This repository contains RFM analysis applied to identify customer segments for global retail company and to understand how those groups differ from each other.

A Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.

The Super Store dataset contains data on order details of customers for orders of a superstore ie; chain of multiregional stores under a brand globally. This is a detailed analysis on customer behavior analysis.

Growth of the PIM industry include rising demand for PIM solution from flourishing eCommerce industry and increasing need to offering enhanced customer services are driving the growth of the PIM market globally. The global product information management market accounted for US$ 7.5 billion in 2019 and is anticipated to register a CAGR of 14.5%. The report "Global Product Information Management Market, By Enterprise Type (Large Enterprise, Small & Medium Enterprise), By Industry (BFSI, Healthcare, Telecommunication & IT, Government, Retail, Transportation & Logistics, Management, Energy & Utility, Media & Entertainment, and Others), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) - Trends, Analysis and Forecast till 2029”. Key Highlights: In October 2020, Pimcore introduced new features and improvements. The company updated its Pimcore platform and added new features, such as an editable dialog box, cache performance improvement, and tree sorting. In June 2020, Winshuttle formed a partnership with ABBYY, a digital intelligence company. The aim behind the partnership is to help organizations and businesses in digital transformation, which involves extracting data from physical documents and automatically loading it into SAP. Analyst View: Increasing investment in product information management Rising demand for centralized data storage of information related to products is driving the product information market. Centralized data storage is helping companies to easily manage and organize all the data related to its products. Data sources are updated with a single change in the centralized data storage, saving time and cost required for data management. Also, compliance and verification requirements are increasing due to the growing number of threats to information security. This provides safe and secure access to information stored in the centralized database. Access is granted only after completing verification of all the security credentials required. Product information management facilitates quick and easy access to the repository of information, at the same time strategic data storage techniques help in maintaining the data quality. Indexing and linking helps in reducing the time required to complete various processes related to data storage, increasing the operational efficiency. Marketing and sales of products are important processes to generate revenue. Growing PIM industry The market enables manifestation of products to achieve client centricity and unified customer view and provides a centralized system for improving the efficiency of promotional activities. All the distribution channels are managed effectively by using this solution. Integration of Big Data and business intelligence applications with cloud storage offers tremendous growth opportunities to the market. Browse 60 market data tables* and 35 figures* through 140 slides and in-depth TOC on “Global Product Information Management Market”, By Enterprise Type (Large Enterprise, Small & Medium Enterprise), By Industry (BFSI, Healthcare, Telecommunication & IT, Government, Retail, Transportation & Logistics, Management, Energy & Utility, Media & Entertainment, and Others), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) - Trends, Analysis and Forecast till 2029 Key Market Insights from the report: The global product information management market accounted for US$ 7.5 billion in 2019 and is anticipated to register a CAGR of 14.5%. The market report has been segmented on the basis of enterprise type, application, and region. Depending upon enterprise type, the large enterprises shares the highest market due to the adoption of PI solutions and services is higher in large enterprises. The large enterprises heavily invest in advanced technologies to increase their overall productivity and efficiency. By application, the media & entertainment segment holds the largest share in the market. As most of the populations are staying at home, the usage of media and entertainment has increased with double digit growth. Product information offers high visibility, scalability and service optimization that can handle challenges occurred due to sudden increased demand in media and entertainment industry vertical. By region, North America is the largest market for product information management. The emerging demand to maximize value from the centralized master data and reference data, with ongoing demands of gaining meaningful insights from this consolidated master data is expected to further influence the adoption of PIM systems positively in the North American region during the coming years. The market in Asia-Pacific is expected to witness potential growth opportunities owing to the fast adoption of multi-domain PI software which is expected to enable better services in terms of performance, quality and capacity during the forecast period. To know the upcoming trends and insights prevalent in this market, click the link below: https://www.prophecymarketinsights.com/market_insight/Global-Product-Information-Management-Market-4573 Competitive Landscape: The prominent player operating in the global product information management market includes SAP AG, IBM Corporation, Oracle Corporation., Informatica LLC, Riversand Technologies, Inc., Stibo Systems, ADAM Software NV, Agility Multichannel Ltd., InRiverAB and Pimcore GmbH. The market provides detailed information regarding the industrial base, productivity, strengths, manufacturers, and recent trends which will help companies enlarge the businesses and promote financial growth. Furthermore, the report exhibits dynamic factors including segments, sub-segments, regional marketplaces, competition, dominant key players, and market forecasts. In addition, the market includes recent collaborations, mergers, acquisitions, and partnerships along with regulatory frameworks across different regions impacting the market trajectory. Recent technological advances and innovations influencing the global market are included in the report.

The Marketing Department of a global retail store is running a customer appreciation campaign on the occasion of Christmas and New Year. They need to deploy appropriate marketing program for each customer group and exploit potential customers to become loyal customers. This Python project provided a segmentation analysis base on RFM Model.

Tableau dashboards analyzing customer behavior, sales performance, and profit trends across global markets using real-world retail data. Includes customer segmentation, regional sales analysis, product insights, and forecast validation.

"Global Retail Sales exploratory data analysis (EDA) project focused on uncovering revenue trends, customer patterns, product performance, and profitability insights using Python, Pandas, NumPy, and advanced data visualization techniques."

A comprehensive Power BI analysis of the Global Superstore dataset, uncovering key performance drivers, profitability challenges, and customer trends across major regions like the USA, Nigeria, Australia, and Southeast Asia. Designed to support data-driven decision-making in global retail strategy.

