Case Studies & Projects

Real results from startups to enterprises. From data chaos to trusted insights. Each project tells a story of transformation.

AI-Powered Customer Reactivation System

Dry Cleaning Network Retail / Marketing
39.6x
Expected ROI
10.9%
Conversion Rate
24,803
Target Customers
90%
Automated

Built a comprehensive ML-driven marketing automation system using segment-specific "Point of No Return" analysis (90th percentile of 192,159 transaction intervals). Discovered VIP customers lost after 163 days, Loyal Core after 415 days, and Newcomers after just 29 days. Combined scientific segmentation with Uplift modeling, GPT-4 personalization, and cross-sell optimization to maximize ROI through precision targeting.

Key Achievements

  • Calculated segment-specific "Point of No Return" from 192,159 intervals (VIP: 163d, Loyal: 415d, Newcomers: 29d)
  • Built precision contact windows that reduced daily campaigns from 4,000 to 1,200 (higher quality, 3.0-3.5x ROI)
  • Implemented T-Learner Uplift model to optimize discount allocation
  • Generated 10,000+ personalized messages using GPT-4 with structured outputs
  • Integrated cross-sell recommendations with 5.2x lift coefficients
  • Prevented budget waste on Loyal Core customers who return naturally (median 110 days)
Python GPT-4 CatBoost pandas Pydantic Plotly
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AI-Powered Player Retention System

Online Casino iGaming / Retention
3-4x
Expected ROI
50-60%
Budget Savings
15-23%
Conversion Rate
5M+
Players Research

Designed an Uplift-based retention system that predicts "who will deposit BECAUSE of the bonus" — not just "who will deposit." Based on the Descending Recovery Curve (Day 1: 27% vs 3 months: 2%), segment-specific Point of No Return (VIP: 14 days, Newcomers: 10 days), and LLM personalization with Game Affinity cross-sell.

Key Achievements

  • Uplift model targets only "persuadables" (30-40% of base), saving 50-60% bonus budget
  • Day 1 reactivation 13.5x more effective than 3-month delay (Optimove, 5M+ players)
  • Segment-specific Point of No Return: VIP 14 days, Newcomers 10 days (vs industry "30 days")
  • Game Affinity cross-sell increases LTV 35-40% (Gates of Olympus → Starlight Princess)
  • VIP personal manager intervention achieves 890% ROI (License Gentlemen)
  • AI churn prediction accuracy 90%+ enables proactive whale retention
Python T-Learner Uplift GPT-4 CatBoost Game Affinity Real-time Scoring
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AI-Powered Member Retention System

Premium Fitness Network Health & Wellness
4.2x
Expected ROI
34%
Churn Reduction
8,450
Members Analyzed
87%
Prediction Accuracy

Built a proactive retention intelligence system for premium European fitness clubs with VIP memberships (150-300/month). Used visit frequency decay analysis to identify "Cancellation Risk Windows" (VIP Black: 21 days, VIP Gold: 35 days). Combined churn prediction with GPT-4 personalized re-engagement and trainer intelligence dashboards.

Key Achievements

  • Identified segment-specific cancellation risk windows through visit pattern analysis
  • Built 87% accurate churn prediction model at 30-day horizon
  • Discovered service diversity = retention: members using 3+ categories have 78% lower churn
  • Created trainer intelligence dashboard with at-risk client alerts
  • Achieved 23% service cross-adoption rate among targeted members
  • Personal training identified as "hook" 4.3x higher LTV for members adding PT
Python XGBoost GPT-4 PostgreSQL Metabase Airflow
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AI-Powered Patient Reactivation System

Premium Dental Network Healthcare / Dental
5.1x
Expected ROI
18.7%
Reactivation Rate
12,340
Patients Analyzed
23%
Upsell Rate

Built treatment-aware patient recall system for premium dental clinics specializing in implants, cosmetic dentistry, and medical tourism. Mapped 23 treatment categories to specific "Optimal Recall Windows" (implants: 5-6 months, whitening: 8-10 months). Combined treatment lifecycle prediction with GPT-4 personalized messaging and staff workflow integration.

Key Achievements

  • Built treatment-specific recall windows for 23 procedure categories
  • Identified "Point of No Return" by segment: Platinum 14 months, Gold 18 months, Silver 24 months
  • 82% accuracy reactivation probability model
  • Doctor attribution messaging achieved 2.3x higher open rates
  • Medical tourism segment handled separately with 67% higher recall rate
  • "Protect your investment" messaging resonated with high-value patients
Python XGBoost GPT-4 PostgreSQL Twilio SendGrid
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AI-Powered Client Reactivation System

Medical Aesthetics Network Medical Aesthetics
4.8x
Expected ROI
21.3%
Reactivation Rate
6,820
Clients Analyzed
28%
Treatment Ladder

Built "Beauty Journey Intelligence" system for European medical aesthetics network specializing in injectables, laser treatments, and body contouring. Used "Treatment Fade Window" analysis (Botox: 3.5-4 months, fillers: 8-10 months) with emotionally intelligent GPT-4 messaging focused on results and confidence, not calendar dates.

Key Achievements

  • Built procedure-specific "fade window" models for 18 treatment categories
  • "Treatment Ladder" discovery: clients adding second procedure have 4.1x higher LTV
  • Emotional language outperformed clinical messaging by 3.2x
  • VIP clients achieved 47% reactivation rate with personal aesthetician calls
  • 28% of reactivated clients added new treatment category (cross-sell)
  • Seasonal campaigns (pre-summer, pre-holiday) captured competitor demand
Python XGBoost GPT-4 PostgreSQL Metabase WhatsApp API
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