VSP Vision — Frame Assortment Intelligence

Demand forecasting + product management analysis for Visionworks eyewear retail
XGBoost · Python · 162 SKUs · 3 Brands · 2 Regions · Sep 2023–Aug 2024

View the full HTML analysis report here:

👉 Open the Full VSP Frame Assortment Report

The report includes:

• Full exploratory analysis
• Model methodology
• Feature importance visualizations
• Forecast outputs
• All 14 prioritized action items
• Interactive charts and tables

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Overview

An end-to-end demand forecasting pipeline built for Visionworks frame assortment planning. Starting from raw monthly sales data, this project enriches product attributes, engineers 33 temporal features, trains and compares three ML models, and translates model output into 14 concrete, data-backed product management recommendations.

The goal: replace intuition-driven assortment decisions with a repeatable, leakage-free framework that predicts SKU-level monthly demand and surfaces where the assortment has structural gaps.

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Results

Metric	Value
Model	XGBoost (Tuned)
R² — Log Scale (primary)	0.737
R² — Raw Units	0.542
Cross-Validated R²	0.738
MAE	26.4 units / month
Features	33
Training period	Sep 2023 – May 2024
Test period	Jun – Aug 2024
Total units analyzed	910,853

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Project Structure

vsp-frame-assortment/
│
├── data/
│   ├── detailed_attributes.csv        # Style-level product attributes
│   └── final_demand.csv               # SKU × Region × 12 monthly sales columns
│
├── docs/
│   ├── Final Report VSP.pdf           # Final written report
│   ├── VSP Presentation.pdf           # Slide deck presentation
│   └── methodology.md                 # Methodology write-up
│
├── notebooks/
│   ├── Demand_and_Cluster_Prediction.ipynb   # Demand forecasting + clustering
│   ├── Descriptive-analytics.ipynb           # Exploratory data analysis
│   ├── Enriched_demand.ipynb                 # Data enrichment + feature prep
│   └── VSP_XGboost_finetuned.ipynb           # Final tuned XGBoost model
│
├── outputs/
│   ├── VSP_Report_Updated.html        # Updated interactive HTML report
│   ├── vsp_report.html                # Original HTML report
│   └── action_items.docx              # Prioritized PM action plan
│
├── index.html                         # Portfolio landing page
├── requirements.txt
├── .gitignore
└── README.md

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The Business Problems

Problem 1 — The $150–$199 tier is over-assorted.
64 SKUs (39% of the assortment) concentrated in a single price band generating only $4,017/SKU annually — 37% below the $200–$249 sweet spot at $6,326/SKU.

Problem 2 — Female patients are structurally underserved.
Female-labeled SKUs account for just 15.8% of sales despite having identical per-SKU productivity to male SKUs ($4,783 vs $4,817). The gap is supply, not demand.

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Methodology

Data Preparation

• Two source files merged on StyleCode: demand file (1,240 SKU–region rows × 12 monthly columns) + attributes file (220 styles)
• Cleaned 3,055 null values: size typos (3.0 → 53mm), casing mismatches, missing attributes
• Reshaped to long format: 14,880 rows (one per SKU–Region–Month)
• Log-transformed the sales target log(1 + Sales) — raw skewness = 2.56
• Time-based split: train Sep 2023–May 2024 / test Jun–Aug 2024

Feature Engineering — 33 Features, All Leakage-Free

Category	Features
Lag	Sales 1, 2, 3, 6 months prior
Rolling averages	3-month and 6-month rolling mean
Expanding statistics	SKU mean, max, cumulative (since launch)
Trend	MoM growth (clipped ±200%), volatility, SKU vs brand peers
Time	Month, quarter, year, Jan–Mar peak-season flag
Price	Log price, price tier, margin ratio, price vs brand average
Attributes	Brand, Region, Gender, Shape, Material, SunOptical (label encoded)

Key decision: StyleCode was dropped. Keeping it inflated R² from 0.52 → 0.69 by memorizing SKU identities rather than learning demand patterns. A model that knows which SKU it's predicting cannot generalize to new styles.

Model Comparison

Model	R² (Log)	R² (Raw)	MAE
Linear Regression	0.310	−18.0	55.6
Random Forest	0.520	0.514	26.9
XGBoost (baseline)	0.580	0.523	26.9
XGBoost (tuned)	0.737	0.542	26.4

Tuned via RandomizedSearchCV across 40 combinations, 3-fold CV.
Best params: n_estimators=800, max_depth=4, learning_rate=0.01, subsample=0.7, reg_lambda=3

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Key Findings

What drives demand: Sales trajectory beats all static product attributes. Top 5 features by importance: Rolling 6-Month Avg Sales → SKU Max Sales → SKU Avg Sales → Brand → Optical/Sun. Shape, material, and size have weak individual signal but strong interaction effects.

