Exhibit 1 — In re Bank of America California Unemployment Benefits Litigation (Dkt. 614-3, S.D. Cal. No. 3:21-md-02992)

Record facts

U.S. District Court for the Southern District of California · No. 3:21-md-02992-GPC-MSB · Doc. 614-3 · 2026-01-08 · Docket on CourtListener

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Exhibit 1 
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   Confidential 
UNITED STATES DISTRICT COURT 
SOUTHERN DISTRICT OF CALIFORNIA 
IN RE BANK OF AMERICA CALIFORNIA 
UNEMPLOYMENT BENEFITS 
LITIGATION 
Case No. 3:21-md-02992-GPC-MSB 
EXPERT REPORT OF JAY MINNUCCI 
March 4, 2025 
REDACTED PUBLIC VERSION
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i 
TABLE OF CONTENTS 
I. 
ASSIGNMENT ...................................................................................................................... 1 
II. 
SUMMARY OF EXPERT QUALIFICATIONS ................................................................... 2 
III. 
SUMMARY OF OPINIONS .................................................................................................. 5 
IV. 
FACTS AND DATA CONSIDERED .................................................................................... 8 
V. 
HOURLY RATE .................................................................................................................... 8 
VI. 
BACKGROUND .................................................................................................................... 8 
A. 
Bank of America’s Prepaid Call Center Operations  ................................................. 8 
B. 
Call Center Industry Performance Standards ........................................................... 13 
C. 
Call Center Industry Operational Standards ............................................................ 18 
VII. 
STATEMENT OF OPINIONS AND BASIS FOR OPINIONS .......................................... 19 
A. 
Bank Subjected EDD Debit Cardholders Seeking Assistance with 
Unauthorized-Transaction Claims to Unprecedentedly Long Wait 
Times that Fell Far Below Industry Performance Standards ................................... 19 
B. 
The Bank’s Practice of Understaffing Its Claims Call Center in the 
Face of Foreseeable Call Volume Surges Was Inconsistent with 
Industry Standard Practice ....................................................................................... 26 
C. 
The Substandard Performance Levels in the Bank’s Claims Call 
Center from September 13, 2020 through November 21, 2020 
Reflected the Bank’s Broader Systemic Failure to Operate Its Prepaid 
Call Centers Consistent with Industry Standards ..................................................... 40 
D. 
The Bank’s Understaffing Choice Harmed Cardholders While Saving 
the Bank Money ....................................................................................................... 52 
 
VIII. 
 CONCLUSION ................................................................................................................... 59 
Appendix A: Curriculum Vitae ......................................................................................................... 60 
Appendix B:  Prior Publications (March 2014 to Present) ............................................................... 62 
Appendix C:  Prior Testimony (January 2020 to Present) ................................................................ 64 
Appendix D:  Materials List ............................................................................................................. 65 
Appendix E:  Survey Results, The 2021 US Contact Center Decision Makers’ Guide .................... 70 
Appendix F:  Claims Call Center Data 2020-2021 ........................................................................... 71 
Appendix G:  Fraud Tier 2 Call Center Data 2020-2021 .................................................................. 73 
Appendix H:  Calculating Distinct Demand ..................................................................................... 75 
Appendix I:  CSR Idle Hours Sample Calculation ........................................................................... 76 
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                                                                                                                                        Confidential 
 
