The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database. The database generally updates daily.

Consumer complaints registered with the Oregon Dept. of Justice. The database of consumer complaints is derived from consumer contacts for the years of 2017 - 2019 and is for informational purposes only. This dataset may not offer a completely accurate or comprehensive account of every incident. Several factors, including a company’s size and volume of transactions, may affect the likelihood of a consumer complaint being filed. The number of complaints about a business may not be a reliable measure as to whether it is appropriately conducting business. The information in this dataset is updated as soon as possible. However, recently submitted complaints may not be immediately available. The statements in this dataset do not necessarily reflect the opinion of the DOJ. For more information, see http://www.doj.state.or.us/finfraud/index.shtml

"This dataset captures customer complaints tied to service and experience failures, offering critical insights into where and how breakdowns occur. Sourced from reviews across 160+ industries, it focuses on moments when expectations weren’t met — and how consumers express that failure.

Key data features:

-Complaint text classified by service failure (e.g., “agent never responded,” “damaged item,” “billing error”) -Sentiment of the review (e.g., positive, negative, neutral) -Optional metadata: company/brand, timestamp, region, platform -Resolution request tagging (e.g., refund, apology, fix, cancellation)

The list may vary based on the industry and can be customized as per your request.

Use this dataset to:

-Train AI models that triage and escalate high-frustration complaints -Monitor systemic failure trends across brands or departments -Detect CX touchpoints that drive dissatisfaction or legal risk -Develop bots and assistants that recognize emotional cues in complaints -Inform service design teams about recurring pain points

Whether for automation, empathy modeling, or escalation tracking, this dataset transforms raw frustration into structured intelligence for customer experience leaders and AI builders."

With competition getting more stiffer in telecom direct-to-home operators, complaint management process will lead to the key outcome on business survival and growth.

As per the prevailing process and business setups, to cut down on cost companies prefer outsourcing of call centre operations and complaint management process.

Process Steps for handling customer complaints

Customer logs complaint from various modes like call at the call centre, visit retailer/company showroom/ website site/ App/ Email on customer care/ Social media (Facebook / Twitter) All major operators in business give complete attention and alertness on each and every customer complaint and after customer complaint getting logged in the system , same flows to back end team through CRM workflows Dedicated Service recovery teams made available in backend or service agencies Each and every case got assigned to the backend/service team for a customer visit and complaint closure There are broadly two types of transactions for complaints (FTR) First-time resolution and (NFTR) Non-first time resolution. For FTR cases, front end team like a call centre or showroom executive do the required troubleshooting and give resolution to customer and case closed as per customer satisfaction In NFTR cases, backend operation team aligned and visit done at customer premises and closure done by rectifying hardware, product or Outdoor unit. Some operators give delight code/ Happy code to the customer on logging of NFTR complaints and same code need to be provided to the engineer if complaint got resolved as per customer satisfaction Major Challenges in handling customer complaints

During sudden technical failure or any natural calamity, there will be a high flow of complaints, which takes time to manage and close the complaint to customer expectation. These instances bring challenging time in telecom/ DTH operators as a customer not ready for any delay in resolution As per business requirements, there has been a lot of fresh hiring done for call centre advisors and a lot of efforts being put on their training but due to the initial learning curve, basic mistakes done by new hires leading to irrelevant and wrong complaints being raised in the system. This sometimes leads to delay in resolution and telecom/DTH operator undergo firefighting scenarios Managing social media errors is also one of the challenging tasks, sometimes operator’s reputation goes to stake due to small negligence of any employee or any process failure

content

This dataset consists of almost 2224 rows and 11 columns which belongs to all complaints can be raised by user

Acknowledgements

Reference : https://github.com/Kavyapriyakp/Telecome-Consumer-Complaints-Data-Analytics-PYTHON

NCCDB is a web-based information system for recording and reporting on household goods, safety violation, hazardous material, cargo tank and passenger complaints. NCCDB allows the public and FMCSA staff to submit complaints using an online form. The database contains, among other information, reports on inspection and test of cargo tanks and inventory of tanks. These reports are used in the development and amendment to regulations of cargo security which is the protection of cargo from theft.

Consumer complaints registered with the Oregon Dept. of Justice. The database of consumer complaints is derived from consumer contacts for the years of 2017 - 2019 and is for informational purposes only. This dataset may not offer a completely accurate or comprehensive account of every incident. Several factors, including a company’s size and volume of transactions, may affect the likelihood of a consumer complaint being filed. The number of complaints about a business may not be a reliable measure as to whether it is appropriately conducting business. The information in this dataset is updated as soon as possible. However, recently submitted complaints may not be immediately available. The statements in this dataset do not necessarily reflect the opinion of the DOJ. For more information, see http://www.doj.state.or.us/finfraud/index.shtml

Problem statement

For a financial company, customer complaints carry a lot of importance, as they are often an indicator of the shortcomings in their products and services. If these complaints are resolved efficiently in time, they can bring down customer dissatisfaction to a minimum and retain them with stronger loyalty. This also gives them an idea of how to continuously improve their services to attract more customers. These customer complaints are unstructured text data; so, traditionally, companies need to allocate the task of evaluating and assigning each ticket to the relevant department to multiple support employees. This becomes tedious as the company grows and has a large customer base. In this case study, you will be working as an NLP engineer for a financial company that wants to automate its customer support tickets system. As a financial company, the firm has many products and services such as credit cards, banking and mortgages/loans.

