Choosing a Segmentation Algorithm

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Behavioral Segmentation: Purchase history (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.

Psychographic Segmentation: 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:

Missing Values: Impute (fill in) missing data using statistical methods (mean, median) or remove rows/columns with excessive missingness.

Outliers: Identify and manage extreme values that could skew your analysis.

Data Types: Ensure columns are in the correct format (e.g., dates as datetime objects, numerical values as integers/floats).

Feature Engineering: This involves creating new, more informative variables from existing ones. For example:

Calculating days_since_last_purchase from transaction dates.

Once your data is clean and prepared, you'll select an country email list algorithm to group your customers. For segmentation, unsupervised learning (clustering) algorithms are commonly used as they identify inherent groupings without needing a pre-defined target variable.

K-Means Clustering: This is one of the most popular and straightforward clustering algorithms. It partitions data points into 'k' number of clusters, where each data point belongs to the cluster with the nearest mean (centroid).

How it works:

Initialize 'k' centroids randomly.

Assign each data point to the closest centroid.

Recalculate the centroids based on the mean of the assigned points.

Repeat steps 2 and 3 until the centroids no longer move significantly.

When to use: When you have a clear idea of the number of segments you want or can determine it empirically. It's efficient for large datasets.