Marketing function is evolving rapidly with advancements in eCommerce, digital and mobile, as well as changing consumer demographics. A recent study indicated that e-commerce will account for 17.0% of retail sales by 2022. This is up from a projected 12.9% in 2017. Tech Talks brought this interesting subject to our attention in their article, “Customer segmentation: How machine learning makes marketing smart.”
This indicates the trend is that more and more people are moving online for their purchases or are heavily influenced by their digital activity when doing in store purchases. For retailers, this can be a challenge when it comes to developing marketing strategies.
Customer segmentation can help reduce waste in marketing campaigns. If you know which customers are similar to each other, you’ll be better positioned to target your campaigns at the right people. This is where machine learning can be of assistance.
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 – an unsupervised machine learning algorithm.
Melody K. Smith
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