Customer segmentation is the backbone of modern marketing. In a world where personalization is so important, this simple yet effective strategy is integral to delivering a rich customer experience. Creating targeted and relevant marketing campaigns, performing budgeting and resource allocation, and designing new products are also important. While traditional segmentation has remained very useful, today's marketing demands are beginning to outweigh its efficiency. That's why marketers and sellers have started experimenting with AI-based segmentation. The results were expected to be excellent, giving early adopters a competitive advantage and an increase in sales.
WHAT IS CUSTOMER SEGMENTATION?
Customer segmentation is the process of dividing the individuals in the customer base into smaller segments. The classification is based on similarities in characteristics or attributes shared by the customer. Before the 1990s, when customers didn't have the luxury of options, generic messaging and one-size-fits-all products and services were acceptable. However, with the advent of customer experience, companies have had to switch to more relevant communications and personalized services. Customer segmentation is the very first solid step towards achieving those goals. After breaking down customers into segments, marketers and sellers can take action to improve their understanding of customers and deliver better services.
There are several determinants that can be used to create segments. The five most popular types of customer segmentation models used by modern businesses are:
- DEMOGRAPHIC SEGMENTATION: age, gender, marital status, occupation, sex
- GEOGRAPHICAL SEGMENTATION: location, climate, weather, language
- BEHAVIOR SEGMENTING: email opens, newsletter signups, link clicks
- PSYCHOGRAPHIC SEGMENTATION: values, hobbies, opinions, lifestyle choices, social status
- TRANSACTIONAL/VALUE BASED SEGMENTATION: total amount spent, number of products purchased, lifetime value, types of products purchased
TRADITIONAL SEGMENTATION VS AI-BASED SEGMENTATION: WHAT'S THE DIFFERENCE?
In its most basic form, traditional segmentation is still quite effective. It still delivers good results, especially for customers with a small customer base. However, the power of traditional customer segmentation diminishes with increasing datasets. Using common attributes to segment thousands of customers increases the risk of creating large, generic groups that limit the ability to deliver a high level of personalization. Likewise, this segmentation method can weaken marketing campaigns. With companies unable to identify characteristics that make each customer unique, it becomes nearly impossible to design campaigns relevant to their needs.
On the contrary, AI-based segmentation eliminates many of the limitations of the traditional model. AI machines can process large data sets in seconds and reveal patterns hidden from the human eye. Therefore, it becomes possible to divide customers into smaller and more focused segments. Because customer segmentation based on AI is scalable, businesses can continue to thrive even as the customer base grows exponentially. For marketers concerned about the workload of creating individual campaigns for hundreds of segments, AI can automatically generate marketing content for individuals in each segment. Consequently, the optimization of marketing campaigns is an automatic process with AI. It is simply the perfect solution.
HOW DOES AI-BASED SEGMENTATION WORK?
Artificial intelligence is a very robust system that can contribute to customer segmentation in several ways using different techniques. By opting for AI-based segmentation, companies are assured of accuracy, efficiency and cost-effectiveness. But how exactly does AI achieve such superior segmentation? Here are the four most common techniques:
NATURAL LANGUAGE PROCESSING (NLP)
NLP is a subset of AI that focuses on human and computer interactions. It contributes to AI-based segmentation by being able to analyze customer feedback using techniques such as sentiment analysis, topic modeling and named entity recognition. For example, sentiment analysis can determine the positivity or negativity of sentiments expressed in feedback. Therefore, it can measure the degree of satisfaction a customer has with available products and services.
On the other hand, topic modeling and named entity recognition will identify keywords and information in texts. This provides insight into the topics, products and services that customers discuss on different platforms. All these techniques provide a better understanding of the customers and their interests and priorities. This information can be used to create outstanding segments that produce repeatable results.
PREDICTIVE MODELS
Predictive modeling is a machine learning technique that can predict future events using various statistical algorithms to analyze existing data. Likewise, this ML technique can process customer data, identify key attributes and accurately predict which segment they fit best into. Predictive modeling has several algorithms that allow it to make predictions. They include decision trees, random forests, and gradient gain.
