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Exploring Customer Satisfaction (CSAT) with Data Analytics: A Deep Dive into Customer Support in E-Commerce
Customer Satisfaction (CSAT) is a crucial metric in the e-commerce industry, particularly when evaluating the efficiency of customer support. With the rapid growth of e-commerce, businesses need to continually enhance their customer service performance to maintain high levels of customer satisfaction.
Understanding factors such as response time, communication channels, and the role of agents and supervisors in determining CSAT can help e-commerce businesses improve their overall customer experience.
In this blog, we will walk through a detailed e-commerce data analysis project where we explore customer service performance data using Python. Our aim is to uncover trends that affect customer satisfaction and highlight key areas where e-commerce platforms can focus on improving their support systems.
E Commerce Data Analysis Project Source Code
Understanding the Dataset
We’ll be working with a dataset that contains detailed records of customer support interactions in an e-commerce environment. Here’s a breakdown of some key columns in the dataset:
- Agent Name: The name of the customer service agent handling the request.
- Supervisor Name: The name of the supervisor overseeing the agent.
- CSAT Score: A customer satisfaction score ranging from 1 to 5.
- Issue Type: The type of issue faced by the customer.
- Response Time: The time taken to resolve the issue.
- Feedback: The textual feedback provided by the customer.
We begin by loading the dataset and inspecting it:
import pandas as pd
# Load dataset
df = pd.read_csv('customer_support_data.csv')
# Check dataset structure
df.head()
# Summary statistics of CSAT scores
df['csat_score'].describe()
# Check for missing data
df.isnull().sum()This analysis forms the backbone of an e-commerce data analysis project that aims to improve customer service efficiency through data-driven insights.
Tracking CSAT Trends Over Time
Tracking trends in CSAT scores over time allows e-commerce businesses to detect any fluctuations in customer satisfaction and respond proactively. We compute the weekly average CSAT scores and visualize them.
import matplotlib.pyplot as plt
# Convert date column to datetime
df['date'] = pd.to_datetime(df['date'])
# Create a week column
df['week'] = df['date'].dt.isocalendar().week
# Calculate weekly average CSAT
weekly_csat = df.groupby('week')['csat_score'].mean()
# Plot weekly CSAT trend
plt.figure(figsize=(10,6))
weekly_csat.plot()
plt.title('Weekly CSAT Trend')
plt.xlabel('Week')
plt.ylabel('Average CSAT Score')
plt.show()Output Plot: Weekly CSAT Trend
Here, we observe how the average customer satisfaction score changes from week to week. Such a trend can help identify periods of lower satisfaction and prompt actions to investigate and resolve any issues.
Agent and Supervisor Performance: Who’s Leading the Pack?
In every e-commerce data analysis project, it’s important to identify the strengths and weaknesses of the team handling customer support. We calculated the average CSAT score for each agent and supervisor to assess their performance.
Top 10 Agents by CSAT Scores:
# Calculate average CSAT score per agent
agent_csat = df.groupby('agent_name')['csat_score'].mean().sort_values(ascending=False)
# Top 10 agents
top_agents = agent_csat.head(10)
# Plotting the top agents
top_agents.plot(kind='bar', figsize=(10,6), title="Top 10 Agents by CSAT Score")
plt.xlabel('Agent Name')
plt.ylabel('Average CSAT Score')
plt.show()Output Plot: Top 10 Agents by CSAT Score
This visualization reveals which customer support agents are delivering the highest satisfaction, helping businesses recognize top performers.
Top 10 Supervisors by CSAT Scores:
# Calculate average CSAT score per supervisor
supervisor_csat = df.groupby('supervisor_name')['csat_score'].mean().sort_values(ascending=False)
# Top 10 supervisors
top_supervisors = supervisor_csat.head(10)
# Plotting the top supervisors
top_supervisors.plot(kind='bar', figsize=(10,6), title="Top 10 Supervisors by CSAT Score")
plt.xlabel('Supervisor Name')
plt.ylabel('Average CSAT Score')
plt.show()Output Plot: Top 10 Supervisors by CSAT Score
By analyzing the performance of supervisors, we can understand how leadership affects the performance of agents under their management.
Channel Performance: Which Communication Channels Drive Satisfaction?
An important aspect of this ecommerce data analysis project is understanding how different communication channels impact CSAT scores. We analyzed customer satisfaction across various channels like live chat, email, and phone.
