Does a customer’s income influence their level of frustration when they complain to the CFPB? Does a customer’s income influence their perceived discrimination when they complain to the CFPB?
To begin evaluating the role that social economics play in customer complaints to the CFPB, PositivityTechⓇ ingested Census Bureau income data and integrated it with CFPB complaints from the last year, ultimately identifying these insights:
- Complaints escalated to the CFPB are concentrated in mid-income ranges.
- Customers’ perceived bias, as identified by PositivityTech’s Bias Index, is consistent across income ranges and reveals variability when customers complain about specific products.
- Customers’ severe frustrations, as identified by PositivityTech’s Severity Score, are consistent across income ranges, and reveal variability when customers complain about specific products.
- Customers’ levels of frustration and perceived bias vary by financial institution, and are influenced by the target market / product mix, organizational culture, and policies.
Do income levels impact customer complaints?
Almost 75% of the complaints escalated to the CFPB last year are from customers with incomes that range between $35,000 and $99,000.
Customer complaints that reveal PositivityTech’s highest Bias Index and customer complaints that reveal PositivityTech’s highest Severity Score are compared to the entire set of customer complaints to the CFPB in the last 12 months. As showcased in the graph below, there is consistency of income distribution across all three of these groups.
Here is a complaint from a customer with a high income, which reveals a high Bias Index and high Severity Score:
I felt like I [had] a discrimination experience with customer service… When I made contact through a video relay service, they thought it was theft identification and flagged it to the fraud department. I understand their concern about security… I am deaf and using an interpreter to communicate with them. They had a communication barrier. XXX seems not to follow the America Disability Acts Law procedure. The customer care service representative threatened me and violated my rights.
Here is a complaint from a customer with a low income, which reveals a high Bias Index and high Severity Score:
I received a letter that claimed that activity on my account with XXX was indicative of high risk of failure to pay. This is simply not true as I have maintained an excellent payment and credit history. I called in and spoke to a telephone representative who did not provide good customer service and arrogantly told me to reapply if I wanted the card. I believe that I am a victim of XXXX and XXXX discrimination and redlining. While XXXX may be a low-income area, I am not in financial distress and have been a great customer.
Income levels differentiate perceived bias and levels of frustration when coupled with product-specific complaints
- Higher income complaints with high perceived bias are concentrated in mortgages and credit cards.
- Middle income complaints with high severity scores are concentrated in credit reporting and debt collection.
- Lower income complaints are concentrated in credit reporting and debt collection.
With benchmarking, see how frustrated your customers are
Using PositivityTech, you can create customized logic for financial institutions’ benchmark groups.
Using a benchmark group of 17 large financial institutions, we identified four institutions with the highest perceived bias based on their customers’ words, and four institutions with the highest customer frustrations based on their customers’ words.
Within this group of 17 large financial institutions, one institution has the highest percentage of complaints in both the High Bias Index range and the High Severity Score Index range. Here is a complaint from a customer of this financial institution, which reveals a high Bias Index and high Severity Score:
This was very rude behavior and absolutely humiliating. Yet again it wasn’t until after I had the assistance of my fiancé that this representative proceeded to further help. I feel as if I’ve been discriminated based on my gender and/or age… I’d also like to point out that I’ve always attempted to be very rational, respectful, and polite when speaking with these people so this kind of treatment was very unnecessary and demeaning. I plan to switch banks upon the release of my money… I hope these policies and lies do not continue to be implemented and overlooked.
The power of granular segmentation
PositivityTech provides you with the opportunity to understand which segments of your customer base are complaining, and to identify the issues most pressing for these segments.
You can gain the most sophisticated, granular views of your segments and address your critical front-end business needs. You can be on the cutting edge by integrating customer complaints as a data source — and turning negatives into positives.
See what PositivityTech can do for your business. Schedule a demo by emailing me at email@example.com.