Selected Work, Research & Data Contributions

Portfolio

Turning Data Into Research, Resources & Practical Knowledge

My portfolio reflects my work across data analytics, statistical research, machine learning, real-world dataset development and practical data education. From publishing applied research and developing datasets to creating practical tutorials using major real-world databases, my work focuses on one objective:

Using data to solve real problems, generate evidence and make analytical knowledge practically useful.

Research Datasets Educational Content Projects
01 · Published Research

Strategic Customer Lifetime Value Prediction

Leveraging Machine Learning to Maximize Profitability in Retail: A Case Study Using 2010-2011 Online Retail Data

Wilfred Nyakeri Retail Analytics | Machine Learning | Customer Analytics Published: SSRN, 2025 DOI: 10.2139/ssrn.5291821

This research develops a machine-learning-driven framework for predicting Customer Lifetime Value (CLV) and strategically segmenting retail customers using transactional data from the 2010-2011 Online Retail dataset.

The study evaluates customer revenue potential across 3-month and 6-month observation windows and compares Linear Regression and XGBoost for CLV prediction. XGBoost outperformed the baseline model in both scenarios.

The research further integrates Recency, Frequency and Monetary (RFM) analysis with K-Means clustering, connecting traditional customer-behaviour metrics with predicted customer value. The findings demonstrate how these techniques can support targeted customer engagement, retention and revenue optimization.

Analytical Techniques

Customer Lifetime Value (CLV) Prediction RFM Analysis Customer Segmentation K-Means Clustering Linear Regression XGBoost Machine Learning Retail Analytics

Business Application

The project demonstrates how historical transactional data can be transformed into forward-looking intelligence for:

Customer Segmentation Customer Retention Targeted Marketing Revenue Optimization Identification of High-Value Customers Data-Driven Decision-Making
View the Published Paper on SSRN
02 · Published Dataset

NeluxTech Retail Dataset

Real-World Retail & Service Business Data From Kenya

Wilfred The Analyst Platform: Kaggle June 5, 2023 - September 12, 2024 Retail | Sales | Customer Analytics | Business Intelligence

The NeluxTech Retail Dataset captures daily sales activity from a diverse retail and service business operating in Kenya.

The dataset contains transactions across different products and services, including food, stationery and cyber services, while distinguishing between loyalty and non-loyalty customer segments.

Created from internal sales records, the dataset provides a practical foundation for exploring real-world business questions and developing analytical solutions.

Potential Applications

Sales Forecasting Customer Segmentation Revenue Analysis & Optimization Purchasing Behaviour Analysis Business Intelligence & Dashboard Development Machine Learning Trend & Performance Analysis Data Analytics Education

Why I Published It

Real-world analytical skills are developed by working with data that reflects actual business problems.

I published the NeluxTech Retail Dataset to provide analysts, researchers, students and data science practitioners with a practical resource for exploring business questions, developing analytical models and experimenting with data-driven decision-making.

Explore the NeluxTech Retail Dataset on Kaggle
03 · Data Analytics Education & Knowledge Sharing

Wilfred The Analyst · YouTube

Solving Real-World Research Problems Using Stata

Beyond consulting and research, I create practical educational content designed to help researchers, students, institutions and research organizations understand how to solve real-world research problems using data.

My YouTube channel, Wilfred The Analyst, focuses particularly on applying Stata to real research workflows.

The content extends beyond teaching individual software commands.

Tutorials demonstrate the analytical journey from:

Research Question Study Conceptualization Research Design Data Management Data Cleaning Statistical Analysis Interpretation Reporting

Real-World Databases Used

Tutorials and demonstrations draw on established real-world and public datasets, including:

NHANES

National Health and Nutrition Examination Survey

NAMCS

National Ambulatory Medical Care Survey

NHAMCS

National Hospital Ambulatory Medical Care Survey

SEER

Surveillance, Epidemiology, and End Results

MIMIC-IV

Clinical and critical-care data

DHS

Demographic and Health Surveys

along with other public and institutional data sources.

Topics Covered

Stata Programming & Do-Files Data Management & Cleaning Missing Data Descriptive Statistics Hypothesis Testing Regression Analysis Survey Data Analysis Research Methodology Statistical Interpretation Results Reporting Real-World Database Analysis

The objective is to help learners understand not only which commands to use, but also why an analytical method is appropriate, how to interpret the results and how to communicate the findings.

Visit Wilfred The Analyst on YouTube
04 · Real-World Database Analytics

Working With Complex Public & Research Data

An important part of my portfolio involves working with large-scale public, research, health and economic databases.

My analytical workflows include experience with data sources such as:

NHANES NAMCS NHAMCS SEER MIMIC-IV DHS TCGA CDC WONDER Canadian Community Health Survey (CCHS) World Bank Indicators

Projects involving these datasets can require much more than statistical analysis alone.

The workflow may involve:

Data Identification Extraction & Import Data Cleaning Dataset Merging & Integration Variable Construction Missing Data Assessment Exploratory Analysis Statistical Modelling Visualization Interpretation & Reporting

This experience supports projects across public health, healthcare, epidemiology, cancer research, economic analysis, population studies and other data-intensive research areas.

05 · Analytical & Technical Capabilities

Tools Behind the Work

My portfolio spans multiple analytical environments and technologies

Stata R Python SPSS SAS SQL Power BI Tableau Microsoft Excel Google BigQuery

Core Capabilities

Data Cleaning & Management Statistical Analysis Econometrics Machine Learning Predictive Analytics Business Intelligence Data Visualization Research Analytics Survey Analysis Forecasting Dashboard Development Analytical Reporting

More Work Coming

A Growing Portfolio of Applied Data Work

This portfolio will continue to document selected research, datasets, analytical projects, dashboards, statistical applications and educational resources.

Where client confidentiality applies, projects may be presented as anonymized case studies, focusing on the analytical challenge, methodology, tools and outcomes without disclosing confidential information.

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