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XYZ Debt Relief’s Customers - Statistics Assignment Help

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Added on: 2021-08-26 05:08:24
Order Code: MAS49761
Question Task Id: 347306
  • Country :

    United States America

Assignment Task
 

The Background:
XYZ Debt Relief’s customers are people who have a substantial amount of debt and because of an unexpected hardship, are no longer able to make their minimum monthly payments. Upon enrolling in XYZ’s Debt Relief program, customers cease making payments to their creditors and instead make deposits they can afford into a new dedicated bank account with XYZ. XYZ uses these funds to negotiate with each of the client’s creditors to settle the debt, typically at only a fraction of what was originally owed. Once a settlement has been agreed upon for an individual account, XYZ facilitates payments from the dedicated bank account to the creditor based on the terms of the settlement agreement. XYZ then collects fees from the client for the individual account that was settled. XYZ earns fees for each account it successfully negotiates a settlement agreement. The number of settlement agreements XYZ can negotiate is proportional to the monthly deposited amount. Assume industry statistics for any analysis input that you would want to use that is not provided here, including but not limited to fee percentage XYZ would collect along with ongoing cost per client between initial enrollment and program graduation.

The Question:
Imagine that XYZ ran a recent marketing campaign to promote the value proposition of how the debt relief program helps people achieve financial freedom. Assume the cost of this campaign was $5 million. There are five months of data in the datasets provided. Let’s say campaign took place over the course of the third month. You now want to show the marketing, sales and operations teams just how successful this campaign was.

Using the three datasets given:
Provide a quantitative assessment of whether the marketing campaign was successful. How and why did you choose your specific success metric(s)?
Based on the provided data, how would you recommend campaign strategy be adjusted in the future to improve performance?
How do you think campaign performance would have changed if we did not run the campaign in Month 3, but instead postponed it until month 6? Provide an incremental number versus your result in Question #1.
Is there anything not in the provided dataset that you think would provide value if added? If so, how would you strategize gathering and leveraging the additional information?

The Guardrails:
You are free to use any tool or means you would like to highlight your abilities, but we would like to see something more advanced than Excel pivot tables, for example. We will be assessing not only on your ability to provide the correct results, but also how you decide to visualize and explain your results.

Assume the following:
There is no seasonality in the results, and the campaign spend was distributed evenly across Month 3 (i.e., spend on the first day is the same as spend on the last day).
Channel mix, targeting, and efficiency are outside the scope of this exercise, but may be addressed as part of Question #4.
There may be data provided that is not useful in your end presentation.

Share your work back with us in this way:
Data Prep and Analysis: All methods, written code, visualizations, and extrapolations, including any and all analysis that may or may not have made it into the final presentation; we are interested in your ability to see what is important for business stakeholders versus what is not.
Findings and Recommendations: Use a tool, like PowerPoint, to tell your story in a compelling way. Assume that your findings will be presented to a mixed audience of senior leadership, business stakeholders, and peers in business intelligence.

The Data:
Attached you will find three files with the data you will need to complete the analysis.
*Note: These datasets were created for this analytical exercise and are only intended to be used to assess the critical thinking and technical abilities of interview candidates. This data purposely does not reflect actual client or deposit information. No inferences should be made from this information in regard to XYZ’s client base, deposit activity, company size or company growth trajectory.
client_table: You will find data specific to fictional clients
client_id: Randomly generated unique surrogate identifier for a client
client_geographical_region: Client geographical location in relation to U.S. Census definitions
client_residence_status: Client residence status in relation to whether they rent or own
client_age: Client age in relation to date of birth
deposit_table: You will find data specific to the client deposit behavior
client_id: Randomly generated unique surrogate identifier for a client
deposit_type: Delineates whether a client deposit is the scheduled record or actual record
deposit_amount: Client deposit amount to the dedicated bank account with XYZ
deposit_cadence: Timing and pattern of client deposit activity
deposit_date: Deposit date for deposit type
calendar_table: This is a calendar reference table
gregorian_date: This date aligns with the Gregorian calendar
month_name: These are the designated months in the case study
Month 1 and 2 are pre-campaign
Month 3 is the campaign
Month 4 and 5 are post-campaign

 

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  • Uploaded By : Brett
  • Posted on : August 26th, 2019
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