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How to Improve Customer Segmentation Using Machine Learning

A businessman's hand is touching a group of people to improve customer segmentation using machine learning.

Machine learning is making waves in many industries, and marketing is no exception.

Now,  marketing automation and machine learning can help your customer segmentation strategies too.

Only now has customer segmentation been a challenging feat. Traditional methods relied upon marketers manually sifting through considerable datasets to identify trends and patterns. Not only was this enormously time-expensive, but it also left the door wide open for all kinds of human error. 

Even the most skilled data analyst can’t capture all the intricate patterns and nuances in customer behavior that machine learning algorithms can uncover. With machine learning, analyzing vast swathes of customer data takes a fraction of the time and will produce much more accurate, reliable, and granular customer insights. 

In this article, you’ll learn how to use machine learning for customer segmentation and how your business will benefit. First, though, let's recap on what customer segmentation is precisely.

What is customer segmentation?

Just as a barista at your favorite cafe might make suggestions as you order, consumers these days expect their favorite brands to create personalized marketing experiences for them. They want content, suggestions, and deals that specifically speak to them and their needs. 

Customer segmentation is how businesses can start doing this. By doing so, businesses get to know the individual customers within their customer base, better understand their preferences and behaviors and improve customer services

Customer segmentation happens when a business organizes its customer base into distinct sub-categories defined by shared characteristics, such as demographics, behaviors, or preferences. The company can then target particular subsections of its customer base with marketing strategies tailored to their preferences. 

For instance, a cyber security business may segment its customer base geographically and discover a percentage of its customers operating in Qatar. In this case, they could then target that sub-category with information on how to buy .qa domain names safely.

Why is customer segmentation so important?

Once they’ve grouped customers with similar needs and behaviors, businesses can create archetypes, or customer profiles, to represent each distinct group. They can then start tailoring their marketing efforts to cater to the needs of these customer archetypes more accurately. 

At a more rudimentary level, it means that when the business identifies an individual customer's needs, it can then generalize that insight across a percentage of its customer base. 

So, let’s say a business providing communications solutions had a customer from an older age demographic ask an online chatbot, ‘What is an eFax?’. Based on this information, the company could predict that customers over a certain age won’t be familiar with this technology. With that in mind, it could start targeting the older segments of its customer base with information on e-fax applications.

Customer segmentation enables more potent marketing campaigns and allows a company to make data-driven decisions across all aspects of the business. When used to its full potential, machine learning can have knock-on effects on customer satisfaction, resource allocation, and overall business performance. 

Why machine learning is a better method for customer segmentation

AI, known as machine learning, allows computers to pick up a task without being explicitly trained to perform so. This kind of AI has an incredible array of tasks in the business world. There’s AI for call center solutions, data analysis, talent management, marketing automation, and anything else. 

One application of machine learning is customer segmentation, a drastic improvement to traditional methods. Put, AI is far better equipped for this kind of task. It can comb through more extensive data sets and recognize more subtle or intricate trends that might not be apparent to the human eye. 

In brief, customer intelligence systems with machine learning enable businesses to derive more useful data and insights from customer segmentation. This means they have more information to optimize their targeted marketing efforts.

Using machine learning for customer segmentation offers many benefits that can significantly improve marketing strategies. Let’s review some of these below.

1. More accuracy

AI can analyze complex patterns and relationships within data that might be difficult for traditional methods to detect. This results in more precise, meaningful client categories with more insightful data on their individual customers. Moreover, AI produces much more reliable information as it leaves no room for human error. 

2. More granularity

Machine learning is also capable of much more fine-grained analysis. It can identify subtle, detailed trends and patterns that the human eye might miss, thus achieving more granular customer segments. Consequently, it provides more precise, nuanced categorizations of customers. This can translate into more segments with more detailed insights connotated to them.

3. More efficiency 

AI can streamline your workflow for customer segmentation. By automating the process segmentation, you save the time and resources that would have been spent completing the task manually. Considering the size of the datasets some businesses have to deal with, this improvement in efficiency is enormous. This also means that customer segmentation can be much more scalable for companies.