1 
I. 
ASSIGNMENT 
1. 
I have been retained as an expert witness in this matter by Cotchett, Pitre & 
McCarthy LLP, and Altshuler Berzon LLP (“Counsel”), co-lead counsel for the Class Plaintiffs.  
2. 
I understand from Counsel that Class Plaintiffs are seeking certification of five 
classes, three of which are relevant to this report: (1) a “Claim Denial” class, defined as all Bank 
of America EDD debit cardholders who notified the Bank that an unauthorized transaction had 
occurred on their Bank of America EDD debit card account (“Claim”) at an automated teller 
machine (“ATM”), and whose Claim the Bank denied or closed at any time from September 28, 
2020, through June 8, 2021, based solely on Indicator 1 of the Bank’s Claim Fraud Filter 
(“CFF”); (2) a “Credit Rescission” class, defined as all Bank of America EDD debit cardholders 
who received permanent credit from the Bank in connection with their Claim, which credit the 
Bank rescinded at any time from September 28, 2020 through June 8, 2021, based solely on 
Indicator 1 of the Bank’s CFF; and (3) a “Customer Service” class, defined as all members of the 
Claim Denial Class and/or Credit Rescission Class who telephoned the Bank’s customer service 
telephone number for its EDD debit cardholders at any time between September 13, 2020 
through November 21, 2020, and whose telephone call was routed to the Bank’s Claims call 
center.  Counsel have asked me to review and analyze certain documents and data produced by 
the Bank and testimony provided by the Bank’s Rule 30(b)(6) designees on matters related to the 
Customer Service Class’s claims.  Specifically, counsel have asked me to determine the 
following:  
a. 
The Bank’s Claims call center performance metrics during 2020 and 2021, 
as measured using industry standard metrics; 
b. 
Whether the Bank’s Claims call center performance metrics fell below 
industry standards, and if so, to what extent and during what period; 
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c. 
Whether the operational processes, including call forecasting and staffing, 
and technology the Bank utilized in its Claims call center in 2020 and 
2021 fell below industry standards, norms, and best practices; 
d. 
Whether there were reasonable alternative actions the Bank could have 
taken consistent with industry standard practices to avoid or mitigate the 
adverse impact on EDD debit cardholders who telephoned the Bank’s 
customer service telephone number for its EDD debit cardholders at any 
time between September 13, 2020 through November 21, 2020, and whose 
telephone call was routed to the Bank’s Claims call center; and 
e. 
The effects on EDD debit cardholders of the Bank’s performance failures 
in its Claims call center between September 13, 2020 and November 21, 
2020. 
II. 
SUMMARY OF EXPERT QUALIFICATIONS 
3. 
I am the founder and owner of Service Agility, Inc. (“Service Agility”), an S-Corp 
domiciled in Pennsylvania.  Service Agility was founded in 2008 and provides consulting, 
training, and related services to the call center industry.   
4. 
I received a Bachelor of Business Administration from Temple University in 
1984, graduating magna cum laude.       
5. 
My entire professional career—more than four decades—has been in the call 
center industry, working for and advising in-house departments and third-party vendors that 
handle inbound and outbound customer service calls, and the corporate managers that oversee 
those departments and vendors. I spent 17 years in various leadership positions in a call center 
and have served as a consultant, author, speaker, and trainer for more than the last two decades. 
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6. 
In October 1983, I began working in the 800-seat call center serving the AARP 
Group Health Insurance Program, where I held a variety of supervisory and management roles, 
in both the call center and related back-office departments.     
7. 
In 1995, I was promoted to Director and Senior Director roles at that call center, 
where I established policies and practices to maximize call center performance while minimizing 
staffing expenses.  Some of my core responsibilities over the next five years in those positions 
included:  
a. 
Forecasting and budgeting oversight, including developing staffing 
expense estimates and monitoring actual spend vs. projections.  
b. 
Meeting our key performance metric of service level, which is a 
measurement of the percentage of calls answered within a certain number 
of seconds (e.g., 70% of calls answered within 30 seconds), while 
minimizing staffing expenses.  
c. 
Designing and developing the organization’s first Workforce Management 
Team.  In a call center, the Workforce Management (“WFM”) team is 
responsible for forecasting incoming call volumes and associated average 
handling times (the amount of time required to complete a call from the 
time it is answered), and using those projections to calculate the number of 
staff needed to meet the organization’s targeted service level objectives.   
d. 
Overseeing proper maintenance and support for key applications and 
systems, including overseeing telephone IT support positions (which 
perform technology related functions that are essential to satisfying call 
center performance metrics, such as designing and maintaining phone 
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systems, Interactive Voice Response (IVR) systems, outbound calling, and 
call monitoring). 
8. 
Following my 17 years of work in a call center, I began work as a call center 
consultant, first as Vice President of Consulting from 2000 to 2008 at the International Customer 
Management Institute (“ICMI”), where I provided call center consulting services to the Federal 
Reserve Bank, Discovery Financial, Charles Schwab, Wells Fargo, and dozens of other clients. 
During that time, I grew the firm’s consulting revenue from $0 to over $3 million per year.  In 
2008, I founded my own company, Service Agility, which has provided consulting services to 
over 100 clients operating call center services, including MetLife, Nationwide Insurance, 
Guardian Life, LGE Community Credit Union, Thrift Savings Plan, United Healthcare, Allstate, 
and UBS Group.  Through my work at both ICMI and Service Agility, I have, among other 
things: advised clients on best practices in selecting and managing an outsourcing provider; 
overseen the drafting, negotiation, and revision of General Service Agreements (GSAs) and 
Statements of Work (SOWs) involving dozens of call centers; provided guidance on determining 
proper staffing levels, including for call center agent positions, as well as other support positions; 
created forecasts and scheduling tools to help determine staffing needs; built quality assurance 
programs to help clients optimize call handling processes and results; educated clients on 
recruitment, training and employee retention strategies; and counseled executive teams on setting 
objectives and monitoring performance in call centers. 
9. 
In addition to my consulting work, I speak at industry events and I have published 
several articles in leading trade publications, focusing on best practices for the management of 
call centers.  More detail on my background is provided in my Curriculum Vitae in Appendix A.  
Details of my published works in the past ten years are included in Appendix B.  
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10. 
I have designed and delivered training courses for call center managers around the 
globe.  Through those courses, I estimate that I have trained well over one thousand call center 
managers. 
11. 
I have also worked as an expert witness in several cases involving call center 
operations, technology, service contracts, and related performance standards.  A list of the cases 
in which I provided testimony under oath in the past four years appears in Appendix C.  
III. 
SUMMARY OF OPINIONS 
12. 
The Bank’s data shows that from September 13, 2020 through November 21, 
2020, the performance of the Bank’s Claims call center was so substandard that it subjected 
cardholders to wait times rarely, if ever, seen in the call center industry. For example, the 
average speed to answer (“ASA”) is an industry-standard metric that reflects the average amount 
of time a customer is kept waiting on hold before their call is initially answered, a metric that is 
typically measured in seconds. The average ASA reported in 2020 across 214 call centers 
surveyed by ContactBabel was 75 seconds (1.25 minutes), which is consistent with (but on the 
higher end of) my understanding of the industry standard ASA generally.1 By contrast, between 
September 13, 2020 and November 21, 2020, 
 
