The Service Experience Intelligence System is a Streamlit-based analytics application for understanding service quality, satisfaction patterns, feedback themes, and improvement priorities.
The core question is:
Which aspects of the service experience are improving, deteriorating, or creating risk?
This project focuses on experience intelligence rather than operational forecasting or generic complaint monitoring.
Service feedback often contains both structured and unstructured signals: ratings, service areas, response times, comments, and satisfaction indicators. These signals can help identify where service quality is strong and where improvement may be needed.
This app helps analyze:
- satisfaction and experience scores,
- high-risk service areas,
- feedback themes,
- experience trends,
- and improvement actions.
The app supports:
- CSV upload for service feedback datasets,
- synthetic demo feedback data,
- time-window filtering,
- service-area filtering.
The dashboard creates an experience score using rating, text sentiment signals, and response-time pressure.
This gives a simple, interpretable service-quality indicator.
The app visualizes the distribution of healthy, moderate, and high-risk service experiences.
Service areas are compared by feedback volume and average experience score.
The app uses TF-IDF to extract important terms from open-text feedback.
This provides a lightweight way to surface recurring experience themes without requiring heavy NLP infrastructure.
The app tracks monthly experience-score changes to help identify deterioration or improvement over time.
The recommendation section converts service experience patterns into practical improvement suggestions.
This project intentionally uses lightweight, interpretable NLP rather than a complex black-box language model.
The goal is to create a practical service-improvement dashboard that is easy to run, easy to explain, and suitable for portfolio demonstration.
- Python
- Streamlit
- Pandas
- Plotly
- Scikit-Learn
- TF-IDF Vectorization
- Service feedback analysis
- Learner support experience review
- Internal service quality monitoring
- Customer experience monitoring
- Support-channel improvement planning
- Satisfaction trend analysis
The app works best with fields similar to:
- feedback date
- service area
- rating
- feedback text
- response time
- channel
- issue category
Synthetic demo data is included so the app can run without external data.
pip install -r requirements.txt
streamlit run app.pyLink: https://service-experience-intelligence-sb.streamlit.app/
Possible improvements include:
- VADER or transformer-based sentiment scoring,
- topic modeling using NMF or LDA,
- service deterioration alerts,
- satisfaction driver modeling,
- response-time impact analysis,
- experience segmentation,
- dashboard export features.
This project fits the Decision Support / Service Experience Analytics area.
It shows how feedback can be converted into clearer service-improvement priorities without requiring a large production system.
The core decision-support question is:
What should be improved first, and why?