1. Problem — Mass Marketing is Bleeding Money
The Cellar & Butcher, a premium steak & wine dining brand,
was running blanket campaigns across its entire 2,240-customer base.
Random targeting of a premium product to a general audience meant
campaigns cost more than they returned — every time.
Baseline — all 5 campaigns at negative ROI
Campaign 1
−76.5% ($−5,132)
Campaign 2
−95.1% ($−6,375)
Campaign 3
−73.3% ($−4,912)
Campaign 4
−72.6% ($−4,868)
Campaign 5
−73.6% ($−4,934)
−78%
Average ROI (Baseline)
2,240
Customers Targeted (Mass)
~15%
Response Rate (Mass)
2. Solution — Predictive Scoring + Profit Simulation
Instead of deciding who to target by intuition, I built a machine
learning pipeline to score every customer by their probability of
responding, then ran a profit simulation to find the mathematically
optimal targeting cutoff — the "sweet spot" between response rate
and campaign economics.
Modeling pipeline
01
🔧
Feature Engineering
MntWines, MntMeatProducts, Income, Age, Recency
02
🌲
Random Forest Classifier
Non-linear pattern recognition · 5-Fold Cross-Validation
03
📊
Probability Scoring
Each customer assigned a response probability (0–1)
04
💰
Profit Simulation
ROI & net profit modeled for every top-n% cutoff
05
🎯
Sweet Spot Selection
Top 19% maximizes total profit at 45% response rate
3. What Predicts Response?
The Random Forest model revealed that Recency (days
since last visit) is the single strongest predictor, followed by
wine and meat spending — confirming that behavioral signals dominate
over demographics for this brand.
Top feature importances (Random Forest)
1
Recency
Engagement
0.107
2
MntWines
Spending
0.102
3
MntMeatProducts
Spending
0.094
4
Income
Demographics
0.088
5
MntGoldProds
Spending
0.062
4. Finding the Sweet Spot
The profit simulation shows that targeting the
top 19%
of customers by model score maximizes total net profit — beyond that
point, adding lower-probability customers dilutes ROI faster than it
adds revenue. The break-even targeting ratio is 27.3%.
5. Who Are the Top 19%?
The model's target segment spends dramatically more on wine and
meat, and earns significantly higher income — validating that this
is genuinely a distinct, high-value customer group, not a
statistical artifact.
Customer profile — Target (Top 19%) vs. Others
Wine Spend
$583
2.4× higher than others
Meat Spend
$342
2.7× higher than others
Income
$64k
30% above average
Response Rate
45%
vs. ~15% mass baseline
Response Rate
~15%
Mass campaign baseline
6. Campaign Proposals for Top 19%
🥩
Meat-centric High Spenders
The Butcher's Table Invitation
Private Chef's Tasting exclusively for top meat spenders —
experiential upsell to increase LTV beyond transactional visits.
🍷
Wine Purchase History
Sommelier's Curated Selection
Personalized pairing offers built from individual wine purchase
patterns — not generic promotions, but historically-informed
recommendations.
⏱️
Top 19% · 60+ Day Inactive
Strategic Churn Prevention
Re-engagement offers targeted only at high-probability customers
who haven't visited in 60+ days — highest Recency signal with
proven spend history.
7. Results
-
Targeting ratio reduced to 19% — from 2,240
customers to ~424, saving 81% of campaign budget
-
Expected response rate: 45% vs. ~15% baseline —
3× improvement
-
ROI turns positive at top-19% cutoff — a direct
reversal of the −78% mass baseline
-
Next step: Integrate real-time POS data for
instant personalization upon guest arrival via CRM (Salesforce)