As businesses increasingly adopt artificial intelligence to enhance customer experiences, building machine learning models has shifted from a niche capability to an operational necessity. Today's enterprises rely on intelligent systems that power everything from contextual chatbots to predictive customer analytics, fundamentally transforming how they deliver service and drive engagement. When trained well, these models reduce churn, accelerate resolution, and generate actionable insights at scale.
Training a CX-focused ML model demands a structured approach that balances accuracy, scalability, and business alignment.
key challenges in machine learning model development
ML model development involves overcoming several challenges to ensure successful deployment and performance. Here are some key challenges faced by ML professionals:
- Data quality: Inaccurate, incomplete, or noisy data can severely affect model performance, making data preprocessing a crucial step.
- Overfitting: When a model learns noise and specific details from the training data, it may perform poorly on unseen data. Balancing complexity is essential to avoid this issue.
- Underfitting: If the model is too simple, it may fail to capture the underlying patterns, leading to poor performance even on the training data.
- Algorithm selection: Choosing the right algorithm for the problem at hand can be challenging. The wrong choice can lead to inefficiencies or inaccurate predictions.
- Slow training: Large datasets and complex models can result in long training times, requiring advanced computational resources or optimization techniques.
- Scalability: As the amount of data grows, models can become less effective or require significant retraining to remain accurate.
- Model monitoring: Once deployed, models must be continuously monitored and retrained with fresh data to ensure their relevance and performance over time.
Forward-thinking enterprises can ensure that these challenges do not turn fatal by following a structured approach to training ML models.
essential steps in building a machine learning model
Model training is a multi-stage process beginning with business clarity and ending with continuous optimisation. Core stages include:
define the problem and objectives
Start with a clear business problem and desired customer outcomes. Whether it's reducing churn, improving support resolution, or prioritising tickets, framing it as a machine learning task (classification, regression, or clustering) ensures your model delivers business value.
gather and prepare data
Data quality is critical. Collect relevant CX data from CRM systems, support logs, and customer feedback. Clean, label, and preprocess it to remove noise and handle missing values. Certain tools can help enhance pipeline automation and data versioning for reproducibility.
select and engineer features
Feature engineering shapes model intelligence. Use domain knowledge to extract key signals, and apply techniques like normalisation or dimensionality reduction. Feature choices often impact performance more than algorithm selection.
choose the right algorithm
Select algorithms based on your objective. Options include decision trees, support vector machines, and ensemble methods. For complex applications like sentiment detection or routing, deep learning may be more effective.
For regulated use cases or stakeholder-facing outputs, consider models that offer interpretability. Tools like SHAP or LIME can help explain decisions and build trust.
training and validation best practices
Training must be rigorous to ensure reliable outcomes. Split datasets into training, validation, and test sets. Use cross-validation to minimise bias, especially with limited data. Tune hyperparameters using grid or random search. Track metrics like precision, recall, and F1-score, selecting those that align with your CX goals.
Establish baseline models early to measure performance improvements. Simple models like logistic regression often provide strong baselines that help evaluate whether complex approaches deliver meaningful gains. Link technical metrics to business impact. For customer service, high recall may matter more than accuracy if missing a support issue has a high cost.
deploying and monitoring models in production
Models that excel in testing need careful production oversight. CX data evolves constantly, requiring active monitoring and regular retraining. MLOps frameworks streamline this via CI/CD pipelines and performance tracking. Establish retraining triggers based on performance thresholds and data pattern shifts.
real-world CX applications
Machine learning drives measurable improvements across industries. These examples highlight how session-based recommendations, voice analytics, and well-trained models deliver real business impact
- The retail sector uses recommendation engines to boost conversions and reduce cart abandonment.
- Financial services deploy fraud detection to protect customers while limiting false alerts.
- Healthcare leverages chatbots for faster triage, preserving human expertise for complex cases.
how can Infosys BPM power smarter ML model development?
Infosys BPM supports enterprises across the complete machine learning lifecycle. From data preparation to feature engineering and machine learning model development, our solutions drive measurable impact and operational readiness.
We use robust MLOps frameworks to streamline development and ensure production reliability. Our AI-driven platforms connect seamlessly with CX systems, delivering real-time insights, automated decisions, smarter customer interactions, and intelligent automation across the ML lifecycle.
