Customer Clustering Using Artificial Intelligence

Note: This article was originally published on November 24, 2017 and has been migrated from our previous blog. Some details — tools, libraries, benchmarks, industry context — may be outdated. For our latest perspective, see our recent posts.

Challenge

Well-defined audience segments are essential to a successful marketing strategy. Incorrect targeting can make the campaign ineffective in acquiring customers. Traditional campaign design methods often fail to reach potential purchasers, and are too generic.

Solution

Fortunately, due to modern AI techniques we are able to create highly-effective, personalized campaigns.

As a first step we perform clustering analysis in order to redefine your target segments. It allows us to sort users into highly distinct groups that have similar characteristics, and are therefore more likely to take similar actions in the future.

Later on, the algorithm classifies which segments should and should not receive your marketing material. Another Machine Learning model analyzes your audience data and creates personalized marketing messages for every customer.

Benefits

• More effective customer targeting, yielding higher ROI
• Automation of marketing campaigns
• Significantly lower cost per customer acquisition
• Increasing customer satisfaction through reducing irrelevant content
• Increasing customer engagement through highly personalized marketing campaigns