4. More potential for integration

The greatest benefit of employing machine learning for client segmentation is that businesses can link it with other applications for further automation of marketing procedures.

For example, you could integrate customer segmentation with Dialpad’s contact center AI. This would form a kind of symbiosis between the two. The contact center AI could provide sentiment analysis of every call, allowing insight into more than a customer's character, but their emotions too. Simultaneously, information from customer segmentation could inform the dialog flows and interactions offered to customers from specific segments.

How you can implement machine learning in customer segmentation today

While customer segmentation with machine learning is much simpler in the long run, it requires some expertise to get it set up. If you're unfamiliar with machine learning techniques and domain knowledge, you’ll likely want to consult with data scientists or other experts in the field.

However, to give you an idea of the process, here is a step-by-step on how to implement machine learning for customer segmentation.

Step 1: Define objectives and data collection

First, set your goals and understand what you want to achieve from customer segmentation. Perhaps your focus is creating more personalized marketing campaigns, you want to make a data-informed decision over eCommerce localization, or you're just getting started collecting customer data and need to know where to begin. 

Then, start gathering data. You’ll likely need to use various sources, such as customer behaviors, purchase history, demographics, and interactions with your products or services.

Step 2: Data preprocessing and exploration

Could you clean your data to remove duplicates, handle missing values, and standardize formats? This is a pivotal step: without it, your data won’t be analyzed properly and may produce erroneous insights. At this point, it’s also worth exploring the data manually to get any insights or patterns that leap out at you. 

Step 3: Feature selection and engineering

Could you identify the features that will be used to determine segmentation? These could include any number of things: customer demographics, transaction history, behavioral metrics, and more. 

Step 4: Choose a machine learning algorithm

You can select an appropriate machine learning algorithm for your customer segmentation strategy. Common choices include clustering algorithms like k-means, hierarchical clustering, or more advanced methods like Gaussian Mixture Models or DBSCAN. I think this choice should be informed by your data's nature and segmentation goals.

Step 5: Data splitting

You’ll then need to divide your data into training and testing sets. As the name suggests, the training set is used to train the model. The testing set, unsurprisingly, tests the model's performance. A common split ratio is 70-80% for training and the rest for testing.

Step 6: Model training

Train the chosen machine learning algorithm using the training data. The algorithm will learn patterns in the data that can be used to segment customers effectively. Adjust hyperparameters as needed to optimize model performance.

Step 7: Customer segmentation

Now, let AI do its thing. You can apply the trained model to segment your customer data. It will then divide your customer base into distinct groups based on the patterns it learned during training, assigning each customer to a specific segment without your input. You don’t need to be present for it, you could watch it all unfold through your remote desktop for Android.

Step 9: Integration and strategy development

Finally, I'd like you to use this insight to tailor marketing strategies. 

In the long term, you’ll want to integrate your customer segmentation algorithm with relevant systems and continuously monitor the performance of your segments, updating the model as new data becomes available. 

Remember that customer segmentation is an ongoing process, and you’ll want to iteratively adjust and improve based on real-world outcomes for the best results.

Would you be ready to implement machine learning for customer segmentation?

Congratulations, you’re ready to start implementing machine learning for customer segmentation. The truth is, though, you should already be using machine learning for customer segmentation. The technology has been around for a while, the kinks have been ironed out, and your competitors are likely already using it.

Follow these steps to leverage machine learning to create more accurate, dynamic, and actionable customer segments.

Bio:

Austin Guanzon – Tier 1 Support Manager, Dialpad

Austin Guanzon is the Tier 1 Support Manager for Dialpad, the leading AI-powered customer intelligence platform that provides valuable call details for business owners using toll-free numbers from Dialpad. He is a customer retention and technical support expert with experience at some of the largest tech service companies in the US. Austin is also the co-founder of the California-based Infinity Martial Arts and has been an instructor in the sport. Austin has written for other domains, such as VMblog and ClickUp. You can find him on LinkedIn.

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