Customer Analytics
Modern businesses survive and grow by understanding their customers.
Customer analytics uses data to study customer behavior, preferences, and patterns to improve decision-making.
SPSS plays a key role in turning customer data into actionable insights.
What Is Customer Analytics?
Customer analytics focuses on:
- Understanding customer behavior
- Identifying valuable customers
- Reducing customer churn
- Improving customer satisfaction
It combines statistics, predictive modeling, and business knowledge.
Common Customer Data
Businesses collect various types of customer data:
- Demographic data
- Purchase history
- Interaction records
- Survey responses
SPSS helps analyze both structured and survey-based data.
Key Customer Analytics Metrics
Important customer metrics include:
- Customer Lifetime Value (CLV)
- Churn Rate
- Retention Rate
- Customer Satisfaction Scores
These metrics guide strategic business decisions.
Customer Segmentation
Customer segmentation divides customers into meaningful groups based on shared characteristics.
Segmentation helps businesses:
- Target marketing efforts
- Personalize offers
- Optimize resource allocation
SPSS techniques used:
- Cluster analysis
- Descriptive statistics
Churn Analysis Example
A company wants to identify customers likely to leave.
Variables include:
- Usage frequency
- Customer complaints
- Subscription length
Using logistic regression, SPSS predicts churn probability for each customer.
Predictive Models in Customer Analytics
Common predictive models include:
- Logistic regression for churn
- Decision trees for segmentation
- Regression for spending prediction
These models help prioritize high-impact customer actions.
Using SPSS for Customer Analytics
Typical SPSS workflow:
- Import customer data
- Clean and prepare variables
- Explore descriptive statistics
- Build predictive models
- Interpret and act on results
SPSS outputs support data-driven marketing strategies.
Real-World Business Impact
Customer analytics enables businesses to:
- Reduce marketing costs
- Increase customer loyalty
- Improve product offerings
Small improvements in retention often lead to large revenue gains.
Common Mistakes
Common errors in customer analytics:
- Using poor-quality data
- Ignoring business context
- Overfitting predictive models
Analytics must align with business goals.
Quiz 1
What is the goal of customer analytics?
To understand and improve customer behavior and decisions.
Quiz 2
Which metric estimates customer value over time?
Customer Lifetime Value (CLV).
Quiz 3
Which model is commonly used for churn prediction?
Logistic regression.
Quiz 4
Why is segmentation important?
It helps target customers more effectively.
Quiz 5
Does customer analytics require business context?
Yes.
Mini Practice
Imagine a business with customer data.
Identify:
- One metric to measure loyalty
- One variable that might predict churn
Explain how you would analyze them using SPSS.
Focus on linking data analysis to business decisions.
What’s Next
In the final lesson, you will prepare for SPSS exams and interviews, and celebrate completing the course.