 
 
 
 
 
1 The U.S. Contact Center Decision-Makers’ Guide is a report published by ContactBabel 
annually studying the “performance, operations, technology and HR aspects of US contact center 
operations;” the 2020 survey included data from 214 U.S.-based call centers, including 29 from 
the Finance industry and 28 from the Outsourcing/Telemarketing industry.  See The 2021 US 
Contact Decision-Makers’ Guide, 13th Edition, ContactBabel (“2021 Decision-Makers’ Guide”) 
at 13, 46. 
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 the industry average.  In their effort to get the 
Bank’s Claims call center to answer their calls, 
 
 
.  The performance of the Bank’s Claims call center during this ten-week period in 
the Fall of 2020 is by far the worst I have seen across a two-and-a-half-month period in my 41 
years in the industry.2 
13. 
Virtually all EDD debit cardholders who telephoned the Bank between September 
13, 2020 and November 21, 2020 and had their calls routed to the Bank’s Claims call center 
 
 
 
 
. 
14. 
The very long wait times to which EDD debit cardholders were subjected resulted 
 
. The average abandonment rate in 2020 
across call centers surveyed by ContactBabel was 6.1%.3 By contrast, 
 
 
 
. Because wait time before abandonment is not included in the ASA data, ASA 
 
2 To date, the only call center I have seen provide similarly inadequate service over two+ months 
is 
. 
3 See 2021 Decision-Makers’ Guide at 46. 
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significantly understates the actual amount of time spent on the phone by the many EDD debit 
cardholders who had to call and wait multiple times before connecting with a CSR. 
15. 
 
