can be defined as a way of grouping customers to different characteristics.
Generally, they are various methodologies for customer segmentation which are defined by five major parameters: demographic, techno-graphic, geographic, behavioural and psychological.

Types of Data to Use in Customer Segmentation

Type of DataExamples
DemographicAge, gender, occupation, education, religion, salary, ethnicity and nationality
BehaviouralBuying history, website visitations, buying frequency., intervals between buying and display of loyalty
GeographicalLocation, climate and distance
TechnographicDevices, preferred payment options, offline and online time and contacts
PsychographicValues, attitudes, interests and activities

Customer Segmentation Models

RFV (recency, frequency, value) segmentationRFV customer segmentation uses behavioural and financial data compiled by machine learning algorithms. This helps businesses to know and predict the most loyal customers, revisit lapsed clients, identify new customers and promote upsell and cross-selling
Customer value segmentationMachine learning uses a lifetime value (LTV) scale to identify high-value customers for eligible offers, promotions and loyalty rewards.
Customer status segmentationRegarding RVF model, machine learning algorithms can help identify customers based on their active status for targeted campaigns.
Generational segmentationGenerally, the segmentation model uses geographical data to lure customers based on the prediction made by the machine learning system to identify the likelihood to purchase.
Life stage segmentationBy making purchasing patterns predictions, machine learning in customer segmentation identifies products that go hand in hand with what the customers recently purchased.
Seasonal segmentationBy understanding locations and matching them to the seasons in time, machine learning identifies customers and patterns based on geographical data. Seasonal promotions can be put in place to satisfy seasons like Christmas, Halloween and new year’s eve.

Application of Machine Learning for Customer Segmentation

Machine learning systems are the greatest tools best designed to do customer data analysis, find insights and make meaningful predictions. Identifying segments which is a difficult task to do manually is made possible by artificial intelligent models. Depending on the purpose and target, different types of machine algorithms are used to conduct customer segmentation. 

Machine Learning Algorithms for Customer Segmentation 

Different types of algorithms are used in machine learning. They can be broadly broken down into supervised learning, unsupervised learning and reinforced learning algorithms. Further, they can be broken down into: 

Type of AlgorithmsList of Algorithms
Supervised AlgorithmsRegression, Random Forest, Decision Tree, KNN, and Logistic Regression et cetera.
Unsupervised AlgorithmsApriori algorithm and K-means
Reinforced AlgorithmMarkov Decision Process

But when it comes to customer segmentation, unsupervised learning is the most credible algorithm.

Unsupervised Machine Learning for Customer Segmentation

Unsupervised machine learning discovers patterns in data from unlabeled datasets. Algorithms in unsupervised learning group data in similar attributes.

Clustering is one of the main models of unsupervised learning. The clustering algorithm only finds natural clusters in the input data and interprets it.

Popular clusters of the algorithm are:

  • K-Means Clustering
  • Density-Based Spatial Clustering
  • Hierarchical Clustering
  • Agglomerative and
  • Mean-Shift Clustering Expectation-Maximization (EM) Clustering.

The most preferred cluster in customer segmentation is K-means because of its ability to take unlabeled data and put it into groups. Here, the number of clusters is represented by the k value. The algorithm further assigns input data into a single cluster according to the features in the picture.

Benefits of Machine Learning in Customer Segmentation

Saves TimeIn previous years, customer segmentation used to be conducted manually. It took a lot of time which could go up to a month or years depending on the data that was being sorted. Machine learning has made segmentation easier. With its amazing speed and accuracy, customer segmentation can be performed with less stress, allowing businesses to run other demanding activities and save money in the process.
Ease of RetrainingDue to the ever-growing and ever-changing data mechanism, customer segmentation can be improved after deployment as a result of more labelling. Customer segmentation can be updated in many ways like; combining the output of an existing model with a new model or retraining the old model as a start.
Improved scaling Scalability is supported by machine learning models that are deployed in the cloud infrastructure during production. Due to its flexible nature and inherent capability to handle more data and scale in production, machine learning can be improved at any given time whenever data is available.
Laser-sharp accuracyBetter result outcomes can be achieved with, machine learning. Elbow methods, for instance, ease the process of tracing the number of clusters in the customer dataset.

How to Integrate Machine Learning in Customer Segmentation?

The following steps can be used to integrate machine learning in customer segmentation:

1. Business case creation

To achieve better results you need to have a goal. Understanding your business purpose of using machine learning is a good start. To avoid reckless and disorderly results, break down the habits and patterns of your customers from different perspectives. Further look into other different parameters like demographics, psychology and techno-graphics.

2. Data preparation

More data for machine learning means more accuracy of machine learning data models, even though it takes time. Using historical data to train your model is a good way of speeding it up. With well-formatted and clean data, you are guaranteed to get more features to look at. Features may vary from sales, retention rate, client satisfaction and average lifetime value.

3. Implementing K-means

As mentioned above, k-means clustering is a popular algorithm in unsupervised machine learning. It samples customers by different features, sorts them into clusters, groups them together and breaks them down further to the lowest possible outcome.

The algorithm assigns data to the nearest and closest centroid – a centroid is the centre of a cluster – and arranges data into clusters that show more similarities.

4. Tuning hyper parameters

To find the most rewarding customer groups, you have to choose the hyper parameters for algorithms. This is called hyper parameter tuning. Here, you have to build several K-means (ranging from 1-15) with corresponding values. The elbow method requires choosing K-value where the inertia decreases and stabilizes the most.

5. Visualizing data

This is where you interpret and visualize your findings to grow your business. After you have a chosen K – value, plug it into the k-means models so you can see your desired customer groups and reorganize your approach towards them. You will be in a position to optimize your customer groups in terms of performance. Knowing your best and worst-performing segments will help you improve your strategies by creating targeted marketing campaigns, create product roadmaps and feature launches. 

Final Thoughts

Using machine learning for customer segmentation leads to ROI in less time and with less effort. By making use of machine learning and artificial intelligence businesses will accurately segment their customers in a very easy way to help you make better strategies and grow your business.

Topics in Machine Learning

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