Handling Missing Values: Decide how

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Third-Party Data Providers: Supplement your internal country email list data with external demographic, psychographic, or behavioral data for a more holistic view.
Data Cleaning and Preprocessing: Raw data is rarely perfect. This crucial step involves:

to address gaps in your data (e.g., imputation, removal).
Standardization and Normalization: Ensure data is in a consistent format for accurate analysis.
Outlier Detection: Identify and manage unusual data points that could skew your analysis.
Feature Engineering: Transform raw data into meaningful features that highlight customer characteristics. For example, instead of just individual purchase dates, you might calculate "days since last purchase" (Recency) or "average order value."

on your business objectives, decide which attributes are most relevant for grouping customers. This could be a combination of demographic, behavioral, and psychographic factors.

Applying Segmentation Techniques: This is where the magic happens. Various techniques can be employed:

Rule-Based Segmentation: Simple, pre-defined rules (e.g., customers who spent over $500 in the last 3 months).
RFM Analysis: A widely used technique that scores customers based on Recency, Frequency, and Monetary value.
Clustering Algorithms (Machine Learning): For more complex and nuanced segmentation, unsupervised machine learning algorithms like K-Means, Hierarchical Clustering, or DBSCAN can identify natural groupings within your data without predefined rules. These algorithms are particularly powerful for uncovering hidden patterns.
Decision Trees: These models can classify customers into segments based on a series of decisions derived from their attributes, offering interpretability.