 
 
 
 
 
 
 
 
 
 
.  
16. 
Although the Customer Service class as defined by plaintiffs includes only those 
EDD debit cardholders who called the Bank and were routed to the Claims call center between 
September 13, 2020 and November 21, 2020, the substandard performance level in the Claims 
call center during this period was not an isolated occurrence for the Bank.  Throughout 2020 and 
2021, the Bank repeatedly failed to meet industry standards for disaster planning, staffing, and 
security across its call centers, all of which were factors causing the Bank to provide grossly 
substandard service levels for sustained periods of time.  
17. 
The Bank’s consistent conduct in understaffing its Claims call center and not 
using standard technology 
, 
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while causing significant harm to EDD debit cardholders.  
IV. 
FACTS AND DATA CONSIDERED 
18. 
In preparing this report, I have relied on my general knowledge, training, 
experience, and other expertise accumulated during my 41 years as an executive and consultant 
in the call center industry.  In addition, during the course of my analysis, I have relied upon 
documents and information produced in discovery in this case as well as publicly available 
documents and information.  I have relied upon the materials cited in this report and those 
materials cited in Appendix D.   
19. 
My work on this matter is ongoing, and I may review additional materials or 
conduct further analysis.  I reserve the right to update, refine, or revise my opinions as 
appropriate, including should additional information become available to me. 
V. 
HOURLY RATE 
20. 
I am being compensated for my work on this matter at a rate of $375 per hour.  
The compensation for my work in this matter is not contingent upon the nature of my findings or 
on the outcome of this litigation.   
VI. 
BACKGROUND 
A. 
Bank of America’s Prepaid Call Center Operations  
21. 
In the 2020-2021 timeframe covered by this report, the Prepaid call centers at 
Bank of America served 
 
 
 
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.4  
22. 
From April 2020 to June 2021, the Bank’s customer service operations for its 
prepaid debit cardholders 
 
 
.5 
23. 
.6   
 
 
.7  
 
.8  
 
.9  
 
 
.10  
 
 
.11 
24. 
 
 
4 BANA_EDD_MDL-00643363 at -643387-88. 
5 BANA’s Responses & Objections to Plaintiffs’ Fifth Set of Interrogatories (“5th Rogs”), No. 
34. 
6 Rule 30(b)(6) Depo. of William Golden (“Golden Tr.”) 100:2-9. 
7 Golden Tr. 100:16-21. 
8 See BANA_EDD_MDL-00002286 at -2453. 
9 5th Rogs, No. 34, 14:26-15:4. 
10 See BANA_EDD_MDL-00153666 at -153673-74. 
11 Golden Tr. 32:5-21. 
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10 
 
.12   
25. 
  
 
 
.13  
 
 
 
 
.14   
26. 
Prior to the June 2021 preliminary injunction obtained by the Plaintiffs,15 
 
 
 
 
.16  
 
 
.17   
27. 
 
 
 
12 5th Rogs, No. 34, 15:5-14. 
13 5th Rogs, No. 34, 15:11-22. 
14 See 5th Rogs, No. 34; BANA_EDD_MDL-00153666 at -153673-74; Rule 30(b)(6) Depo. of 
Shane Daniels (“Daniels Tr.”) 84:23-86:24. 
15 Preliminary Injunction (ECF 103), Yick v. Bank of America N.A., 3:21-cv-00376-VC (N.D. 
Cal. June 2, 2021). 
16 See Golden Tr. 32:5-21, 100:2-21, 168:5-17. 
17 See ibid. 
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.18 
 
 
 
.19  
28. 
 
