The “RFM” in RFM analysis stands for recency, frequency and monetary value. RFM analysis is a way to use data based on existing customer behavior to predict how a new customer is likely to act in the future.

Когортный анализ и RFM анализ данных e-commerce.

Python scripts as the solutions of tasks resolved by me at Analyst course including Cohort analysis, RFM analysis, interaction with APIs etc.

Analysis and calculation of product metrics of users. Raw data taken from AppMetrica.

Project completed within the module "First project" of the Data Analyst course on the Karpov Courses platform

Проект с применением RFM анализа и когорного анализа.

Dataset Northwind Mini Project Data Engineering

This project aims to identify and present the target personas for a new Marketing campaign.

RFM analysis focuses on identifying and segmenting customers based on their purchasing behavior. Analyzed to understand and interact with customers. It can be used together for more effective marketing and customer management strategies.

Advanced SQL

Build a RFM (Recency Frequency Monetary) model for Retail Customers

The goal of the project is to build a cohort transition analysis of a user base by month. On a high level, this analysis should show how users' engagement develops over time and how users come into and fall out of the platform again. A cohort by definition is a certain status that's affixed to both a user and a concrete month, depending on user

Project explores the transaction history of an online household goods store through detailed data analysis, visualizations, and statistical hypothesis testing, offering valuable insights into purchase trends, customer behavior, and strategic product decisions.

Course of business intelligence bootcamp by Dibimbing

Creating a time-based cohort analysis of customer purchase transactions.

Sebuah project Data Analayst untuk mengetahui bagaimana pola pembelian pelanggan dalam periode waktu tertentu (Cohort Analysis)..

Business Analytics project to optimize marketing expenses, calculate metrics and provide marketing recommendations for Yandex Afisha

In this project, I aimed to analyze the profitability of products in an e-commerce dataset. I performed various SQL queries to extract valuable insights about product profitability, including the identification of the top 5 products with the highest profit margin, and unique combinations of brands and product lines with the highest profitability.

This Cohort table sorts customers by when they made their first purchase and calculate the retention rate of each groups by checking how many of them keep buying over months.