Fingerhut (Online Retailer) Strategic Consulting
About
This project was completed as part of the Data Theory capstone course, Statistics M148 - Experience of Data Science at UCLA.
Background
Fingerhut is the leading provider for middle-income consumers seeking to establish build or rebuild credit through an independent bank partnership, offering qualifying customers revolving credit and installment loans with monthly payment options and the flexibility of paying over time. Qualifying customers use their accounts for e-commerce purchases at fingerhut.com for everything they and their family need from a broad selection of national brands.
Objective & Methodology
The objective of this project was to provide strategic consulting to Fingerhut, to help them understand an ideal customer “journey” and to identify potential areas for improvement in their customer experience. The project was divided into the following phases:
- Data Collection & Cleaning: Cleaning raw granular data tracking customer interactions with the Fingerhut website.
- Exploratory Data Analysis (EDA): Analyzing the cleaned data to identify trends and patterns in customer behavior, and to identify anomalies.
- Feature Engineering & Customer Segmentation: Creating new features and segmenting customers based on their behavior and characteristics.
- Predictive Modeling: Building predictive models to forecast customer behavior and to identify potential areas for improvement.
- Deep Dive Analysis: Conducting a thorough analysis of promotional campaigns to judge their effectiveness.
- Recommendations: Providing strategic recommendations to Fingerhut based on the findings.
Relevant Files
The following slides were presented to the Fingerhut team and the advisory board for the Data Theory program.
The following report provides a detailed overview of the project.