The Impact Of Machine Learning In Customer Segmentation

Customer segmentation is a cornerstone of modern marketing. Businesses across all verticals gather data from several channels to better understand their customers and segment them into different criteria-based groups to target them with more precision. That’s because this practice is one of the best ways to boost your marketing efforts and improve your campaigns’ effectiveness.

Now, there are different customer segmentation techniques you can rely on to get that, from manually segmenting the customers according to a couple of predefined criteria to using sophisticated AI-based systems. Here, we’ll briefly review the four most common ones and then we’ll make a case in favor of machine learning-based segmentation analyzing the impact it can have on your marketing.

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Different Approaches To Customer Segmentation

Most experts in customer segmentation agree that there are four levels of segmentation. They talk about levels because they understand there’s a hierarchy in them as some are better than others. Let’s review them from the bottom up.

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    No Segmentation
    Naturally, the lowest form of customer segmentation has no segmentation at all. As you can surely anticipate, that’s mostly a huge mistake. Treating all your customers as if they were all the same in the golden age of personalization is just asking for people to go over to the competition. This feels like a too-obvious thing to point out, but you’d be surprised about how many businesses don’t segment customers at some point in their marketing efforts – especially in email marketing.

    This doesn’t mean that the no segmentation approach is useless. There are certain circumstances in which you can go down this road without major repercussions. For instance, if you own a business in a highly-specialized niche, there might not be a need to segment the audience, as the audience itself is too small and highly-interested in your services. Having no segmentation can also come in handy at the beginning of your business when you have a manageable number of customers who don’t call for a segmented approach.

    As a rule of thumb, having no segmentation is never a good approach in the long run, as the need for dividing customers into groups grows the more you grow your business.
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    Manual Segmentation
    Proper segmentation begins here, with humans taking care of the entire process of dividing customers. There are several ways to tackle this practice by using spreadsheets to employing BI tools. Usually, the division comes from criteria defined by the analysts themselves. Some of these criteria are relatively obvious (gender, age, location), and others are more specific (purchase history, amount of brand interactions).

    Using that criteria to perform a basic segmentation will enable you to target the resulting groups differently. However, it has several issues:
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    It needs constant updates to stay relevant.
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    There’s a limited number of criteria that can be used.
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    It’s practically impossible to scale.
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    There might be some biases thrown in by the human analysts.
  • 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.
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    Automated Segmentation
    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.
  • Recommendation Systems
    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.

Take Your Marketing To The Next Level With Machine Learning

Maybe it doesn’t make sense for you to adopt a recommendation system for your company, as you might not have the resources or the business scale to justify such an implementation. However, that doesn’t mean you can’t enjoy an ML-based approach to customer segmentation. Automated segmentation still is a step ahead towards more sophisticated campaigns that can drive better results.

For instance, you can implement machine learning to pre-filter your customer data and create groups that you can later migrate to another tool, such as one that you’ve worked on with a Salesforce application development company. Doing so can provide you with better groups and more actionable insights that can increase your engagement and drive your conversions up.

Many businesses are already leveraging the power of machine learning, even at a smaller scale. You can’t afford to be left behind, so you should consider implementing a similar solution. Just remember that you need to use the proper customer segmentation technique, the one that makes the most sense for your business.

Feeling a little lost about it? Don’t worry. At BairesDev, we can help you decide the best way to implement machine learning to boost your marketing. Just contact us and tell us all about your business goals and we’ll design a powerful marketing solution together. 

How Does Machine Learning Streamline Business Operations?

In the digital business world, machine learning makes for more accurate, more personalized, and more responsive consumer marketing. In the same your smartphone converges all your tech needs into a pocket-sized convenience, so to do machine learning possibilities bring together multiple points of information to make shopping, ordering, or booking easier.

Leading brands such as Google and Amazon are using voice-assisted search engines to capture customer information at the same time as they solve customer problems. The same applies to B2B or even internal uses of machine learning. 

You can empower aspects of your working life to work just as efficiently and proactively. There are basic uses such as automatically refilling a customer’s order once supplies run low, right through to advanced analysis that will inform you of shifts in customer demographics, product use, and the efficiency of your own work habits.

As more enterprises deploy machine learning solutions in their customer operations embracing at least a basic set of AI-boosted business tools becomes a must in order to maintain market competitiveness.

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