The Impact Of Machine Learning In Customer Segmentation

All that results in a time-consuming effort that doesn’t yield the best results. As it happens with the no segmentation approach, there are moments where using manual segmentation can be enough for your business. Small businesses or those with easily segmented groups can use it and be confident that the approach will serve them for more time, as the segmentation criteria are more stable.

This is where the magic starts to happen, as this is the level where machine learning begins to impact the customer segmentation process. ML is the main driver of this approach. You can use AI-based algorithms to divide customers into highly-specific groups, thanks to the clusterization of datasets according to patterns (which range from the obvious to the fairly obscure).

The results from this approach are highly variable, as they depend on the machine learning algorithms you use for it. In that sense, k-means and hierarchical aggregation are the two most widely used in customer segmentation, as they can work with limited to no human supervision. For the most part, both techniques can provide highly-specific groups that can be both a blessing and a curse.

On the one hand, having more defined groups can provide you with a deeper understanding of your customers, which ultimately can better inform your marketing strategies. However, as the algorithms work on all data (both useful and useless), you can lose a lot of time to get many groups that don’t offer some actionable insights. What’s more – you’ll get a limited number of resulting clusters that may leave some actionable opportunities aside.

All in all, automated segmentation is great to achieve more specific groups depending on criteria that might be hidden for you. Its high scalability also makes it an excellent alternative for larger companies with a vast audience and a lot of data that can’t be handled manually.

Is there a way to overcome the issue that comes with automated segmentation for which you end up with many unusable groups clustered together around irrelevant criteria? Sure! You could target your audience individually and hyper personalize your marketing. By crossing information from products and customers, the advanced machine learning algorithms used in this approach can predict behaviors and segment clients based on hard data and inferred factors.

This is what you can see in action when you come across a recommendation system, such as the ones used in Amazon to suggest products you might like. The AI behind these systems use hard data (such as the products you bought, their brands, their categories) and combine it with inferred factors (like aesthetics, potential uses for the product) to provide actionable insights on each customer.

This allows you to use recommendation systems to build very narrow segments based on highly specific tastes and interests and launch laser-focused campaigns for people that share them. For instance, you can target people who love Japanese culture, casual cooking, and home technology to promote a kitchen gadget to make sushi. Such a personalization level would be difficult to achieve with any of the other approaches, as it takes criteria that might be too specific to define.

The best thing about this is that it takes full advantage of machine learning, as the whole system is adjusted with each new customer interaction. In other words, a new purchase can better inform the underlying algorithm and trigger new associations that can end up in suggestions that are better tailored to that specific customer. That’s the most impact a machine learning algorithm can have in customer segmentation: dividing customers with such fine detail that allows you to customize your marketing strategy to an individual level.

Of course, this approach’s complexity makes it hard to attain, which is why it’s mostly used by large companies with a strong data-driven culture that has huge datasets to analyze and enough time to train the models to function correctly.