Exploratory Data Analysis (EDA) is a powerful technique for discovering patterns, relationships, and anomalies in data. When applied to customer service and customer satisfaction data, EDA can reveal actionable insights that help businesses improve customer experience and loyalty. Here’s how to effectively use EDA to understand the relationship between customer service and customer satisfaction.
Understanding the Context
Before diving into EDA, it’s essential to understand the variables involved:
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Customer Service Metrics: Response time, resolution time, support channel (email, chat, phone), number of interactions, agent performance, etc.
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Customer Satisfaction Metrics: CSAT (Customer Satisfaction Score), NPS (Net Promoter Score), CES (Customer Effort Score), reviews, complaints, etc.
The goal is to explore how aspects of customer service influence customer satisfaction scores.
Step 1: Data Collection and Preparation
1.1 Collecting Relevant Data
Gather historical data from CRM platforms, customer surveys, support ticketing systems, and feedback tools. Ensure the dataset includes:
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Unique customer identifiers
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Interaction timestamps
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Service channel used
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Response and resolution times
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Customer feedback or ratings
1.2 Cleaning the Data
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Handle missing values (e.g., remove, impute, or flag)
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Standardize categorical variables (e.g., “Phone Support” vs. “phone”)
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Convert time variables into duration (e.g., resolution time in minutes)
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Normalize or scale numerical features if needed
Step 2: Univariate Analysis
Start by analyzing individual variables to understand their distributions and characteristics.
2.1 Customer Service Variables
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Plot histograms of response times, resolution times, and number of interactions.
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Use boxplots to detect outliers in service duration.
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Count plots for service channels to see frequency of use.
2.2 Customer Satisfaction Variables
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Distribution of CSAT, NPS, or star ratings.
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Identify mean and median satisfaction scores.
Univariate analysis helps you detect data skewness, common ranges, and data quality issues.
Step 3: Bivariate Analysis
Bivariate analysis focuses on finding relationships between two variables—here, customer service features and satisfaction scores.
3.1 Correlation Analysis
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Use a correlation matrix (heatmap) to quantify relationships between numerical features like resolution time and CSAT.
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Identify strong positive or negative correlations.
3.2 Scatter Plots
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Plot resolution time vs. CSAT to visualize trends.
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Add regression lines to assess linearity of relationships.
3.3 Boxplots and Violin Plots
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Use boxplots to show how satisfaction scores vary by service channel.
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Compare satisfaction for different support agents or departments.
Step 4: Multivariate Analysis
Explore how multiple service features together impact satisfaction.
4.1 Pair Plot or Scatter Matrix
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Use pair plots to visualize interactions among multiple variables.
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Helpful to detect clusters or patterns.
4.2 Grouped Analysis
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Group by customer service agent and calculate average satisfaction.
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Analyze differences in satisfaction based on combined variables like “response time + channel.”
4.3 Interaction Effects
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Use pivot tables to see how different service channels and response times together influence satisfaction.
Step 5: Segmentation and Clustering
Segment customers or interactions based on similarities in service and satisfaction metrics.
5.1 K-Means Clustering
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Cluster customers based on response time, number of interactions, and satisfaction.
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Identify profiles like “High-effort, Low-satisfaction” or “Fast-resolution, High-satisfaction.”
5.2 Customer Segments Analysis
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Perform EDA within each segment to understand pain points or success factors.
Step 6: Time Series and Trend Analysis
Analyze how satisfaction trends evolve over time in relation to service improvements.
6.1 Time-based Plots
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Plot average satisfaction score by week/month.
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Overlay significant events (e.g., new chatbot launch) to evaluate impact.
6.2 Seasonality Detection
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Examine if satisfaction dips during holiday seasons or promotional periods.
Step 7: Text Analysis for Qualitative Feedback
If you have open-text survey responses or reviews, perform text analytics to extract themes.
7.1 Word Clouds and Frequency Analysis
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Identify common themes in complaints or praise.
7.2 Sentiment Analysis
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Use NLP models to assign sentiment scores to customer comments.
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Compare sentiment trends with structured satisfaction scores.
Step 8: Hypothesis Testing
Validate patterns discovered through EDA using statistical testing.
8.1 ANOVA or t-tests
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Test if the average CSAT differs significantly between service channels.
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Evaluate if longer resolution times significantly reduce satisfaction.
8.2 Chi-square Tests
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Assess if categorical variables like channel and satisfaction level are independent.
Step 9: Visualization Best Practices
Clear visualization is critical for EDA insights to be actionable.
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Use consistent color coding for satisfaction levels (e.g., red = low, green = high)
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Annotate charts to highlight key takeaways
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Choose appropriate chart types: bar charts for counts, line charts for trends, heatmaps for correlations
Step 10: Drawing Conclusions and Recommendations
Based on the findings:
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Identify which service metrics most impact customer satisfaction.
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Recommend process improvements (e.g., reduce average resolution time).
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Suggest training for agents or investment in preferred channels.
Example Insights
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High resolution times correlate with low CSAT: Prioritize faster response workflows.
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Phone support has higher satisfaction than email: Invest more in live support channels.
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Customers needing multiple interactions report lower satisfaction: Aim for first-contact resolution.
Conclusion
EDA offers a comprehensive approach to uncovering the underlying dynamics between customer service and customer satisfaction. By systematically analyzing individual and combined variables, visualizing patterns, and validating findings, businesses can transform raw customer data into a roadmap for improved service strategies and higher customer loyalty.