Wayfair Retail Price Reverse Engineering Tool
Built a pricing analysis tool to estimate how supplier base cost affected Wayfair retail pricing, helping determine cost inputs needed to reach target retail prices.
- Python
- pandas
- Excel
- openpyxl
- CSV
- pricing analysis
Overview
I built a pricing analysis tool to better understand how Wayfair retail prices were being calculated from supplier cost inputs and related cost stack variables.
The goal was to reverse engineer the pricing relationship enough to recommend base cost values that would produce retail prices close to a target.
Problem
Wayfair pricing involved several variables, and it was not immediately clear how changes to supplier cost would flow through to the final retail price.
Without a structured model, setting a base cost to reach a desired retail price required trial and error.
Solution
I analyzed Wayfair cost stack data and built a tool that could estimate the relationship between base cost and retail price.
The system included:
- Excel data processing
- SKU-level analysis
- Cost stack review
- Base cost scenario testing
- Target retail price inputs
- Recommended base cost outputs
- Support for comparing different SKUs and assumptions
The tool helped turn a confusing pricing process into a more usable decision model.
Impact
This project made pricing decisions more structured and reduced the need for manual trial and error. It helped estimate what supplier cost should be used to get closer to a desired retail price.
What I Learned
This project reinforced the value of reverse engineering business rules from messy real-world data. It also showed how even a focused internal tool can create meaningful value when it makes a recurring decision easier and more accurate.