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MKTG6018 – Customer Analytics & Relationship Management

Group Assignment Brief: Customer Complaint Intelligence Report (S1 2026)

Data is abundant. What is scarce is the ability to transform raw data into clear, credible, and strategically useful insights. This group assignment is designed to develop your capability to work with real-world customer data from acquisition through managerial recommendations. In this assignment, you will collect, clean, prepare, integrate, analyse, visualise, and interpret consumer complaint data from the U.S. Consumer Financial Protection Bureau (CFPB). You will also enrich the complaint data by linking it with the Social Vulnerability Index (SVI) dataset using a ZIP–FIPS cross-walk. Your task is not simply to describe patterns, but to generate strategic insights and recommendations for senior management.

Your final submission should be written as a professional report for an executive audience. The report should be no longer than 25 pages (Word Document or PDF), including all components such as tables, figures, and references, but excluding appendices.

Your team is the Strategic Analytics Unit for one of the Big 4 Banks in the United States: Bank of America, JPMorgan Chase, Citi, or Wells Fargo. You are preparing an analytics report for the Chief Customer Officer (CCO).

The CCO wants to better understand recent patterns in consumer complaints, identify the most important complaint-related risks facing the bank, and determine how customer experience and complaint management can be improved.Using complaint data collected from the CFPB Consumer Complaint Database for the period 1 January 2023 to 31 December 2025, your team will produce a data-driven strategic report.

All groups must analyse the broader Big 4 complaint landscape in Part 1. In the remaining parts, each group will focus on its allocated bank.

Groups must independently collect complaint data from the CFPB Consumer Complaint 

Database: https://www.consumerfinance.gov/data-research/consumer-complaints/search/ 

The data should cover the period (3 years): 1 January 2023 to 31 December 2025

The complaint data include structured fields such as:

2. Social Vulnerability Index (SVI)

Groups must also incorporate county-level vulnerability indicators from the Social Vulnerability Index (SVI) 2022 dataset: https://www.atsdr.cdc.gov/place-health/php/svi/svi-data-documentation-download.html

Your team should choose “2022” “United States” “Counties” for “CSV File.”

Because the CFPB complaint data contain ZIP code information while SVI is reported at the county/FIPS level, groups must perform a ZIP–FIPS cross-walk in order to merge complaint records with SVI measures (consider the use of Excel VBA or Python).

Data Acquisition, Cleaning, and Preparation

Unlike previous versions of this assignment, the datasets will not be pre-provided. Each group must independently source and prepare the data.

Groups are expected to perform basic data cleaning and preparation, including but not limited to:

Not all complaint records will be successfully matched to SVI. This is expected. You will not be penalised for imperfect match rates if your approach is transparent and reasonable.

Required Scope of Analysis

Part 1

All groups must analyse complaints across the following four focal institutions:

To ensure consistency, groups should use the appropriate company labels as they appear in the CFPB data.

Each group must focus on its allocated bank only (allocation will be made during the week 4 workshop).

Report Structure

Part 0 – Data Acquisition, Cleaning, and Enrichment (10 points)

Provide a concise but clear description of how your data were collected, prepared, and enriched.

Your discussion should include:

• how the CFPB data were accessed,

• the filters (date, companies, etc.) used to collect the data,

• the variables retained,

• the number of observations before and after cleaning,

• key cleaning decisions,

• how missing values and scrubbed ZIP codes were handled,

• how the ZIP–FIPS cross-walk was conducted,

• how SVI data were merged, and

• any important data limitations.

This section should demonstrate methodological transparency and reproducibility.

Part 1 – Big 4 Complaint Landscape Analysis (20 points)

In this section, your team must provide an industry-level overview of the complaint landscape across the Big 4 banks during 2023–2025.

The purpose of this section is to give the CCO a broad understanding of the external complaint environment.

Your analysis may address questions such as:

This section should go beyond simple counts. You should identify and explain the most meaningful patterns in the broader complaint environment.

