Overview
hello sir, can you please help me adjust the assignment you helped me with for the Unit 4? The professor is giving me 48 hours to resubmit it. His feedback was,”I just asked my professor for feedback on the assignment you helped me with, and he said, “Overall, you are moving in the right direction. Your Excel workbook contains a substantial amount of supporting work, including raw data, descriptive statistics, cluster analysis, visualizations, and comparative analysis. That is a strength. The workbook shows that you made a serious effort to analyze the data, compare the customer clusters, and connect your findings to strategic marketing recommendations. Your written report also has a clear business focus, includes outside sources, and presents recommendations that are generally aligned with the bank’s marketing challenge.
My main recommendation is to make sure the written report contains enough of the major analytical findings from the workbook. It is acceptable, and often very useful, to place detailed calculations in Excel. However, the written report still needs to summarize the most important findings clearly enough that a business reader can understand the analysis without having to search through each worksheet. In a workplace setting, the Excel workbook supports the analysis, but the written report communicates the major findings, implications, and recommendations to decision-makers.
One of the strongest parts of your submission right now is the effort you put into the Excel workbook. You separated the raw data, descriptive statistics, cluster analysis, visualizations, and comparative analysis into different worksheets, which makes the supporting work easier to follow. That organization is helpful and shows that you are approaching the assignment in a structured way.
One important correction I recommend is reviewing how some of the response rates were calculated in the pivot tables. In some areas of the workbook, the pivot table appears to use “Sum of Customer ID” when calculating percentages. Customer ID should not be summed for response-rate calculations because the ID numbers are identifiers, not values that should be added together. For response rates, you should use “Count of Customer ID” or create a numeric response variable where “Yes” equals 1 and “No” equals 0, then calculate the average of that response variable.
This matters because some of the response-rate figures in the written report appear to be based on the summed Customer ID results rather than count-based response rates. Based on the count-based term deposit likelihood table, the Moderate cluster appears to have the highest response rate, followed closely by the High-Value and Low-Value clusters. This is still a useful finding, but the difference across the clusters is relatively small. Therefore, I recommend avoiding language that suggests the Moderate cluster is dramatically more responsive than the other groups.
A stronger and more defensible interpretation would be something like this: “The Moderate cluster had the highest term deposit response rate, but the difference across clusters was relatively small. Therefore, the bank should prioritize Moderate customers for targeted outreach while still maintaining tailored strategies for High-Value and Low-Value customers.” That wording is more precise because it reflects the data without overstating the size of the difference.
Your descriptive statistics section is well developed in the workbook, but I recommend bringing more of that information into the written report. The assignment specifically asks for central tendency, variability, and categorical breakdowns. I recommend adding a separate section in the written report titled something like “Descriptive Statistics and Customer Profile.” In that section, summarize the key descriptive statistics directly in the report, including average age, median age, average account balance, median account balance, balance variability, average campaign contacts, marital status distribution, education distribution, and cluster distribution.
This information should not remain only in the Excel workbook. The workbook supports the calculations, but the written report should communicate the main findings clearly. Including this section would strengthen the assignment because it would show that you addressed the descriptive statistics requirement directly in the report.
I also recommend adding a clearer cluster analysis section before the recommendations. Right now, the report moves fairly quickly from the introduction into strategic recommendations. The recommendations are useful, but the reader needs a stronger analytical bridge explaining what the clusters showed. Consider adding a section that compares High-Value, Moderate-Value, and Low-Value customers by average age, education level, marital status, average balance, average campaign contacts, previous outcome, and response rate. A concise table in the written report would work very well here.
After the table, add a short paragraph explaining the most important patterns. For example, you could discuss whether the clusters differ meaningfully in balance, whether campaign contacts were similar across segments, whether education level appears to vary by cluster, and which cluster had the highest response rate. This would make your recommendations stronger because the reader could see exactly how the data led to your conclusions.
