The concept of customer segmentation is a critical one in modern business strategy. In essence, it is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing. Segmentation enables businesses to target specific customers with messages that are tailored to their needs and thus increase marketing efficacy. However, it’s worth exploring how this old as ages practice has evolved, and how it can now be viewed as a product in itself.
Customer Segmentation: An Overview
For as long as there has been business, there has been customer segmentation. Historically, it has been considered more of an art than a science, with businesses using intuition and experience rather than concrete data. However, with the rise of machine learning and data science, efforts have been made to transform this art into a more precise and accurate science.
Despite these attempts, traditional data science methods like clustering haven’t often taken off beyond the model build itself. The reason? These machine-learning customer segmentation models often fail to produce business-friendly, actionable segments, which leads us to the pivotal aspect of customer segmentation as a product.
Reading the Power of Customer Insights: Unlocking Hidden Opportunities for Your Business can prove helpful in unlocking ideas concerning customer segmentation.
Empowering Business Users Through Segmentation
If machine learning models are not yet able to create actionable segments, the best course of action is to empower business users with the ability to create useful segments themselves. To do this effectively, businesses need to make it easy for their teams to manipulate and explore the data.
Traditionally, this would involve running complex queries and waiting for segment counts – a time-consuming process and a significant bottleneck. To alleviate this issue, segmentation needs to follow a “train of thought” approach. In this process, business users can quickly identify the number of customers meeting certain criteria and then drill down further into that data.
The Three-Step Process of Segmentation
Creating customer segmentation as a product is a three-step process:
Step 1
Quantifying the size of a customer segment is the initial step in identifying its potential value. This allows businesses to get the most out of predictive modeling software for targeting customers of their preferred base by grasping the scope and significance of the segment.
Step 2
Determine the profile of each segment to provide deeper insights into customer behaviour and preferences. This can include metrics such as average frequency, value, favourite products, and other relevant attributes to help create a comprehensive understanding of each segment’s characteristics.
Step 3
Once the customer segments are identified and profiled, businesses must transform them into action-ready entities. This may involve initiating targeted smart shopping campaigns, personalised communications, or exporting data for further analysis or integration into other systems.
This clear, step-by-step process helps businesses create actionable segments that can drive marketing and sales efforts.
Segment Management And Refreshing
Once a segment is created, businesses must decide whether it is a one-off need or something that will be revisited often. This decision introduces two significant complications. Firstly, the segment has to be stored for future use, meaning it has to be easily searchable, categorised, and similar segments need to be grouped. Secondly, the labels indicating whether a customer belongs to a segment or not must be refreshed regularly. This is a process that requires careful consideration, especially in large businesses with high data volumes where refreshing segments daily can be a challenging task.
The Future of Customer Segmentation
Looking to the future, it’s entirely possible that AI could change the face of customer segmentation. Advanced pattern recognition could help identify useful segments within data without the need for human intervention. This raises intriguing opportunities and suggests that the original premise of “data science” clusters being less useful than segments may well get reversed in the future.
The concept of customer segmentation as a product is a fascinating one. It involves leveraging data in a user-friendly manner to create actionable segments and drive business growth strategies. With continuous advancements in AI, this approach could revolutionise how businesses understand and target their customer base, making the art of segmentation more scientific and precise than ever before.