Coding Test Instructions for Reviewers:
- Please open it with Python Framework
- Here I used Python 3 for the coding
- Please assess the overall code structure, comments, and documentation for clarity and organization.
Data details: The provided dataset is derived from the telemarketing efforts of a bank, with the goal of refining their strategy to promote term deposit accounts. It comprises various customer-related attributes and campaign-specific data. The dataset includes demographic information such as customer age, job type, marital status, and education level. Additionally, it captures financial indicators like whether the customer has defaulted on credit, their average yearly balance, and if they have housing or personal loans. The dataset also contains details about the bank's marketing interactions, including the method of the last contact, the day and month of the last contact, and the duration of that contact. Further campaign-specific metrics encompass the number of contacts made during the current campaign, the days since the client's last contact from a prior campaign, the number of previous contacts, and the outcome of the previous marketing campaign. The ultimate target variable, denoted by "Y," signifies whether the customer subscribed to a term deposit.
Predictive Modeling/Classification: ● Classify using Decision Tree: Accuracy - 89.75 ● Classify using Naive Bayes: Accuracy - 82.53 ● Compare the results of the two techniques: Decision Tree is better than Naive Bayes N.B: In the code, there are more details for the decision.
Conclusions and Recommendations: To detail the principal findings from the various sections of the dataset, I'll examine each section individually and highlight key insights:
Demographic Information (Age, Job, Marital, Education): The dataset reveals a diverse customer base, characterized by a wide age range, a variety of job types representing occupational diversity, a mixture of marital statuses including single, married, and divorced customers, and differing education levels among customers. These demographic factors highlight the need for a flexible and multifaceted marketing strategy that can effectively engage with a broad spectrum of potential clients.
Financial Status (Default, Balance, Housing, Loan): The financial analysis of the dataset yields significant insights. Notably, a substantial portion of customers has maintained a strong credit history, with a low incidence of credit defaults. Moreover, the dataset showcases a wide range of financial well-being, evidenced by the variability in average yearly balances in Euros, suggesting diverse economic backgrounds among customers. Furthermore, housing loans are prevalent among the customer base, while personal loans are less common, pointing to distinct preferences and financial situations. These findings emphasize the need to tailor financial products and marketing approaches to cater to the varied financial circumstances and preferences of customers.
Marketing Campaign Information (Contact, Day, Month, Duration, Campaign): The dataset's analysis of marketing campaigns offers several key observations. It demonstrates a variety of communication channels employed for contact. The distribution of contact days throughout the month indicates a flexible approach to reaching customers on different dates. Marketing campaigns were conducted throughout the year, potentially indicating seasonal effects on customer responses. Call durations vary, implying diverse engagement levels, and the number of contacts made during the campaign provides insights into the intensity of marketing efforts, helping gauge customer receptiveness to the bank's outreach. These insights can guide the bank in optimizing its marketing strategies.
Previous Campaign Data (Pdays, Previous, Poutcome): The dataset's examination of previous campaign data highlights two key findings. Firstly, a significant number of customers have not been contacted in prior campaigns, as evidenced by a -1 value in the 'Pdays' field, indicating a fresh approach for many clients. Secondly, the number of previous contacts and the outcome of prior campaigns provide valuable historical context, aiding in the understanding of past customer interactions. These insights enable the bank to tailor its current campaign more effectively based on past engagement and outcomes.
Target Variable (Y - Subscription to Term Deposit): The binary variable 'Y,' denoting subscription to a term deposit, serves as the primary focus of analysis in this dataset. Understanding the distribution of "yes" and "no" values in this variable is of paramount importance, as it holds the key to predicting and comprehending customer behavior. This target variable is central to the bank's marketing objective, guiding efforts to refine telemarketing strategies and enhance the success of promoting long-term deposit accounts.
Recommendation:
To enhance their telemarketing strategy, the company should first segment their customer base based on demographic and financial factors, tailoring their approach for different customer profiles. They should then carefully time their campaigns based on the distribution of contact days and consider potential seasonality effects. Analyzing the effectiveness of communication channels, adjusting call durations, and optimizing the intensity of contacts can improve campaign outcomes. Leveraging historical data to avoid over-contacting customers and personalizing messaging is essential. A/B testing can help identify the most effective strategies, while continuous monitoring and predictive analytics can assist in early subscriber identification. Furthermore, maintaining compliance with regulations and respecting customer privacy is crucial for long-term success. Lastly, investing in agent training and skill development is essential to ensure effective customer engagement and persuasion. These recommendations will enable the company to refine their telemarketing strategy and improve subscription rates for long-term deposit accounts.