AI for customer acquisition

Before we jump in the technical stuff acquiring customers, let us first understand the essential concepts of this article – customer segmentation and unsupervised machine learning algorithms. First, customer segmentation is a process where a business creates a cluster of customers based on specific factors such as gender, age, location, and more. Various sets of customer segments can be created out of the total customer population. Based on different customer segmentation, marketing strategies can and will vary.

But to create these various customer segmentation, it takes more than human resources. Technology is needed to build varied customer segments. AI is something that can help build customer segmentation based on several input factors. Interestingly, AI technology heavily relies on machine learning algorithms.

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There are two kinds of machine learning algorithms primarily:

  1. Supervised algorithms
  2. Unsupervised algorithms

Today we are going to emphasize the latter and learn how to use unsupervised algorithms for customer acquisition. The reason why we choose the unsupervised machine learning algorithm is that it is necessary to scrape through unlabeled data over the internet and identify patterns that connect those customers to be clustered in a customer segment.

Now that you have comprehended why AI for customer acquisition is required, we can move ahead to learn how to use it.

Data analysis for segmentation

The general population database can be built using various data sources. Later, the population data, which contains most of the unlabeled data, can be classified using unsupervised learning. However, the algorithms need to be trained further to enable it to produce more accurate customer segments.

Data cleaning

The attributes that have missing values need to be cleaned by replacing them with identifiable values that can be used for classification. Unsupervised algorithms used by AI can further help with data cleaning and creating a more digestible database that can further used for clustering the database.

Clustering the data

K-means clustering algorithm is used by the AI tool to divide customer segments from the general population. The algorithm is simple and aptly handles the task creating the clusters by measuring the variation in its observations among the clusters. Also, the algorithm helps in separating the general population with fewer identifiable features and create a solid customer database.

Analyzing clusters

Once the general population and the customer data are segregated, these two distinct clusters are analyzed with specific technical parameters. Based on the analysis of the clusters, the AI system can try and convert the general population into a customer database further by finding similarities with the already-created customer clusters.

AI for customer acquisition

After getting the final clusters of general data and customer data, they are fed to the AI system that can train the machine learning models further to decide whether to target the particular customer segment or not. Also, the customer segments are created intelligently by the ML models, and they predict what customer segments can be approached for marketing.


By using AI and machine learning algorithms such as unsupervised algorithms, the AI system can determine the customer segment based on the general population database. ONPASSIVE AI platform performs the AI for customer acquisition tasks for you so that you don’t have to worry about customer segmentation and acquisition. You can let the ONPASSIVE platform take care of the important operations that can help build a significant stream of revenue for life.

What’s assuring is that even if you are not tech-savvy, you can still utilize the AI for customer acquisition feature because it is very simple to execute. All the technical operations are performed behind the scenes, and you need not know anything about it but the final recommendations.

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