 
 
.20  
 
 
.    
29. 
After the onset of Covid-19, call volume data reports show that 
 
 
 
 
 
.21 
 
 
18 5th Rogs, No. 34, 15:23-16:12. 
19 Id. 
20 Golden Tr. 57:19-25. 
21 Compare BANA_EDD_MDL-00859710 
 
 
 
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12 
30. 
 
.22  If all callers are routed in the same manner, it logically follows 
that any substantially large sub-group of these callers will experience the same average wait 
times. 
 
 is strong evidence that the 
majority of calls to the Claims call center were also from EDD debit cardholders. Therefore, 
during any given period, EDD 
 
. 
31. 
If there were any variations in service, in my opinion it is likely that EDD 
cardholders would have received worse service than other prepaid cardholders received.  In my 
experience, the only callers who experience meaningfully shorter wait times when contacting a 
severely understaffed center are those who place their calls as soon as the call center begins 
operations each day.  Because the Bank’s Claims call center was open from 8:00 am to 10:00 pm 
Eastern Time Monday through Friday and 9:30 am to 8:00 pm Eastern Time on Saturday, callers 
living in the Pacific Time Zone, which would include most California EDD cardholders, would 
have had to call at 5:00 am Pacific Time to receive the faster early morning service.  While EDD 
 
22 Golden Tr. 57:19-58:4.  It is possible with modern call center phone equipment to route 
different groups of cardholders differently; and had the Bank done so,
 
 
 
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callers were likely under-represented in these early morning hours, they were likely over-
represented at the 7:00 pm Pacific close of the business day. Unfortunately, any callers in queue 
at the end of the day would have been dropped from the queue when the last CSR signed out, 
regardless of how long they had waited, adding to those EDD callers’ frustration.  
32. 
Further, to the extent that the Bank’s call center leaders could influence whether 
EDD cardholders received better or worse service than other prepaid cardholders, 
 
 
23  
B. 
Call Center Industry Performance Standards  
33. 
Call centers operate in what is referred to in the industry as a “real-time” 
environment, meaning that phone calls must be answered shortly after being queued for an agent.  
It is well known in the industry that failure to answer incoming calls shortly after they are 
queued results in high rates of call abandonment (i.e., where the caller hangs up before the call is 
answered), resulting in high levels of customer dissatisfaction. 
34. 
Call centers are heavily reliant on data to manage operational performance.  Since 
the emergence of call centers in the 1970s, phone equipment has been developed to track and log 
nearly every event that occurs during a call center call (e.g., the specific welcome message 
played, the selection made by the caller on the main menu, the time the call was queued to an 
agent, etc.).  By logging these events, a phone system can generate massive amounts of data 
related to both group and individual CSR performance, which is reviewed by managers.   
35. 
There are standard call center performance metrics that nearly every call center 
measures. These include the incoming volume of calls (also referred to as offered calls), the 
 
23 BANA_EDD_MDL-00118438. 
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number of calls handled (also referred to as answered calls), the number and percentage of 
abandoned calls, and the speed with which calls are answered.  The Bank collected and tracked 
this data for its Prepaid call centers.24 
36. 
Two of the most common metrics for evaluating call center performance are 
average speed of answer (ASA) and abandonment rate.25  The ASA is the arithmetic mean of the 
number of seconds each answered call remained on hold before the caller was connected to a live 
CSR, while the abandonment rate is the percentage of callers who hang up or are otherwise 
disconnected after their call enters the hold queue but before their call is answered.26  These are 
standard measurements that call center vendors regularly report to their clients, often multiple 
times per day.  
 
.27 
37. 
The vast majority of call center vendors operate under contracts that specify 
performance targets for both ASA and abandonment rate. It is standard practice to reference 
average performance across call centers to negotiate and set vendor performance targets. 
 
 
,28 which 
is a common industry target for abandonment.  
 