Business goal

You need to build a model that is able to classify customer complaints based on the products/services. By doing so, you can segregate these tickets into their relevant categories and, therefore, help in the quick resolution of the issue. With the help of topic modelling, you will detect patterns and recurring words present in each ticket. This can be then used to understand the important features for each cluster of categories. By segregating the clusters, you will be able to identify the topics of the customer complaints. You will be doing topic modelling on the .json data provided by the company. Since this data is not labelled, you need to apply techniques to analyze patterns and classify tickets into the following five clusters based on their products/services:

1. Credit card / Prepaid card

2. Bank account services

3. Theft/Dispute reporting

4. Mortgages/loans

5. Others

With the help of topic modelling, you will be able to map each ticket onto its respective department/category. You can then use this data to train any supervised model such as logistic regression, decision tree or random forest. Using this trained model, you can classify any new customer complaint support ticket into its relevant department.

Dataset

The data set given to you is in the .json format and contains 78,313 customer complaints with 22 features.

Listing of consumer complaints filed against Insurance companies licensed in Connecticut. This dataset includes the Company, Line of Business, nature of complaint, outcome or resolution, and recovery.

This data set features consumer complaints against businesses that were mediated by the DCA Consumer Services Division during the last and current calendar years. It excludes complaints that may have ongoing legal investigations.

Complaint data from consumer complaints filed with the Consumer Protection Division. The existence of a complaint is not evidence of wrongdoing.

Analysis of ‘Consumer Services Mediated Complaints’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a1695a32-10c7-412c-ae3e-5e6907fb77a7 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This data set features consumer complaints against businesses that were mediated by the DCA Consumer Services Division during the last and current calendar years. It excludes complaints that may have ongoing legal investigations.

--- Original source retains full ownership of the source dataset ---

DESCRIPTION

Comcast is an American global telecommunication company. The firm has been providing terrible customer service. They continue to fall short despite repeated promises to improve. Only last month (October 2016) the authority fined them a $2.3 million, after receiving over 1000 consumer complaints. The existing database will serve as a repository of public customer complaints filed against Comcast. It will help to pin down what is wrong with Comcast's customer service.

Data Dictionary

1. Ticket #: Ticket number assigned to each complaint
2. Customer Complaint: Description of complaint
3. Date: Date of complaint
4. Time: Time of complaint
5. Received Via: Mode of communication of the complaint
6. City: Customer city
7. State: Customer state
8. Zipcode: Customer zip
9. Status: Status of complaint
10. Filing on behalf of someone

Analysis Task

To perform these tasks, you can use any of the different Python libraries such as NumPy, SciPy, Pandas, scikit-learn, matplotlib, and BeautifulSoup.

• Import data into Python environment.
• Provide the trend chart for the number of complaints at monthly and daily granularity levels.
• Provide a table with the frequency of complaint types.

Which complaint types are maximum i.e., around internet, network issues, or across any other domains. - Create a new categorical variable with value as Open and Closed. Open & Pending is to be categorized as Open and Closed & Solved is to be categorized as Closed. - Provide state wise status of complaints in a stacked bar chart. Use the categorized variable from Q3. Provide insights on:

Which state has the maximum complaints Which state has the highest percentage of unresolved complaints - Provide the percentage of complaints resolved till date, which were received through the Internet and customer care calls.

Each week we send thousands of consumers' complaints about financial products and services to companies for response. Complaints are listed in the database after the company responds or after they’ve had the complaint for 15 calendar days, whichever comes first.

We publish the consumer’s description of what happened if the consumer opts to share it and after taking steps to remove personal information. See our Scrubbing Standard for more details

We don’t verify all the facts alleged in these complaints, but we take steps to confirm a commercial relationship. We may remove complaints if they don’t meet all of the publication criteria. Data is generally refreshed nightly. Company level information should be considered in context of company size and/or market share.

More about the Consumer Complaint Database | How we use complaint data | Technical documentation

BEE-spoke-data/consumer-finance-complaints

consumer-finance-complaints but in a format that actually works.
Pulled Feb 2024

This web site is designed to help you obtain basic information about complaints filed regarding companies that do business in Hawaii.

The web site provides access to complaints that were filed with or initiated by OCP. Case numbers reflected in this web site relate to OCP cases.

The information contained in this web site DOES NOT comprise all information from official OCP records available to the public. For more detailed information about how cases are processed in OCP, go to http://hawaii.gov/dcca/ocp/about.

Legal Actions that were filed by OCP before 2001 may not be reflected on this site.

