ML MODELS

Custom Models That Understand Your Business

Deploy purpose-built machine learning models trained specifically on your data, industry dynamics, and business objectives. We train, deploy, monitor, and connect those models into your marketing systems with stronger control where privacy matters.

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Why Generic ML Falls Short

Off-the-shelf machine learning models cannot capture the nuances that drive success in your specific market context.

One-Size-Fits-All Predictions

Generic models trained on broad datasets miss the unique patterns, seasonality, and customer behaviors specific to your business and industry vertical.

Limited

generic accuracy

Slow Model Updates

Pre-built models become stale quickly as market conditions change. Without continuous retraining on your fresh data, predictions lose relevance and accuracy.

Slow

model updates

Limited Customization

Packaged ML solutions lack the flexibility to incorporate your proprietary data sources, custom features, or business-specific objectives into the prediction framework.

Rigid

customization

Black Box Predictions

Vendor-provided models offer no visibility into decision logic, making it impossible to understand why predictions were made or how to improve model performance.

Opaque

decision logic

Production-Ready ML Model Suite

Conversion Propensity Models

Predict which leads, prospects, or customers are most likely to convert based on behavioral signals, demographic attributes, and engagement patterns. Enables precise targeting and resource allocation.

Customer Lifetime Value Forecasting

Forecast the total revenue potential of each customer relationship using transactional history, product affinity, and retention signals. Prioritize high-value relationships automatically.

Churn Prediction Models

Identify customers at risk of churning weeks or months before traditional indicators appear. Deploy proactive retention campaigns triggered by predictive risk scores.

Attribution Models

Multi-touch attribution that understands complex customer journeys across channels and devices. Algorithmic attribution that updates continuously based on actual conversion patterns.

Sentiment Analysis Models

Natural language processing models trained on your industry terminology to analyze customer feedback, social mentions, and support tickets at scale. Extract actionable insights from unstructured text.

Recommendation Engines

Collaborative filtering and content-based models that recommend next-best products, content, or actions personalized to individual user preferences and behavior history.

ML Model Development Process

From data exploration to production deployment, our implementation process delivers high-performance models in weeks, not months

Discovery•Week 1

Data Assessment

We analyze your available data sources, quality, volume, and feature richness to determine optimal model architectures and establish baseline performance targets.

Key Outcomes

✓Data quality audit and gap identification
✓Feature engineering roadmap
✓Model architecture recommendations

Development•Week 2-3

Model Training

We train custom models using your historical data, testing multiple algorithms and configurations to maximize predictive accuracy.

Key Outcomes

✓Multiple candidate models trained and validated
✓Cross-validation performance analysis
✓Feature importance documentation

Validation•Week 4

Testing & Refinement

Rigorous backtesting against holdout data ensures models generalize well. We validate predictions against business outcomes and refine for production readiness.

Key Outcomes

✓Production-ready model selected
✓Performance benchmarking completed
✓Explainability framework implemented

Deployment•Week 5

Production Launch

Models are deployed to production infrastructure with real-time inference APIs, monitoring dashboards, and automated retraining pipelines to maintain accuracy over time. Custom implementations can connect directly into your CRM, ad platforms, automation tools, or reporting stack.

Key Outcomes

✓Live model serving with <30ms latency
✓Monitoring and alerting active
✓Continuous retraining scheduled

Case Study

B2C Fintech Startup

Financial Technology

A B2C fintech startup needed to acquire customers fast to demonstrate product-market fit. After engaging 5 different digital marketing agencies, all of whom promised a lot but failed to deliver, the management team was skeptical that any agency could produce results.

TMG took a practical, transparent approach, spending hours with the team to explain what would be done, why specific strategies were proposed, and what to expect. Rather than upselling unnecessary services, TMG advised what the team could handle internally to save costs and build core competencies, while focusing their own efforts where they could deliver the most impact.

Prior Failed Agencies

ML Model Comparison

Different model types optimized for specific marketing use cases

Features	Logistic Regression Linear models	Gradient Boosting Ensemble models	Neural Networks Deep learning
Prediction Accuracy	✓ 75-82%	✓ 88-94%	✓ 90-96%
Training Time	✓ Fast	✓ Medium	✓ Slow
Inference Speed	✓ <10ms	✓ <30ms	✓ <50ms
Explainability	✓ High	✓ Medium	✓ Low
Data Requirements	✓ Small datasets OK	✓ Medium datasets	✓ Large datasets required
Best Use Cases	✓ Binary classification, baseline models	✓ Propensity scoring, CLV prediction	✓ Image analysis, NLP, complex patterns

Machine Learning Model FAQs

Common questions about custom ML model development and deployment

How much historical data is needed to train accurate models?

How often do models need to be retrained?

Can we use the models with our existing marketing tools?

What if model predictions are wrong?

Do we own the models you develop?

How do custom models compare to platform-native AI features?

Ready to Build Custom ML Models?

Discover how purpose-built machine learning can deliver prediction accuracy and business insights that generic solutions cannot match. Schedule a consultation to discuss your specific use cases and data landscape.

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