The impact of AI and ML on customer segmentation

Machine Learning (ML) capabilities can significantly impact a marketer’s customer segmentation strategy. There is a lot in the industry press covering Artificial Intelligence. ML is a subset of AI focused on extracting knowledge from data and learning from it.

Traditionally, marketers leverage common patterns between users to create segments. These could be demographics, like average income, age range or education level, or behavior, like past purchases, shopping frequency or average spend. Marketers are practiced at finding those patterns, but ML models can churn through massive amounts of data in a short period of time and draw some conclusions from the data. [1, 2, 3, 4, 5]

Key impacts of AI on customer segmentation: [1, 2, 6]

Increased Accuracy: ML models can identify subtle patterns and complex relationships within customer data, leading to more detailed segmentation than traditional methods based on demographics alone. [1, 2, 6]
Machine learning can adapt to changes in customer behavior in real time, which enables dynamic segmentation and the continuous updating of customer profiles. [1, 2, 7]
Hyper-Personalization: By understanding individual customer needs and preferences at a deeper level, AI enables marketers to deliver highly personalized marketing messages and offers. [2, 3, 8]
Predictive Capabilities: AI can predict future customer behavior, such as churn risk or potential lifetime value, allowing for proactive customer retention strategies. [1, 9]
Scalability: AI can efficiently process large volumes of customer data from multiple sources, enabling segmentation across large customer bases without manual effort. [1, 2, 10]
Improved ROI: By targeting the right customers with the most relevant messages, AI-powered segmentation can significantly improve campaign performance and return on investment. [1, 2, 5]

How AI is used in customer segmentation: [1, 2, 3]

Machine Learning Algorithms: Analyzing customer data through machine learning models to identify hidden patterns and group customers into meaningful segments. [1, 2, 3]
Natural Language Processing (NLP): Analyzing customer feedback and reviews to understand sentiment and identify customer preferences. [11, 12]
Behavioral Analysis: Tracking customer interactions across various channels (website, app, email) to identify behavior patterns and segment accordingly. [1, 2, 5]

Potential challenges with AI customer segmentation: [1, 12, 13]

Data Quality: The accuracy of AI segmentation heavily relies on the quality of customer data collected. [1, 12, 13]
Privacy Concerns: Ethical considerations regarding data privacy need to be addressed when utilizing customer data for AI segmentation. [12, 13]
Over-reliance on Algorithms: Marketers should not solely rely on AI insights without considering human intuition and domain expertise. [1, 4, 13]