It makes predictions by processing data and splitting it into training and test data sets. The selected algorithm then uses the training data set to generate models that can make predictions, while the test set evaluates the model's performance. Therefore, the AI-based segmentation model has its own feedback loop that improves the accuracy of its predictions when new data becomes available.
REINFORCEMENT LEARNING
This machine learning subset makes decisions based on the feedback received. Therefore, reinforcement learning can continuously learn from customer behavior data and improve AI-based segmentation. For example, a customer may respond to a marketing campaign on Twitter with a direct message. The machine can then use this new information to build or update its understanding of the customer. This allows marketers to quickly adjust their customer segments for better results.
CLUSTERING ALGORITHMS
Of all common techniques, clustering algorithms are the most used for AI-based segmentation. As the name suggests, the algorithm can group customers into clusters based on similarities in attributes defined by the marketer or seller. K-means is the most common clustering algorithm in use today. Alternatives are hierarchical clustering and DBSCAN (Density-based Spatial Clustering of Applications with Noise). Since each of these applications has its strengths and weaknesses, it is often best to combine multiple algorithms for more accurate results.
WHAT ARE THE WAYS AI-BASED SEGMENTATION HELPS BUSINESSES?
1TP208 AI-powered transformation offers ecommerce businesses plenty of opportunities to grow and outperform their competition. Here's a summary of five proven ways these advanced customer segmentation methods can transform businesses today:
IMPROVED TARGET
AI-based segmentation enables companies to gain in-depth insights into their customers. This extends to understanding their interests and defining attributes, pain points and preferences. With the wealth of such information, marketers can create more targeted campaigns that target their individuals directly. Instead of reaching out to generic ads, marketers can design more specific campaigns for better lead generation and nurturing, customer acquisition, and customer retention.
MORE EFFECTIVE PERSONALIZATION
Personalization is a non-negotiable customer expectation these days. They want sent messages to be relevant to them. They also want services like product recommendations and email marketing to revolve around the products and services that interest them. While traditional segmentation supports personalization efforts well, AI-based segmentation takes personalization to the next level. Using any of the techniques and algorithms mentioned above, AI can identify hidden patterns and achieve any type of segmentation. As a result, it becomes much easier to send relevant emails and recommend the right products.
OPTIMIZED CAMPAIGNS
As any marketer understands, marketing campaigns are critical to running a business. It's how companies convert prospects and get higher lifetime value from existing customers. AI-based segmentation gives marketers actionable insights into their customers, enabling marketers to design outstanding campaigns. It also enables real-time adjustment of custom segments based on new and emerging data, meaning companies can quickly adjust their marketing strategies for better results. In addition, AI can measure the effectiveness of campaigns, while large language models such as ChatGPT be able to create engaging content that is perfectly suited to the segments. All this simplifies the process of optimizing marketing campaigns and getting great results.
IMPROVED CUSTOMER EXPERIENCE
1TP208 AI-powered transformation can improve the customer experience in many ways. These include more targeted campaigns, personalized services and relevant messages. In addition, companies can use AI to streamline shopping experiences and improve customer service. This can take the form of additional support for customers to alleviate their pain points. For example, Segmentatie can help identify customers at risk of churning and then push marketers to address the issues that are hindering their experience. This way companies can keep more customers happy and satisfied.
HIGHER PROFIT MARGINS
As a culmination of the above, AI-based segmentation ultimately leads to more profit. Satisfied customers are likely to make more purchases, increasing their lifetime value and generating more revenue. Similarly, companies save in the long run thanks to AI's ability to reduce costs and increase productivity. For example, the various AI techniques mentioned above will reduce the amount of manpower required to perform tasks, allowing companies to save on labor costs. It also helps staff be more productive by taking over repetitive tasks and allowing them to focus on more important activities. This allows companies to save and earn more, and use the profits to expand their business.
Finally, AI-based segmentation is the way forward. Using techniques such as natural language processing, predictive modeling, cluster algorithms and reinforcement learning, AI can perform unprecedented levels of segmentation for personalization and marketing purposes. That is why companies today have to invest in this tool to improve their services improve.
