In the realm of online retail, understanding and predicting Customer Lifetime Value (CLV) is crucial. CLV represents the total value a customer brings to a business over their entire relationship. It's a key metric for decision-making, aiding in customer retention strategies, personalized marketing, and overall business growth.
- Conducted Exploratory Data Analysis (EDA) on an online retail dataset to unravel insights and trends.
- Employed statistical analysis to comprehend the significance of various features in relation to CLV.
- Extracted and processed relevant columns for CLV analysis, involving operations and calculations.
- Calculated Customer Lifetime Value using a tailored formula.
The dataset encapsulates diverse facets of online retail, featuring columns such as invoice number, invoice date, stock code, description, quantity, unit price, customer ID, and country.
Unveiled essential insights through visualizations:
- Identified and removed duplicate values to ensure data integrity.
Utilized the describe() function to derive key statistical measures, shedding light on data distribution.
Applied specific operations on the dataset to filter and manipulate necessary columns, leading to the calculation of Customer Lifetime Value.
- The analysis provides insights into customer behavior, helping formulate strategies for customer retention and targeted marketing.
- Statistical measures offer a quantitative understanding of the dataset.
- The calculated CLV serves as a foundational metric for making informed business decisions.