# Calculate average CSAT per channel
channel_csat = df.groupby('channel_name')['csat_score'].mean().sort_values(ascending=False)
# Plot CSAT scores by communication channel
channel_csat.plot(kind='bar', figsize=(10,6), title="CSAT Scores by Communication Channel")
plt.xlabel('Channel Name')
plt.ylabel('Average CSAT Score')
plt.show()Output Plot: CSAT Scores by Communication Channel
This analysis helps businesses identify which communication channels are more effective in providing satisfying customer experiences and which ones need improvement.
Does Response Time Impact CSAT?
A common question in e-commerce data analysis is whether response times influence customer satisfaction. To explore this, we plotted a scatter plot showing the relationship between response time and CSAT score.
import seaborn as sns
# Scatter plot of response time vs CSAT score
plt.figure(figsize=(10,6))
sns.scatterplot(x=df['response_time_in_hours'], y=df['csat_score'])
plt.title('Response Time vs CSAT Score')
plt.xlabel('Response Time (in hours)')
plt.ylabel('CSAT Score')
plt.show()Output Plot: Response Time vs CSAT Score
This scatter plot allows us to observe any correlation between response times and satisfaction scores, showing whether slower response times negatively impact CSAT.
To take this a step further, we categorized response times into intervals and calculated the average CSAT for each interval.
# Creating bins for response time
bins = [0, 1, 4, 8, 24, 48, 100]
labels = ['0-1h', '1-4h', '4-8h', '8-24h', '24-48h', '48+h']
df['response_time_bin'] = pd.cut(df['response_time_in_hours'], bins=bins, labels=labels)
# Average CSAT for each response time bin
response_time_csat = df.groupby('response_time_bin')['csat_score'].mean()
# Plotting the relationship between response time and CSAT
response_time_csat.plot(kind='bar', figsize=(10,6), title="CSAT Score by Response Time Intervals")
plt.xlabel('Response Time Interval')
plt.ylabel('Average CSAT Score')
plt.show()Output Plot: CSAT Score by Response Time Intervals
Categorizing response times into intervals helps visualize at what point delays start to significantly affect customer satisfaction.
Analyzing Customer Feedback Using Word Clouds
Customer feedback is a goldmine of information in any e-commerce project. We created a Word Cloud to identify the most frequent words in customer feedback, offering insights into what customers appreciate and what frustrates them.
from wordcloud import WordCloud, STOPWORDS
# Concatenating all feedback text
feedback_text = ' '.join(df['feedback'].dropna())
# Creating a Word Cloud
wordcloud = WordCloud(stopwords=STOPWORDS, background_color='white', max_words=200).generate(feedback_text)
# Display the Word Cloud
plt.figure(figsize=(10,6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.title('Customer Feedback Word Cloud')
plt.show()Output Plot: Customer Feedback Word Cloud
This visualization of customer feedback highlights common terms in customer comments, helping businesses identify recurring themes in both positive and negative reviews.
Pareto Analysis: Identifying Common Issues in Negative Feedback
Lastly, we performed a Pareto analysis on negative feedback (CSAT scores ≤ 3) to identify the most common issues. Understanding these problems can help e-commerce platforms prioritize solutions that have the greatest impact.
# Filter negative feedback
negative_feedback = df[df['csat_score'] <= 3]
# Count issue types in negative feedback
issue_count = negative_feedback['issue_type'].value_counts()
# Creating a Pareto chart
plt.figure(figsize=(10,6))
issue_count.plot(kind='bar')
plt.title('Pareto Chart of Issues in Negative Feedback')
plt.xlabel('Issue Type')
plt.ylabel('Frequency')
plt.show()Output Plot: Pareto Chart of Issues in Negative Feedback
By addressing the most frequent issues customers face, e-commerce platforms can make targeted improvements to enhance customer satisfaction.
Conclusion: Actionable Insights for E-Commerce Platforms
Through this e-commerce data analysis project, we uncovered several actionable insights:
- Agent and Supervisor Training: Invest in training for underperforming agents and supervisors.
- Faster Response Times: Reduce response times, particularly for slower channels like email, to improve CSAT.
- Channel Optimization: Encourage customers to use channels like live chat, which drive higher satisfaction.
- Issue Resolution: Focus on resolving recurring issues mentioned in negative feedback to enhance customer satisfaction.
These findings can serve as a solid foundation for businesses looking to improve their customer service. Additionally, for those working on an e-commerce project in Java source code, integrating such
data analysis techniques can significantly improve customer experience management systems.
This project provides a clear example of how e-commerce platforms can leverage data analytics to optimize customer satisfaction, ensuring they stay competitive in today’s market. Whether you are building an e-commerce project in Java or working on a customer service improvement initiative, data-driven insights are the key to success.
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