Analyze Toyota Customer Feedback at Scale

Business Context

Toyota Motor receives large volumes of unstructured text from Toyota owners through dealer service notes, Toyota app feedback, call-center transcripts, and post-service surveys. You are asked to show how NLP can be applied in a data science role by building a production-ready pipeline that turns this text into actionable signals for quality, service, and product teams.

Data

You have 2.4 million historical text records collected over 18 months across ToyotaCare, dealer service centers, and connected vehicle support channels.

• Text sources: service advisor notes, customer complaints, survey comments, chat/email transcripts
• Text length: 10-1,200 tokens (median 96)
• Language: English 88%, Spanish 9%, Japanese-translated summaries 3%
• Labels available:
  • Issue category (18 classes; highly imbalanced)
  • Sentiment (positive / neutral / negative)
  • Annotated vehicle entities for a 120K-record subset (model, trim, component, DTC code, symptom)
• Class imbalance: top 3 issue categories account for ~61% of records; safety-related complaints are <4%

Success Criteria

A good solution should achieve macro-F1 >= 0.82 for issue-category classification, recall >= 0.92 on safety-related categories, and entity-level F1 >= 0.88 for extracting vehicle/component mentions. Batch scoring should process daily volume within 2 hours, and near-real-time inference for new feedback should stay under 150 ms per record.

Constraints

• Must run in Toyota Motor's secure cloud environment
• No external API calls with raw customer text
• Need explainable outputs for quality and dealer operations teams
• Support weekly retraining with newly labeled data

Requirements

1. Design an NLP pipeline for issue classification and vehicle/entity extraction.
2. Describe preprocessing for noisy dealer notes, abbreviations, and multilingual text.
3. Implement a modern Python solution using transformers and spaCy.
4. Explain how you would handle imbalance, thresholding, and model monitoring.
5. Define offline evaluation, error analysis, and deployment considerations for Toyota Motor.