Purpose
In this assignment, you will explore cluster analysis as a tool for identifying customer segments in a real-world business scenario. Businesses use clustering techniques to group customers based on shared characteristics, helping refine marketing strategies, create targeted product recommendations, and enhance customer engagement.
This assignment emphasizes the practical application of cluster analysis by having you interpret a pre-clustered dataset to extract meaningful insights, identify patterns, and develop strategic recommendations. Your goal is to analyze patterns, assess engagement, and develop strategic recommendations based on your findings.
Task
A Portuguese bank recently ran a direct marketing campaign to promote term deposit accounts, but engagement was lower than expected. As a data analyst at a marketing agency specializing in financial services, you have been hired to analyze customer clusters using a dataset provided by the bank.
Your analysis should focus on identifying which customer segments were most likely to respond positively and developing data-driven recommendations to improve future campaign performance. Your report will be presented to the banks marketing and strategy team, data analysts, and key executives and represents key deliverables you may encounter in the workplace as a business professional, requiring the ability to translate data insights into strategic business decisions.
Your task includes:
- Examining customer segment patterns
- Evaluating customer engagement levels
- Providing data-driven marketing recommendations
Understanding the Banks Marketing Challenge
The bank is experiencinglow customer engagementwith itsterm deposit offerings. Your analysis should address:
- How can the bank improve engagement with the right customers?
- What customer characteristics influence campaign success?
- Which customer segments are most likely to open a term deposit?
What is a Term Deposit?
Aterm depositis asavings account where customers deposit money for a fixed period in exchange for a guaranteed interest rate. Banks use term deposits to secure long-term investments. This campaign aimed toincrease customer participation in term deposits.
Understanding Customer Engagement
Customer engagement is measured using:
- Campaign Contacts:How many times a customer was contacted.
- Response Rate:Percentage of customers whoopened a term deposit (“Yes”) vs. those who declined (“No”).
Customer Clusters Explanation
Each customer has been pre-assigned to one of three clusters:
| Cluster | Description |
| High-Value Customers | Large account balances, strong financial history, and most likely to engage with financial products. |
| Moderate-Value Customers | Some financial activity, potential for engagement with the right incentives. |
| Low-Value Customers | Low financial engagement, least likely to respond but may be incentivized. |
Understanding the Dataset
The dataset includes customer characteristics, campaign interaction history, and pre-assigned cluster labels.
| Variable | Description |
| Customer ID | Unique identifier for each customer |
| Age | Customer’s age |
| Job | Profession of the customer |
| Marital Status | Single, Married, or Divorced |
| Education | Primary, Secondary, or Tertiary |
| Balance | Account balance |
| Campaign Contacts | Number of times contacted |
| Previous Outcome | Outcome of previous marketing campaigns (Success/Failure) |
| Response (Yes/No) | Whether the customer opened a term deposit |
| Cluster | Customer segment (High-Value, Moderate, Low-Value) |
Using the provided dataset, conduct an analysis to understand customer behavior, segment differences, and key drivers of engagement. Your goal is to extract meaningful insights that will inform strategic marketing recommendations. Complete the following steps in Excel and present your findings in a professional report.
1. Descriptive Statistics. Analyze customer behavior by calculating the following key statistics in Excel:
- Central Tendency (Mean, Median, Mode)
- Age: Average customer age
- Balance: Average customer balance
- Campaign Contacts: Average number of times contacted
- Variability (Standard Deviation, Range)
- Balance Distribution: Do account balances vary widely?
- Campaign Contacts: Were some customers over-contacted?
- Categorical Breakdown
- Marital Status: Percentage of married, single, and divorced customers.
- Education Level: Percentage of primary, secondary, and tertiary education levels.
- Cluster Distribution: Percentage of customers in each cluster.
2. Cluster Analysis Review. Examine how each cluster differs basked on:
- Demographics: Average age, marital status, and education.
- Engagement: Contact frequency, response rate.
- Term Deposit Likelihood: Which cluster is most responsive?
- Balance Differences: Do High-Value customers have significantly larger balances?
- Campaign Contacts: Are High-Value customers contacted more often?
- Response Rate: Which cluster had the highest “Yes” rate?
3. Visualizations for Cluster Comparisons. Use Excel charts to present key insights:
- Bar Chart: Show marital status or education level by cluster.
- Box Plot or Histogram: Compare balance across clusters.
- Scatter Plot: Show the relationship between balance and campaign contacts.
4. Comparative Analysis. Compare customer engagement and behavior across clusters:
- Compare Two Clusters (High-Value vs. Low-Value)
- Behavior: How does engagement differ?
- Engagement Patterns: Which cluster is more responsive?
- Demographic Influence: Are factors like age or marital status affecting engagement?
- Identify Key Factors Driving Customer Engagement
- Balance: Do higher balances correlate with more engagement?
- Campaign Contacts: Does frequent outreach help?
- Previous Outcome: Does a past “Success” predict future engagement?
- Use Basic Statistical Tests
- Correlation: Relationship between balance & response rate
5. Strategic Recommendations. Based on your findings, provide three targeted strategies to improve the banks marketing efforts.
- What recommendations would you make for each customer segment (High-Value, Moderate-Value, Low-Value)?
- How should the bank adjust its approach to increase engagement?
- What specific marketing tactics could be most effective for each segment?
Submission
- Submit two documents, a Microsoft Word written analysis with data visualizations and a Microsoft Excel workbook file.
- Your report should be written from the perspective of a data analyst providing actionable insights to improve the banks marketing strategy. Ensure your analysis is clear, data-driven, and tailored to business decision-makers.
- Your submission must be your own original analysis, demonstrating your ability to interpret data, generate insights, and develop strategic recommendations.
- All recommendations should be supported by data from the analysis and, where applicable, credible external sources. Use proper APA citations to reference industry reports, academic research, or relevant business frameworks that support your conclusions.
Requirements: 2 documents
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