Beyond Surveys: Real-Time User Satisfaction Prediction

on YouTube ↗13m

" summary

A system called DNPS predicts session-level Net Promoter Score from telemetry, behavior and text using a hybrid stack of gradient-boosted trees, sequence models and BERT embeddings, fused with an explainability layer (SHAP/LIME). It claims 85.9% accuracy with MAE ≈ 1.21 and live inference at ~80 ms latency. Ablations show removing text drops accuracy to ≈55% and removing behavioral/telemetry drops it to ≈78%, and dashboards surface root causes like high back‑clicks and short sessions.

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" chapters (15)

1. 0:00Intro: Beyond Surveys—Real‑Time NPS Prediction with LLMs + Telemetry
2. 0:39NPS 101: What Net Promoter Score Measures
3. 1:16How NPS Is Calculated (Detractors, Passives, Promoters)
4. 2:10How Companies Collect NPS Today (and Why It’s Slow)
5. 2:49The 4 Big Problems with Traditional NPS Surveys
6. 3:52The Big Idea: Predict Sentiment Without Surveys
7. 4:14Introducing DNPS: Dynamic Net Promoter Score
8. 4:45DNPS Architecture Overview: Ingestion → AI Engine → Insights
9. 4:56Data Collection: Behavior, Telemetry, and Text Feedback
10. 6:06Data Processing Pipeline: Cleaning, Alignment, Feature Extraction
11. 6:57Hybrid Modeling: Structured + Sequence + Text Models
12. 8:34Fusion Layer + Explainable AI (SHAP/LIME)
13. 10:01Results: Accuracy, Ablation Study, and Real‑Time Latency
14. 10:59Key Drivers & Business Value from Explainability Insights
15. 11:45Limitations, Future Work, and Closing

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