The Federal Trade Commission’s Consumer Sentinel Network Data Book, prepared annually for Congress, is a compilation of all consumer complaints received by the agency over the course of the year. The data provides the most quoted numbers related to inquiries and complaints about debt collection and other industries.

Although the FTC stipulates the data is raw, not verified, and in most cases not followed up on, the overall number of “complaints” is too juicy to pass up for most mainstream media outlets.


I personally think it’s a promising development that the CFPB will be taking over ownership of the inquiry process for debt collection – rumored to be happening in the second quarter of this year – because the CFPB’s intention is to resolve, not just collect, these complaints. This should provide more public perspective to what’s behind the big numbers. But as I’ve said before, the devil is truly in the details, and I suspect this devil is what has delayed the announcement of a clear process by the CFPB.

The FTC receives data for the Sentinel Book throughout the year from multiple sources; it turns out there is wide variability in the quality of the data across those sources. I hope the CFPB notes this as it develops its resolution program. This post is meant to illustrate the variability in the complaint data the federal government receives, and explain why it’s meaningful.

First, here are the current sources of inquiry – or complaint – data:

Data Source % of inquiries from each source
FTC Online Complaint Assistant  37.0%
FTC Call Center  26.4%
PrivacyStar (mobile phone app)  19.5%
Better Business Bureau  12.4%
Attorney General offices  2.8%
Other  1.8%
Total  100.0%


The most complex task relative to processing debt collection complaints is verifying which company is being complained about. In the case of banks, for instance, there are relatively few entities, and many of them are easily recognizable household names. It’s generally easy for a consumer to identify who they are interacting with.

In the case of debt collection, there are thousands of legitimate firms, many with similar names, none of which advertise to consumers; therefore they are not commonly known. There are also literally thousands of additional entities that are not legitimate; they have made up names, they change their name, or they simply don’t provide their name. This, among other things, makes it challenging for someone tasked with adjudicating an inquiry.

When we analyzed the FTC’s inquiry data for Q1 2012, we sought to normalize the names so we could identify how many different companies were complained about and who received the most complaints. We used our industry knowledge, along with web searches, to complete this task. We were also fairly conservative about making assumptions about what a consumer likely meant when providing a partial name.

Our process for analyzing the Q2 2012 data went a bit deeper, incorporating phone calls. As we placed calls to approximately 4,000 numbers provided by consumers, we began to recognize patterns. For instance, many numbers are now disconnected, not in service, or couldn’t be completed as dialed. There were also many instances where the auto attendant and/or individual answering the phone did not state the name of the company, making it much more difficult to verify the entity referred to in the inquiry. On the other hand, we noticed that there were cases with great consistency in the company name provided, patterns to the phone numbers and how the phones were answered, and much more complete physical address information.

Because the 4,000 calls were made sequentially and not randomly, we felt we could not report that data as statistically significant. Since it would have been impractical for us to call 100% of the 45,613 inquiries lodged in Q2 2012, we created a random sample of 200 and called 100% of those.

Our random sample, summarized below, very closely approximated the total universe of source data noted in the chart above. Here is how the sources broke down among our 200 randomly selected calls:

Source of data # of inquiries in the random sample % of inquiries in the random sample
FTC Online Complaint Assistant 76 38.0%
FTC Call Center 54 27.0%
PrivacyStar 41 20.5%
Better Business Bureau 23 11.5%
Attorney General offices 0  0.0%


For each call, we recorded what we experienced. These were the possible call outcomes:

Outcome Our definition
OK The company reached clearly matches the data provided by the consumer
Unclear Unable to verify without further conversation because the auto and/or live attendant did not identify the company
Different than stated The company name, as identified by the auto or live attendant, did not match the data provided
Can’t be completed Received a fast busy signal, an automated “call can’t be completed as dialed,” or other similar situation
Not a collector The complaint was “tagged” as a 3rd party debt collection complaint, but the only company (and phone number) listed is for a creditor, telemarketer, or other non-3rd party collection entity
# Not in service Automated phone company alert
# Disconnected Automated phone company alert
Payday loan related This was a category so prevalent in our analysis of Q1 2012 data we thought it worth noting separately


Here is what we found, by source:

FTC Online Complaint Assistant # of instances % of calls to this source
OK 36 47.4%
Unclear 17 22.4%
# Not in service 9 11.8%
Can’t be completed 5 6.6%
Payday 3 3.9%
Not a collector 3 3.9%
Different than stated 2 2.6%
# Disconnected 1 1.3%
Total 76 100.0%


FTC Call Center # of instances % of calls to this source
OK 24 44.4%
Unclear 20 37.0%
Can’t be completed 5 9.3%
Not a collector 2 3.7%
# Not in service 2 3.7%
Payday 1 1.9%
Different than stated 0 0.0%
# Disconnected 0 0.0%
Total 54 100.0%


Privacy Star # of instances % of calls to this source
Unclear 16 39.0%
Can’t be completed 8 19.5%
Not a collector 7 17.1%
OK 5 12.2%
Different than stated 3 7.3%
# Not in service 1 2.4%
# Disconnected 1 2.4%
Payday 0 0.0%
Total 41 100.0%


Better Business Bureau # of instances % of calls to this source
OK 18 78.3%
Unclear 3 13.0%
Can’t be completed 1 4.3%
Not a collector 1 4.3%
# Not in service 0 0.0%
Payday 0 0.0%
Different than stated 0 0.0%
# Disconnected 0 0.0%
Total 23 100.0%


Privacy Star didn’t even exist a few years ago, yet today it’s the source of over 20% of all complaint data to the FTC. In the eyes of the mass media, all complaints are created equal, yet PrivacyStar inquiries can be cleanly verified just over 12% of the time, whereas the process used by the Better Business Bureau leads to verifiable data 78.3% of the time.

The largest percentage of inquiries is coming through the FTC’s Online Complaint Assistant. In this case, only 47.4% of inquiries are easily attachable to a clear company. Any way you look at it, this is a daunting task.

In my opinion, it’s encouraging that the CFPB is working to implement a process that somewhat resembles the BBB system. A well-designed process that aims to resolve rather than just collect consumer inquiries will be good for consumers, as well as for businesses, because it will help to get to the bottom of who is following the law and who isn’t.

It’s also not surprising that the CFPB has not yet announced a date when their process will officially begin. I suspect that as they’ve dug into the data in this area, they have learned that the process is not so clear cut, and the labor required to follow up on the sheer volume of inquiries is massive.

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