Customer segmentation can help reduce waste in marketing campaigns.
Customer segmentation can also help in other marketing tasks such as product recommendations, pricing, and up-selling strategies.
Machine learning models can process customer data and discover recurring patterns across various features.
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Machine learning algorithms come in different flavors, each suited for specific types of tasks.
Among the algorithms that are convenient for customer segmentation is k-means clustering.
K-means clustering is anunsupervised machine learning algorithm.
Unsupervised algorithms dont have a ground truth value or labeled data to assess their performance against.
The idea behind k-means clustering is very simple: Arrange the data into clusters that are more similar.
When training a k-means model, you specify the number of clusters you want to divide your data into.
The model starts with randomly placed centroids, variables that determine the center of each cluster.
The model goes through the training data and assigns them to the cluster whose centroid is closer to them.
While a model will converge on any number of clusters you provide it, not every configuration is suitable.
The scatterplot matrix visualizes correlations between different pairs of features.
In this example, the problem space consists of four features.
Dimensionality reduction can simplify your problem space and make it easier to visualize the data and spot clustering opportunities.
But how do you find the optimal configuration?
In general, models with lower inertia are more coherent.
But inertia alone is not enough to evaluate the performance of your machine learning model.
Increasing the number of clusters will always reduce the distance between instances and their cluster centroids.
And when every single instance becomes its own cluster, the inertia will drop to zero.
But you dont want to have a machine learning model that assigns one cluster per customer.
This is called theelbowof the machine learning model.
For instance, in the following image, the elbow stands at four clusters.
Adding more clusters beyond that will result in an inefficient machine learning model.
There are many ways it’s possible for you to put this to use.
For instance, when you get a new customer, youll want to provide them with product recommendations.
In product marketing, your clustering algorithm will help readjust your campaigns.
In general, youll have many more tools to test and tune your ad campaigns.
K-means clustering is a fast and efficient machine learning algorithm.
But its not a magic wand that will quickly turn your data into logical customer segments.
Overall, machine learning is a very effective tool in marketing and customer segmentation.
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