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Automobile Customer Analytics

Advanced Customer Behavior & Sentiment Analysis

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Overall Sentiment Score (Positive)
Customer Satisfaction
Net Promoter Score
Issue Resolution Rate
Customer Sentiment Trend
Sentiment Distribution
💡 Insights
Positive Trend in Electric Vehicle Satisfaction

Customer satisfaction for electric vehicles has increased by 12% over the past quarter, primarily driven by improved battery performance feedback and charging infrastructure satisfaction.

Potential Issue with Model Y Infotainment System

Analysis shows a 23% increase in negative sentiment related to the infotainment system in Model Y vehicles. Consider prioritizing software updates for this model.

Loyalty Program Driving Positive Sentiment

Customers enrolled in the loyalty program show 18% higher satisfaction scores and are 32% more likely to recommend your brand to others.

Sentiment by Vehicle Category (Top 5)
Sentiment by Region
Common Topics in Customer Feedback
Total Topics Analyzed: | Total Mentions:
Customer Journey Funnel
Purchase Funnel Conversion
Customer Lifetime Value by Segment
Service Visit Frequency
Churn Risk by Customer Segment
Predictive Maintenance Alerts
Satisfaction Forecast Model: Model Name
Sales Growth Forecast Model: Model Name
Definitions — Methodologies & KPI Examples

Purpose: A concise reference of the analytical methodologies and key performance indicator (KPI) definitions used by the Customer Behavior & Sentiment Analysis dashboard.

Methodologies

  • Data collection: Integrate and harmonize data from surveys, reviews, social media, customer support, CRM, sales/DMS, telematics, and service records. Use IDs (customer ID, VIN) to join tables and produce unified records.
  • Sentiment analysis: Apply NLP classifiers to label feedback as positive, neutral, or negative. Aggregate labels to compute sentiment percentages and trends.
  • Topic extraction: Use text classification and topic modeling to surface common themes and tag feedback by topic and sentiment.
  • Customer journey & funnel analysis: Classify customers into journey stages (Awareness, Consideration, Purchase, Post-purchase, Loyalty) using behavioral rules and event logs. Compute counts and percentages per stage and visualize changes over time.
  • Predictive modeling: Train classification/regression models (e.g., logistic regression, random forest, XGBoost, time-series models like ARIMA/Prophet) for churn prediction, maintenance alerts, satisfaction forecasts, and sales growth forecasting.
  • AI-generated insights: Use statistical tests and model outputs to highlight significant changes, anomalies, or recommended actions.
  • Filtering & interactivity: Ensure all metrics and visualizations recalculate for selected filters (vehicle type, region, data source, customer segment).

Key KPI definitions & example calculations

Overall Sentiment Score

Short definition: Share of feedback that is positive.

Formula:

Positive sentiment (%) = (number_positive_items / total_feedback_items) * 100

Notes

  • These definitions are intentionally compact — they focus on method and calculation so they can be referenced directly by analysts and engineers implementing the dashboard.
  • If you want formulas expanded or one-page examples for a specific KPI (e.g., sample SQL or pseudocode), I can add them as low-risk supplements.