E-Commerce
Analytics Glossary

A customer segmentation technique based on Recency, Frequency, and Monetary value of purchases.

The practice of dividing customers into groups based on shared characteristics or behaviors.

Grouping customers by shared acquisition date to track behavior patterns over time.

Delivering personalized content or offers based on a customer's past actions and behavior patterns.

The percentage of customers who stop purchasing from your store within a given time period.

A data mining technique that discovers products frequently purchased together.

If-then rules that describe the probability of relationships between products in transaction data.

The three core metrics used to evaluate the strength and significance of product associations.

The foundational algorithm for discovering frequent itemsets and association rules in transaction data.

AI-powered suggestions of products a customer is likely to want based on data patterns.

Recommending complementary products to increase the total order value.

Encouraging customers to purchase a higher-value version of the product they are considering.

The average amount of money spent each time a customer places an order.

The total revenue a business can expect from a single customer over their entire relationship.

The percentage of website visitors who complete a desired action, typically making a purchase.

Using historical data and statistical models to forecast future customer behavior and trends.

Using quantitative evidence rather than intuition to guide business strategy.

Visual representations of data that use color gradients to highlight patterns, density, or intensity.

Selling multiple complementary products together as a package, typically at a discount.

Automatically adjusting product prices based on demand, competition, and customer segments.