How to do Customer Segmentation using Machine Learning?
Customer segmentation is an essential step towards personalization. Machine learning can help automate this process. In this article, we'll discuss how to implement customer segmentation using ML.
Customer Segmentation is one of the powerful tools that can help you understand your customers better and target them accordingly. It can help you boost your sales, improve customer retention, and minimize the costs associated with customer acquisition.
Companies implementing segmentation are 60% more likely to understand customer challenges and 130% more likely to be aware of their intentions. Moreover, segmented email campaigns are more likely to have high open rates, CTR, and conversions.
In this article, we will cover how you can use customer segmentation effectively using machine learning for your business.
What is Customer Segmentation, and why use it?
Customer segmentation is the process of dividing customers into groups based on their shared characteristics. Customer segmentation allows businesses to understand their customers better and target them with personalized campaigns to which consumers are most likely to relate and respond.
Customer segmentation has various benefits, but here are some compelling reasons to consider implementing it in your business.
Enhance Customer Retention
Customer satisfaction is the core of every successful business strategy.
The so-called concept of NPS ( Net promoter Score) is entirely based on the idea that customers can be grouped into three categories: promoters, passives, and detractors.
Promoters are the consumers that are extremely satisfied with your products and services and are likely to continue using them and referring to others.
On the other hand, passive customers are happy but need to be more enthusiastic and at risk of defecting to a competitor. Detractors are unhappy customers who could easily damage your brand with negative word of mouth.
NPS is calculated by subtracting the percentage of detractors from promoters. For example, if you have an NPS of +40, you have 40% more promoters than detractors.
As the modern customer demands a personalized experience, customer segmentation can easily be used to identify and target promoters with tailored campaigns and messages that further improve their satisfaction.
Increase Customer Lifetime Value
CLV refers to the estimated net revenue a customer will generate throughout their relationship with your company. Several factors are considered in calculating CLV, but the two most important are customer retention and acquisition.
Here customer segmentation can help identify high-value customers and target them with personalized offers that encourage them to stick around. For example, you can run a loyalty campaign for customers who have been with your company for a year.
Reduce Customer Churn
Customer churn refers to the percentage of customers who stop doing business with you over a period of time.
Even a slight decrease in customer churn can significantly impact your business's bottom line. With customer segmentation, you can quickly identify at-risk customers and target them with personalized campaigns with offers to keep them engaged.
Boost Sales and Revenue
Businesses can easily understand the demands and expectations of each segment when their customers are grouped based on their shared characteristics. This allows them to customize their marketing campaigns that align and appeals to each segment, leading to more sales and revenue.
Streamline Customer Support
Using customer segmentation, you can quickly identify the most common issues faced by each consumer segment. This allows you to develop targeted support processes that are more likely to resolve customer issues.
Improve Business Decision-Making
Making driven business decisions is critical to the success of any business. However, it becomes a big challenge for many organizations when you need to understand your customers clearly.
With customer segmentation, you can develop a data-driven understanding of your customers, with which you can make informed decisions about everything from product development to marketing strategy.
Why should machine learning be used for customer segmentation?
Traditionally, customer segmentation only involved looking at a few high-level characteristics to segment customers into groups. This included criteria such as age, income, gender, or location. While this information can be helpful at some point, it doesn’t provide a complete picture of your customers and what they want.
You go beyond these characteristics with machine learning to thoroughly understand your customers. And by using customer datasets with their past interactions, behaviors, and even emotions, machine learning can create models that can accurately predict which customers are most likely to respond to which messages.
This is a powerful tool for organizations seeking to create more personalized customer experiences. Companies can quickly increase customer satisfaction, loyalty, and retention by segmenting customers based on their preferences and needs.
With ML, companies can also figure out how to save money by identifying which customers are likely to churn and taking preventative actions to avoid it.
Moreover, by understanding which customers are the most profitable, businesses can easily focus their efforts on acquiring and retaining customers like them.
In simple terms, machine learning provides a more complete and accurate picture of your customers which can further be used to create more personalized, targeted, and effective marketing campaigns.
How does ML customer Segmentation work?
Traditionally businesses have relied on manual strategies to segment their customers; however, this process takes time and often results in suboptimal segments.
Data Scientists need expertise in tools like Python, Pandas, deep learning, and Numpy. Implementing ML algorithms like the K-means clustering algorithm can identify different groups more accurately. However, building such machine learning algorithms is a challenging task.
Moreover, the libraries that aim to make the job easier, like sklearn or matpotlib, require significant code and effort to implement at scale. This manual approach involves a lot of effort, from data analysis on data frames to determining clusters and following GitHub tutorials.
On a technical front, clustering models need to find the K-value (the number of customer clusters). This is done with heuristics like the elbow method or optimization techniques. Furthermore, there is also the issue of centroid initialization, which significantly impacts the clustering algorithms’ performance.
Attri handles data processing, model selection, validation, and hyperparameter tuning automatically. This frees you from having data scientists' teams code these tasks.
When clustering algorithms are used, the platform will automatically find the optimal number of clusters in your customer data so that a new customer group can easily be detected.
Attri’s AI-powered ML platform offers an efficient way of segmenting your customer base. With Attri, you can quickly build high-quality segments without data science expertise. This allows you to power up your number of use cases, from market segmentation to categorizing users by their spending scores.
Here is how Attri’s built in AI-powered Customer Segmentation Blueprint can help you improve customer retention, marketing campaigns, and boost revenue.
ML Customer segmentation Best Practices
Segmenting your customers effectively is critical to delivering personalized experiences and driving growth. One key is tracking relevant metrics like customer lifetime value, average order value, and market share. These higher-order metrics will give you an exact understating of which segments are most crucial for your business.
Once your customers have been segmented, you can quickly boost sales through cross-selling and up-selling. Cross-selling is the practice of selling complementary products to your existing customers, while upselling is the practice of selling higher-end products to your current customers.
Both cross-selling and upselling are effective in increasing sales and revenue. However, it can be a big turn-off for your business if you need a clearer understanding of your customer's needs or preferences. This is why customer segmentation can be used to identify customers likely to be interested in your complimentary or higher-end products so you can target them with personalized offers.