Gulf Coast Boat Sales
Pricing Intelligence & Inventory Optimization for a Family-Owned Marine Dealership
23%
Faster Inventory Turnover
+23%
$2,800
Avg Pricing Uplift
per unit
91%
Forecast Accuracy
monthly
Illustrative scenario — not a client engagement.
Coastal Marine Sales is a composite, not a client. The pricing dynamics, the seasonality, and the manufacturer and condition premiums are drawn from how the Texas Gulf Coast boat market actually behaves. The firm profile, every figure, and every chart on this page are modeled to show how the approach would work. Nothing here reports a measured client outcome.
The pricing model itself is real and you can use it: our boat valuation tool runs XGBoost with SHAP explanations — trained, as it says there, on synthetic sales records.
01The Challenge
A family-owned marine dealership on the South Texas Gulf Coast was pricing boats the way they always had — gut feel, competitor scanning, and a "what the market will bear" approach. The owner knew his inventory was sitting too long and margins were thinning, but couldn't pinpoint why.
Three specific problems drove the engagement:
- Pricing blindness: No systematic way to set asking prices based on actual market drivers — length, engine, condition, and model year were weighted by intuition, not data.
- Seasonal inventory pileup: Boats purchased for spring selling season sat unsold through summer, tying up capital and requiring costly storage.
- No markdown timing: Price reductions were reactive (when the owner noticed a boat "had been there a while"), not proactive based on aging curves.
Data Landscape
02Our Approach
We approached this as four interconnected modeling problems: pricing optimization, demand forecasting, inventory aging analysis, and customer segmentation. Each model feeds into a unified dashboard the dealership team uses daily.
- XGBoost + SHAP — gradient-boosted pricing model with interpretable feature contributions
- Prophet — Facebook's time series library for monthly demand forecasting with seasonal decomposition
- Survival Analysis (Lifelines) — Kaplan-Meier curves modeling how long each boat type sits before sale
- K-Means Clustering — customer segmentation by purchase behavior, geography, and financing preference
- Pandas + Recharts — data pipeline and interactive dashboard for the dealership team
Raw Sales Data
3,000+ transactions
Feature Engineering
20+ derived features
XGBoost Pricing
R² = 0.83
Prophet Forecast
91% monthly accuracy
Dashboard
Real-time pricing tool
03Key Findings
Seasonal Sales Heatmap
Monthly sales volume by boat type across all years. Peak demand is Mar–Jun for center consoles and bay boats, with a secondary Oct–Nov bump from tournament season.
What Drives Pricing? (SHAP Feature Importance)
Mean absolute SHAP values from the XGBoost pricing model. Condition and model year dominate — the owner had been underweighting condition in his mental model.
Inventory Survival Curves
Kaplan-Meier curves showing the probability a boat is still in inventory after N days. Pontoons and deck boats sell fastest; center consoles have the longest tail.
Interactive Pricing Calculator
Predicted Sale Price
$17,100
Linear model R² = 0.825 · For illustration only
Adjust boat features to see the model's predicted sale price. Based on a linear regression trained on 3,000 historical transactions.
04Business Impact
Projected Annual Value
$67,000 projected annual margin improvement
The dealership implemented data-driven pricing within the first month. The owner reported that the SHAP-based feature breakdown was the "aha moment" — seeing that condition grade had 2x the pricing impact of engine horsepower changed how he evaluated trade-ins and set asking prices.
The survival curves gave the team a concrete markdown policy: if a center console is still on the lot after 75 days, it's past the median sell-through point and should receive a 5% price reduction. Previously, that same boat might have sat 120+ days before anyone noticed.
05Technical Details
Pricing Model (XGBoost)
- Features: length_ft, engine_hp, model_year, days_on_lot, boat_type, condition, trade_in
- Hyperparameters: n_estimators=100, max_depth=4, learning_rate=0.1
- Evaluation: R² = 0.83, MAE = $1,450 (5-fold CV)
Demand Forecast (Prophet)
- Granularity: monthly, per boat type
- Seasonality: yearly (multiplicative) + custom regressor for local events
- Accuracy: MAPE = 9% on 12-month holdout
Survival Analysis
- Library: lifelines KaplanMeierFitter
- Stratified by: boat_type, condition, price_quartile
- Used for: automated markdown trigger points
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