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Predicting Customer Satisfaction Using MLOps Pipelines: Automating Insights for E-Commerce

Client

A leading e-commerce platform aiming to proactively manage customer satisfaction and prevent churn. (Company name withheld for confidentiality.)

Problem

The client faced a critical gap: they lacked visibility into customer satisfaction until after negative reviews appeared. This reactive approach resulted in:

  • Late interventions for service issues,
  • Risk of customer churn, and
  • Difficulty in scaling satisfaction monitoring.
They needed a predictive, automated, and scalable solution.

Result

We created a prototype dashboard in Tableau using test data that aligned with the client’s new branding and design system. The result was a clean, modern, and intuitive dashboard interface with significantly improved readability and UX.
 
Technologies
  • Python (3.8+) – Core programming language
  • ZenML – Orchestration & pipeline management
  • MLflow – Experiment tracking, model registry, deployment
  • Scikit-learn, Pandas, NumPy – Data processing & model building

Goal

  • Predict customer satisfaction scores (1–5 stars) before reviews are submitted.
  • Automate the entire machine learning lifecycle—from data ingestion to deployment.
  • Ensure reproducibility, version control, and continuous retraining as new data arrives.

Results

The automated MLOps system delivered:

  1. Continuous Prediction – A pipeline forecasting satisfaction for 100K+ orders with no manual intervention.
  2. End-to-End Automation – Data ingestion, preprocessing, training, evaluation, and deployment all orchestrated seamlessly.
  3. Self-Improving Models – Automatic retraining and redeployment triggered whenever a new model outperformed the previous one.

Scalability – Framework ready to extend into churn prediction, NPS forecasting, and beyond.

Phase 2: Visual Concept Design

In this stage, we focused entirely on creating a Tableau dashboard prototype based on mock/test data. Key activities included:

  • Designing key visual elements—charts, KPI cards, filters, and layout blocks
  • Applying the brand’s updated color palette, typography, and visual patterns
  • Iteratively refining the prototype based on client feedback

The final deliverable was a visually modern and UX-optimized Tableau dashboard aligned with the new brand identity. It featured:

  • Key metrics at a glance
  • Clear visual hierarchy and intuitive interactivity
  • Layout designed for readability and quick insights

The “after” we provided:

Conclusion

This project demonstrates how ZenML and MLflow can be fused into a production-ready, fully automated MLOps ecosystem. By predicting customer satisfaction in advance, the client gained a competitive edge: faster interventions, higher retention, and a scalable system to support long-term growth.

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