customer-analytics
There are 76 repositories under customer-analytics topic.
joaolcorreia/RFM-analysis
Python script (and IPython notebook) to perform RFM analysis from customer purchase history data
mukulsinghal001/customer-lifetime-prediction-using-python
What is CLV or LTV? CLV or LTV is a metric that helps you measure the customer's lifetime value to a business. In this kernel, I am sharing the customer lifetime value prediction using BG-NBD, Pareto, NBD & Gamma Model on top of RFM in Python.
jalajthanaki/Customer_segmentation
Analysing the content of an E-commerce database that contains list of purchases. Based on the analysis, I develop a model that allows to anticipate the purchases that will be made by a new customer, during the following year from its first purchase.
rsquaredacademy/rfm
Tools for Customer Segmentation using RFM Analysis
jalajthanaki/Customer_lifetime_value_analysis
Customer life time analysis (CLV analysis). We are using Gamma-Gamma model to estimate average transaction value for each customer.
SooyeonWon/customer_analytics_fmcg
Customer & Purchase Analytics using Segmentation, Targeting, Positioning, Marketing Mix, Price Elasticity
trieu/leo-cdp-free-edition
The binary build of LEO CDP Free Edition for training purposes
shawn-y-sun/Customer_Analytics_Retail
Customer Analytics for a FMCG company (K-means clustering, PCA, logistic regression, linear regression)
gknox79/customer-analytics
This repo hosts the course content of Customer Analytics, taught at Tilburg University by George Knox last taught Fall 2022.
azeezat123/Customer-Segmentation-Analysis
Pickl.AI’s Datathon - 4
jalajthanaki/Customer_churn_analysis
Predicting customer churn using scikit-learn
t-varun/Customer-Analytics
Coursera-Customer analytics
cientificas-de-datos/2019BWB
Bootcamp Women in Data - Bogotá, COL
ashendrasharma/Customer-Segmentation---Using-k-means
Customer Segmentation - Using k-means, About: Customer Segmentation is a popular application of unsupervised learning. Using clustering, identify segments of customers to target the potential user base. They divide customers into groups according to common characteristics like gender, age, interests, and spending habits.
abhijitpai000/customer_lifetime_value
Trained a Probabilistic Model to forecast the frequency of purchases and how likely a customer is to churn in a given time period using their historical transaction data.
PeerChristensen/Customer_Analytics
Methods for doing customer analytics in R
zhangkelly014/Customer-Analytics-in-Python
Key: clustering, using logistic regression to build elasticity modeling for purchase probability, brand choice, and purchase quantity & deep neural network to build a black-box model to predict future customer behaviors.
pareshg18/Customer-Segmentation
R-Analysis: Identifying high value customers and low value of customers using RFM modelling
sheikhomar/customer-analytics
Customer segmentation, price elasticity modelling and conversion modelling.
vishalv91/Customer-Analytics
The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.
ZhijingEu/Cohort_Retention_Analysis
This repo is a code demo that implements a custom Customer Retention Analysis class with a number of helpful methods/functions to generate customer churn insights frequently used for marketing analytics to understand the growth and change of your customer base (new vs retained vs lost) .
abhijitpai000/predictive_lead_scoring
Trained a model that estimates if a lead is likely to be converted based on lead behavior in historical customer data using ML.
amritanair254/Machine-Learning-Models
Exploration, visualization and implementation of machine learning models on customer, transnational other such data of Toyota, Universal Bank, Wayfair, etc (R, SAS and Alteryx)
ddsgithub/Python-based-Analysis-of-Simulated-Transaction-and-Customer-Data
"Business Insights from Transaction Data" is a Python project for e-commerce and financial services to optimize customer funnel and KPIs. It involves creating synthetic data to model realistic transaction and customer data, which is then analyzed and visualized to support data-driven decision making.
HannahIgboke/Customer-Analytics-Preparing-Data-for-Modeling
What are some of the best ways to prepare a large dataset for modeling? In this project, I optimized the memory usage of the dataset to ensure that it is stored as efficiently as possible to allow models to run faster.
huzaifakhan04/quantium-data-analytics-virtual-experience-program
This repository contains results of the completed tasks for the Quantium Data Analytics Virtual Experience Program by Forage, designed to replicate life in the Retail Analytics and Strategy team at Quantium, using Python.
jomariya23156/customer-analytics-stp
Customer Analytics with STP (Segmentation-Targeting-Positioning) Marketing strategy
tamasdinh/starbucks-targeting
Customer targeting model to optimize promotion targeting, on simulated data from Starbucks. (work in progress)
WajdiBenSaad/Kaggle_Customer_Transation_Prediction
My Final Submission for the 'Santander Customer Transaction Prediction'. I have participated in this very tough and interesting competition on Kaggle a while ago and I finally got the time to put all the work together in this Repo.
aradhya29/CUSTOMER-ANALYTICS-FOR-PURCHASING-BEHAVIOUR-PREDECTION
# Customer-Analytics-and-Behaviour-Prediction Customer relationship management is one the most significant managerial tasks in organizations. Many companies may usually adopt a strategy that is known as target marketing. The marketing managers who may consider using target marketing will usually break the market down into groups and to target the most profitable segments. The current challenge is the effective utilisation of the data in CRM processes and selection of appropriate data analytics techniques. Hence, a prerequisite for the development of this customer-centric strategy is the specification of the target markets that the companies will attempt to serve. Therefore, this project comes forward as solution that begins by the Customer analysis which requires splitting up the customers into various segments that helps us in extracting the most profitable ones i.e. the loyal customers and then predicting their purchasing behaviour based on their past purchases.
leonard-henriquez/PokemonGo
Customer analytics project on PokemonGo
shrutibalan4591/E-commerce-Customer-Churn-Prediction
This is an end-to-end ML project, which aims at developing a classification model for predicting if a customer for an ecommerce business will churn or not in the following month
zhangkelly014/Online-Superstore-Insight-Analysis-Challenge
Key: descriptive statistics and exploratory data analysis, forecasting (linear regressions, ARMIA, Prophet), and a Tableau dashboard that delivers customer insights such as RFM analysis.
mayankyadav23/Quantium-Data-Analytics
🎯 This repo contains the work completed during the Quantium Data Analytics Job Simulation on Forage. The focus was to analyze customer transaction data, provide insights, and recommend data-driven strategies. Key areas include data preparation, customer segmentation, uplift testing, and reporting for decision-making.✨
Wburto/2Market
In a fictional situation I helped 2Market, a leading international supermarket chain, leverage data analytics to gain deeper customer understanding to boost sales. By analysing consumer datasets, I uncovered valuable insights into customer behaviour, sales trends, and advertising effectiveness.