Part 2 – Strategic Diagnosis of the Assigned Bank (15 points)

In this section, your team must focus on your allocated bank and identify the most important complaint-related challenges facing that bank.

This section should be strategic, not merely descriptive.At a minimum, your team must identify and justify the three most important complaint priorities for your assigned bank. These priorities should be supported by evidence from the data.

Your analysis may consider:

• complaint concentration by product, sub-product, issue, or sub-issue,

• changes over time,

• state-level and/or county-level concentration,

• company response outcomes,

• timely response patterns, and

• other relevant structured indicators.

The goal is to help the CCO understand where attention and action should be prioritised.

In this section, your team must use the SVI-linked complaint data to examine how complaint patterns relate to community vulnerability.

The objective is to move beyond complaint counts and consider whether complaint burden or complaint characteristics differ across more and less vulnerable communities.

• Are complaints disproportionately concentrated in counties with higher social vulnerability?

• Do certain product categories appear more prominent in more vulnerable communities?

• Are some complaint issues more common in high-SVI areas?

• For your assigned bank, are there patterns that suggest elevated complaint risk in more vulnerable communities?

You should interpret patterns carefully. You are not expected to make strong causal claims.

Instead, your task is to identify meaningful associations and discuss their strategic implications. Groups may use the overall SVI indicator and a limited number of summary SVI themes (e.g., RPL_Theme1, RPL_Theme2, RPL_Theme3, RPL_Theme4, and RPL_Themes).

Part 4 – Voice of Customers (VoCs) Analysis (20 points)

Structured complaint fields do not fully capture the voice of the customer. In this section, your team must analyse complaint narratives for your assigned bank in order to uncover deeper insights into customer dissatisfaction and pain-points.

Because not all complaint records contain public narratives, your team should work with the subset of narrative data available.

You may choose some of the following approaches:

• Theme-based analysis: identify recurring complaint themes or patterns in customer narratives,

• Tone/language analysis: examine emotional tone, intensity, or escalation language, or

• Narrative-response linkage: explore whether certain narrative characteristics appear associated with different company response outcomes.

The purpose of this section is to uncover insights that are not fully visible in the structured complaint variables.

In the final section, your team must present three evidence-based recommendations for the Chief Customer Officer.

Your recommendations should be specific, strategic, and clearly supported by your analysis.

Your three recommendations must include:

• one recommendation related to complaint management or operational response,

• one recommendation related to customer experience or service recovery, and

• one recommendation related to data, analytics, or ongoing monitoring.

Strong recommendations will be realistic, prioritised, and directly tied to the evidence presented in earlier sections.

• Word document or PDF

• clear section headings

• professional tables and figures

• appropriate referencing where relevant

Recommended length

Maximum 25 pages, including tables, figures, and references. Appendices may be used where necessary, but the main report should remain concise and executive-focused.

Suggested appendices

You may include the following in appendices (a separate single document) if relevant:

• data extraction screenshots,

• workflow summaries,

• additional tables or figures,

• supplementary text-analysis details.

• AI Use Statement: If your group uses AI in any meaningful way, you must disclose this in a short AI Use Statement included in an appendix. The statement should briefly indicate:

o which AI tool(s) were used,

o what the tool was used for, and

o how the group verified and revised the output.

Submitting AI-generated material that is inaccurate, generic, unverified, or misleading will be treated as poor academic practice and may also raise academic integrity concerns

There are few short video tutorials on Canvas designed for this assignment. Also note that you are not required to provide a very detailed text description of each visualisation. The visualisation itself should be easy to comprehend, and few sentences would be a sufficient supplement for the visualisation. Although we indicated 25 pages as a maximum limit, some additional pages will be accepted without any penalty. Nonetheless, please assure that you are reporting to the senior management team, and they won’t want to read a book.

Be creative and insightful! Happy Analytics!