Assortment gaps:

• Oval frames — 1 SKU, 9,432 units, highest avg/SKU of any shape. Proven demand, no supply.
• Titanium — 5 SKUs at $9,852/SKU (2× Acetate). NIKE 8045 and 8154 are both top-10 SKUs.
• Cat Eye — 2 SKUs total. Nationally growing trend. Visionworks has no real presence.
• CK Sun — 80% dead or slow SKUs, $1,197/SKU. Worst performing brand in portfolio.
• Lacoste in AMER — 37.6% of EMEA but only 14.6% of AMER. Their $200–$249 tier = $10,005/SKU.

Seasonality: Sales jump 73–80% from December to March (insurance plan resets). New styles must be floor-ready by December 15 or miss ~40% of their first-season window.

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Top 10 SKUs

Rank	Style	Brand	Region	Shape	Material	Price	Units
1	L2707	Lacoste Optical	EMEA	Square	Acetate	$199.95	20,417
2	L2876	Lacoste Optical	EMEA	Square	Acetate	$220.00	14,317
3	NIKE 7125	Nike Optical	AMER	Rectangle	Bio Inj-G820	$249.00	14,219
4	NIKE 7246	Nike Optical	AMER	Rectangle	Acetate	$259.00	14,096
5	L2707N	Lacoste Optical	EMEA	Square	Acetate	$199.95	13,443
6	NIKE 8045	Nike Optical	AMER	Rectangle	Titanium	$295.00	13,048
7	L2271	Lacoste Optical	EMEA	Rectangle	Metal	$225.00	12,086
8	NIKE 8154	Nike Optical	AMER	Rectangle	Titanium	$295.00	11,767
9	NIKE 7090	Nike Optical	AMER	Square	Bio Inj-G820	$285.00	11,583
10	NIKE 5538	Nike Optical	AMER	Square	Bio Inj-G820	$135.00	11,129

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Priority Action Items

#	Priority	Action	Justification
1	🔴 HIGH	Cut 10–15 SKUs from $150–$199 tier	$4,017/SKU vs $6,326/SKU — 37% gap
2	🔴 HIGH	Add 4–6 Lacoste styles to AMER ($200–$249)	$10,005/SKU — best brand×tier combo
3	🔴 HIGH	Floor Hero SKUs by Dec 15	73–80% sales jump Jan–Mar
4	🔴 HIGH	Expand Titanium to 8–10 SKUs	$9,852/SKU, nearly 2× Acetate
5	🟡 MED	Cut CK Sun from 15 → 5–6 SKUs	$1,197/SKU, 80% dead
6	🟡 MED	Reduce Lacoste Suns; EMEA-only	17/18 SKUs dead, 84% EMEA
7	🟡 MED	Add female-specific Nike + CK styles	Equal productivity, 30 vs 69 SKUs
8	🟡 MED	Build dedicated EMEA buy list	Current top-10 is 9/10 Nike AMER
9	🟡 MED	Separate AMER/EMEA size grids	AMER: 54mm peak, EMEA: 53mm peak
10	🟡 MED	Rationalize CK Optical to 20–25 SKUs	$3,809/SKU — lowest optical brand
11	🔵 LOW	Test 2–3 Oval optical styles	9,432 avg/SKU — highest of any shape
12	🔵 LOW	Test 2–3 Cat Eye styles	2 SKUs only, growing national trend
13	🔵 LOW	Extend model to 24+ months of history	+0.04–0.08 R² estimated gain
14	🔵 LOW	Implement Dec 15 launch calendar	Late arrivals miss 40% of seasonal peak

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Setup

git clone https://github.com/yourusername/vsp-frame-assortment.git
cd vsp-frame-assortment
pip install -r requirements.txt

Open notebooks in order:

notebooks/01_data_cleaning.ipynb
notebooks/02_eda.ipynb
notebooks/03_xgboost_model.ipynb

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Tech Stack

Python 3.11 pandas numpy XGBoost 2.0 scikit-learn matplotlib Jupyter

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Graduate-level data analytics project — VSP Vision / Visionworks frame assortment analysis.