Global Superstore is a multinational retail organization that sells a wide range of products to customers all over the world. To better understand their business performance and make informed decisions, Global Superstore has utilized data analysis and Microsoft Power BI tools to generate insights into their sales, profits, and customer behavior.

DataSpark is a retail analytics project for Global Electronics leveraging Python, SQL, and Power BI. It uncovers customer insights, sales trends, and store performance to optimize marketing, inventory, and operations. Features include clean datasets, SQL-driven analysis, and interactive dashboards, driving data-driven growth and decision-making.

The primary aim of this analysis is to determine how a company's profits, sales, and products meet customer demands across various regions, segments, countries or markets etc. Globally, millions of consumers are increasingly turning to online shopping, a phenomenon known as the "e-commerce market" or the "online retail market."

Summary The global "Retail Laundry Services" analysis offers in-depth perceptions of the size of the market by product type, application, and end-user. Based on their expenses, gross margins, revenue, market share, income, goods, and other firm information, the top rivals in the global "Retail Laundry Services" market research are evaluated. The research offers a thorough analysis of the supply chain, global marketing, opportunities, risks, and demand variables in order to accurately predict the global "Retail Laundry Services" category. Additionally, a number of strategies—including expansion, collaborations, mergers and acquisitions, production base, key player analysis, and key player sales—have all been carefully looked at in this study. This study includes a thorough description of significant industry developments, requirements, and regulations as well as the micro and macroeconomic metrics used in this study. The primary market driving factors, difficulties, and dangers that have an impact on growth patterns are also tracked in the global market overview section of this market report. The market was investigated in light of expected customer demand. The reader may find this research data useful in understanding the quantitative growth elements of the global "Retail Laundry Services" market. The "Retail Laundry Services" market research also offers detailed segmentation of the global market overview and a thorough traction analysis of the entire "Retail Laundry Services" market globally. The global market report provides verified market research to assess the leading vendors by integrating all necessary goods and services to pinpoint the positions of the key industry participants in the "Retail Laundry Services" market. Similar to this, the global "Retail Laundry Services" market offers a thorough analysis of cutting-edge competitor research as well as emerging market trends with market drivers, constraints, challenges, and lucrative opportunities to provide accurate insights and the most recent information for well-informed decisions. By examining the competitive landscape of the leading players and supporting businesses in securing their share by being aware of the various growth plans, the global "Retail Laundry Services" market report also offers a comprehensive view of the global market. Free Sample Report + All Related Graphs & Charts @ https://www.adroitmarketresearch.com/contacts/request-sample/3281 Different market sizes have been forecasted for all categories and sub-segments using a bottom-up methodology based on previous and expected patterns. This study provides an overview of the industry, sales data from the past and the future, a growth analysis, statistics on costs, production, and demand, and a business prediction. The "Retail Laundry Services" research study examines the important industry dynamics and significant sub-segments. Likewise, from a market standpoint, the 'Retail Laundry Services' research includes each top supplier in the global market's share and production capabilities. Purchase the report at https://www.adroitmarketresearch.com/researchreport/purchase/3281 Key Points Covered in the Report: A thorough analysis of value and volume at the worldwide, sector, and regional levels is included in the global 'Retail Laundry Services' market report. The study offers a full business size 'Retail Laundry Services' from a global point of view through a review of past facts and possible scenarios. Geographically, the Retail Laundry Services of market analysis includes the number of regions and their contrast of revenue. The The market analysis focuses on ex-factory costs, output volume, market share & sales for every manufacturer on a company level basis. Key Reasons to Purchase this Report: A comprehensive study of market size, share and dynamics is a global 'Retail Laundry Services' market research report and a thorough survey of developments in the field. It offers an in-depth overview of revenue growth and an analysis of the total business benefits. In addition to the strategic landscape for commodity pricing and marketing, the 'Retail Laundry Services' industry research also provides key players. This is a new post covering the latest impact on the target market. The research report addresses the rapidly evolving market climate as well as the initial and future impact assessment. ABOUT US: Adroit Market Research is an India-based business analytics and consulting company. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a market’s size, key trends, participants and future outlook of an industry. We intend to become our clients’ knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code– Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps. CONTACT US: Ryan Johnson Account Manager Global 3131 McKinney Ave Ste 600, Dallas, TX 75204, U.S.A Phone No.: USA: +1 9726644514/ +91 9665341414

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The Super Store dataset contains data on order details of customers for orders of a superstore ie; chain of multiregional stores under a brand globally. This is a detailed analysis on customer behavior analysis.

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Power BI Dashboard for Retail Global Customer Analysis

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This project implements a full end-to-end analytics workflow to analyze the Global Electronics Retailer dataset, encompassing transactions, products, customers, stores, and countries.

Conducted detailed sales analysis using SQL Joins on eight related tables

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Retail marketing analytics on 540K+ transactions using Python, applying data cleaning, feature engineering, and EDA to build an automated RFM model for customer segmentation. Leveraged Pandas, NumPy, and visualization tools to uncover trends, enable targeted strategies, optimize campaigns, and drive revenue growth through data-driven insights.

Python RFM Analysis for SuperStore: Segmenting global retail customers for targeted marketing

Retail chains analytics, their analysis, statistics, customer segmentation and creation of global offers

Global Retail dataset Customer Segmentation based on K-means Algorithm and PCA Analysis.

Data analysis of global electronics retail sales and customer behavior using Python, SQL, and Excel.

A Transformer-Based Analysis of Thematic Drivers and Sentiment on Reddit for the Global Retail Customer Experience.