Data segments help effectively analyze data by dividing it and grouping similar data together according to the chosen parameter or filter.
What is Data Segmentation?
Data segmentation is the process of dividing data you hold and grouping similar data together according to the chosen parameter or filter.
Data segmentation helps use data more effectively for business decisions, marketing, and operations. Examining data from various perspectives helps drive new actionable insights for decision stakeholders. Examples of various data segments include:
- Age-wise customer group segments that impact buying patterns
- Customers and prospects where prospect segment represents most likely consumers for a brand
- Combination of two features like customers accessing web portal using iPhone as well as MacBook
- Customer segment with the abandoned cart that needs special attention of online sellers
Why Does Data Segmentation Matter?
Data segments provide a way to analyze data from various perspectives deriving new and meaningful insights about data. Data segmentation ensures different benefits including:
- It simplifies data analysis based on a specific feature or a combination of features and helps identify potential issues affecting the model performance.
- It allows checking prediction results at various granularities and generating alerts for performance issues specific to a segment of the data stream, making model enhancements easy.
Examples illustrating how data segments help
Segmentation helps track which specific segments might cause model degradation. For example, a recommendation system used for an e-commerce website faces performance issues after two years of its productionalization. This performance degradation is due to differences in data used to train the model before two years and current data consumed by the production model. With segmentation, ML professionals noticed that the age-group 50-55 segment affects the model performance due to more tech-savviness of the said segment in the last two years. This change affects their habits of online shopping.
A survey conducted by an e-commerce retailer concluded that 30% of people enjoy eating popcorns. Is that the only insight derived by the retailer? No. After digging deeper with a data segment - people who like cold drinks they discovered that 70% of people in this data segment enjoy popcorns. Segment analysis helps derive new insights and might change your perspective.
Segmenting Data using Censius AI Observability Platform
Data segmentation aids ML professionals with advanced monitoring capabilities. Censius AI Observability Platform enables automatic or manual data segmentation to serve rule-based, customized, or hybrid data segmentation and monitoring needs. This segmented analysis allows analyzing custom-defined data segments, a combination of different features, and segments across various granularities. The platform offers actionable visualizations built on top of the idea of data segments.
Such a valuable early warning mechanism helps data scientists recognize issues beforehand and strategize actions faster.