E-commerce Customer Churn Dataset

Context

This dataset belongs to a leading online E-commerce company. The company wants to identify customers who are likely to churn, so they can proactively approach these customers with promotional offers.

Content

The dataset contains various features related to customer behavior and characteristics, which can be used to predict customer churn.

Features

1. Tenure: Tenure of a customer in the company (numeric)
2. WarehouseToHome: Distance between the warehouse to the customer's home (numeric)
3. NumberOfDeviceRegistered: Total number of devices registered to a particular customer (numeric)
4. PreferedOrderCat: Preferred order category of a customer in the last month (categorical)
5. SatisfactionScore: Satisfactory score of a customer on service (numeric)
6. MaritalStatus: Marital status of a customer (categorical)
7. NumberOfAddress: Total number of addresses added for a particular customer (numeric)
8. Complaint: Whether any complaint has been raised in the last month (binary)
9. DaySinceLastOrder: Days since last order by customer (numeric)
10. CashbackAmount: Average cashback in last month (numeric)
11. Churn: Churn flag (target variable, binary)

Task

The main task is to predict customer churn based on the given features. This is a binary classification problem where the target variable is 'Churn'.

Potential Applications

1. Customer Retention: Identify at-risk customers and take proactive measures to retain them.
2. Targeted Marketing: Design specific marketing campaigns for customers likely to churn.
3. Service Improvement: Analyze features contributing to churn and improve those aspects of the service.

Acknowledgements

This dataset is provided for educational purposes. While it represents a real-world scenario, the data itself may be simulated or anonymized.

This dataset provides a collection of Comcast customer complaints, offering valuable insights for the context estimation of top customer issues. It is ideal for projects in data science and analytics, particularly those involving Natural Language Processing (NLP) to understand customer feedback and service performance. The dataset aims to support the analysis of telecommunications service interactions.

Columns

The dataset contains 11 distinct features, each offering specific details about customer complaints: * Ticket #: A unique identifier for each complaint ticket. * Unique ID: Another unique identification number for each entry. * Customer Complaint: The main subject or brief summary of the complaint. * Complaint Description: A more detailed account of the customer's issue. * Date: The date when the complaint was logged. * Date_month_year: The complaint date presented in an alternative format. * Time: The specific time of the complaint. * Received Via: The medium through which the complaint was received (e.g., call, online). * City: The city from which the complaint originated. * State: The state from which the complaint originated. * Zip code: The postal code associated with the complaint location. * Status: The current resolution status of the complaint.

Distribution

This dataset is tabular in format, typically provided as a CSV file. It comprises 2.2 thousand (2.2k) samples or records, each with 11 distinct features.

Usage

This dataset is particularly suited for: * Data Science and Analytics: To uncover trends and patterns in customer complaints. * Natural Language Processing (NLP): For sentiment analysis, topic modelling, and categorisation of customer feedback. * Telecommunications Companies: To improve customer service, identify recurring issues, and enhance service quality. * Mobile and Wireless Sector Research: For understanding service-related issues within this domain. * Customer Service Improvement: To pinpoint areas requiring operational enhancements.

Coverage

The dataset includes customer complaints from various geographic locations, with notable concentrations in Georgia (13%) and Florida (11%), and specific cities like Atlanta (3%) and Chicago (2%). A significant portion (76% for states, 95% for cities) originates from other regions. While listed as having a global region coverage on some platforms, the provided samples indicate specific regional data. The time range of complaints observed in data samples spans from April 2015 to December 2015 and includes samples from January 2022.

License

CC0

Who Can Use It

This dataset is intended for a range of users, including: * Data Scientists: For building predictive models or performing exploratory data analysis on customer feedback. * Business Analysts: To identify operational inefficiencies and areas for service improvement within telecommunications. * NLP Researchers: To develop and test algorithms for text classification and information extraction from unstructured complaint data. * Customer Service Managers: To gain insights into common customer pain points and measure resolution effectiveness.

Dataset Name Suggestions

• Comcast Customer Complaints Analysis
• Telecom Service Issues Dataset
• Customer Feedback for Telecommunications
• Comcast Customer Care Logs

Attributes

Original Data Source: Comcast Telecom Complaints Dataset

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The Office of Consumer Protection (OCP) conducts investigations to ensure business compliance and to resolve complaints about consumer transactions that occur in Montgomery County, no matter where the consumer or the merchant are located. OCP staff determine the facts, make sure that businesses are properly registered, check if any laws were broken, and try to help the consumer and merchant come to an agreement to resolve any issues. This dataset provides information about business registration compliance audits, executive director-initiated investigations, and consumer complaint cases filed with OCP over the last three (3) years. The data includes information about the date the audit/ complaint was received, the assigned OCP consumer complaint case number, a description of the alleged conduct subject to the complaint, and the case status. The dataset also provides the Montgomery County Council District of the complainant, if applicable, and whether the complainant resides within a Montgomery County Council Equity Focus Area.