Your correlation analysis is a good inclusion, and I appreciate that you attempted to assess the relationship between balance, campaign contacts, and response. However, be careful with the wording. The correlation between campaign contacts and response is very weak and negative. I would avoid saying that this “clearly indicates” that more contacts reduce engagement. A more accurate interpretation would be that the data shows a very weak negative relationship, suggesting that additional contact frequency does not appear to meaningfully improve response and may raise concerns about customer fatigue.
The same point applies to the relationship between balance and response. If the correlation is very weak, then higher balance does not appear to be a strong predictor of term deposit acceptance in this dataset. That is an important insight because it challenges the assumption that High-Value customers are automatically the best target. You are close to this interpretation already, but I recommend making it more explicit in the written report.
Your recommendations are thoughtful, but I recommend organizing them more directly by customer segment. The assignment asks what recommendations you would make for each customer segment: High-Value, Moderate-Value, and Low-Value. You discuss the Moderate segment in Strategy 1 and then discuss High-Value and Low-Value customers later in the report, but the final structure would be stronger if each segment had its own clearly labeled recommendation.
For example, you could use three subsections: “High-Value Customers,” “Moderate-Value Customers,” and “Low-Value Customers.” Under each subsection, provide one or two specific tactics and connect each tactic directly back to the data. For High-Value customers, you could recommend relationship-based outreach, loyalty messaging, or exclusive term deposit offers, while also acknowledging that their response rate was not substantially higher than the other groups. For Moderate-Value customers, you could recommend personalized digital outreach because this group appears to have the strongest response pattern. For Low-Value customers, you could recommend simple messaging, lower minimum deposit thresholds, short-term deposit options, or entry-level incentives because this group may need a lower barrier to participation.
Your use of outside sources is also a positive element. The references show that you are trying to support the recommendations with credible business and academic material. Before final submission, I recommend checking the APA formatting carefully. Make sure article titles, organization names, publication dates, capitalization, italics, and URLs follow APA expectations as closely as possible. Also, make sure each source has a clear purpose in the report. The sources should support specific marketing recommendations rather than appear only as general background support.
The visualizations are another area where you are on the right track, but I recommend expanding and aligning them more closely with the assignment directions. Your report includes a response-rate chart and a scatter plot showing the relationship between account balance and campaign contacts. The scatter plot is useful because it supports your point that balance and contact frequency do not appear to have a strong relationship. However, the assignment also asks for a bar chart showing marital status or education level by cluster and a box plot or histogram comparing balance across clusters. I recommend adding those required visualizations to the written report and discussing them directly.
For each figure, I recommend adding one or two sentences immediately after the chart explaining what the reader should notice. For example, after the education-by-cluster chart, explain which cluster has the highest percentage of tertiary-educated customers. After the balance distribution chart, explain whether balances vary widely across clusters and whether High-Value customers actually have significantly larger balances. After the scatter plot, explain that there does not appear to be a strong relationship between balance and campaign contacts.
There are also several writing and editing issues I recommend correcting before final submission. These are not major barriers to understanding, but they do affect polish and professionalism. For example, “educated-aligned” should likely be “education-aligned.” The phrase “leverage on interest rate incentives” should be revised to “leverage interest rate incentives” or “use interest rate incentives.” The phrase “number of number contacts” should be corrected. I also noticed wording such as “particular for both high and low-value customer clusters,” which should be revised to “particularly for both High-Value and Low-Value customer clusters.” In addition, be consistent with terms such as High-Value, Moderate-Value, Low-Value, term deposit, response rate, and campaign contacts.
My biggest recommendation is this: keep the strong Excel workbook, but expand the written report so it clearly presents the major findings from that workbook. The report should not only provide recommendations; it should also summarize the descriptive statistics, explain the cluster differences, interpret the visualizations, compare High-Value and Low-Value customers, discuss the correlation results, and then lead into the recommendations.”