Psychographic Segmentation

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Demographic Segmentation: Age, gender, income, education, occupation.

Geographic Segmentation: Location, climate, cultural preferences.

Behavioral Segmentation: Purchase history country email list (items bought, frequency, monetary value), website interactions (pages visited, time spent, clicks), engagement with marketing campaigns, product usage patterns. This is often the most insightful type.

Lifestyle, values, attitudes, interests. While harder to measure directly, these can often be inferred from behavioral data.

Value-Based Segmentation (RFM Analysis): Recency (how recently a customer purchased), Frequency (how often they purchase), and Monetary (how much they spend). This is a powerful behavioral segmentation technique for identifying high-value customers.

Key Steps in Implementing Customer Segmentation Code
Implementing customer segmentation using code typically follows a structured process:

1. Data Collection and Preparation
This is arguably the most critical step. The quality of your segments directly depends on the quality of your data.

Data Sources: Gather data from all relevant sources: CRM systems, e-commerce platforms, website analytics (Google Analytics, Adobe Analytics), marketing automation tools, transaction databases, and customer surveys.

Data Cleaning: Raw data is rarely perfect. Use code to handle.