 
24 See, e.g., BANA_EDD_MDL-00719115; BANA_EDD_MDL-00719116. 
25 See Susan Hash, Metrics Roundup, CONTACT CENTER PIPELINE (Oct. 2019), 
https://www.contactcenterpipeline.com/Article/contact-center-metrics-roundup (identifying 
abandonment rate and response time as among the most-used call center performance metrics). 
26 Id.; see also Golden Tr. 32:22-33:11. 
27 See, e.g., BANA_EDD_MDL-00719115; BANA_EDD_MDL-00719116; Golden Tr. 60:24-
61:14. 
28 See e.g., BANA_EDD_MDL-0090040 at -90046 (
); 
BANA_EDD_MDL-0013086 at -13092 (
); BANA_EDD_MDL-0012875 at -
12882-83 (
 BANA_EDD_MDL-0012792 and BANA_EDD_MDL-
0012800). 
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.29  Because ASA and 
abandonment rate are industry-standard metrics and are among the most frequently reported call 
answer metrics in the Bank’s Prepaid call centers, those metrics are used throughout this report 
to measure call center performance. 
38. 
In my experience, competent call center leaders adopt similar ASA and 
abandoned rate standards and deliver similar ASA and abandoned rate results across all lines of 
business within a company. Doing anything different not only exposes call centers to complaints 
from certain segments of a business, but also encourages callers to “number shop”—that is, 
calling different numbers or choosing the wrong options from a menu just to get into a queue that 
might have a shorter wait.  It is therefore not surprising that EDD did not distinguish between 
Main, Claims, and/or Fraud call centers with respect to the performance benchmarks established 
in its contract with the Bank.30 
 
.31   
39. 
Table 2 below shows the average ASA and abandonment rate over a nine-year 
period for 214 call centers that completed an annual survey conducted by ContactBabel 
(complete results regarding performance metrics of these centers are provided in Appendix E).32  
 
29 See ibid. 
30 BANA’s Response to State of California Electronic Benefits Payments RFP Vol I & II (July 
10, 2015) (“2015 EDD-BANA Contract”) at 187, Req. 226 (“The Contractor shall, limit the 
average wait time to speak to a live CSR to no more than 30 seconds for 70 percent of the calls, 
and no more than two (2) minutes for all calls.”). 
31 See, e.g., BANA_EDD_MDL-0090040 at -90046 (
 
; BANA_EDD_MDL-0013086 at -13092 (
 
); BANA_EDD_MDL-0012875 at -12883 (A
 
).  
32 See 2021 Decision-Makers’ Guide at 46. 
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Table 2:  Average ASA and Abandonment Rates for US Call Centers, 2012-2022 
 
40. 
As shown in Table 2, the average ASA across these 214 call centers in 2020 was 
75 seconds (1.25 minutes).33 The average abandonment rate in 2020 was 6.1%. Although this 
survey reported ASA and abandonment rates as an annual average, the most common interval for 
reporting ASA and abandonment rate performance is a month.  Based on my experience and 
familiarity with performance standards in the industry, an ASA of 1.25 minutes and an 
abandonment rate of 6.1% is consistent with industry standards for acceptable average 
performance levels across a single month, although it is on the higher end of what is considered 
acceptable. 
41. 
In my experience working across the finance, insurance, healthcare, utility, 
telecom, and other industries, there are not substantial differences between the ASA or 
abandonment rate targets set in different industries. In my consulting practice, I have found the 
most aggressive ASA targets set at 10 seconds and the least aggressive at 300 seconds, while the 
most aggressive abandoned targets are 2% with the least aggressive at 10%. These targets 
represent the extremes for individual call centers; industry averages have much less variation 
between the most and least aggressive call center in a given industry. 
 
33 ASA breakdowns by industry are available, though no such breakdowns by industry are 
provided for abandoned rates.  See U.S. Contact Center Verticals:  Finance, ContactBabel 2024, 
showing 2020 ASA for 29 Finance call centers of 145 seconds (page 31); see also U.S. Contact 
Center Verticals:  Outsourcing, ContactBabel 2024, showing 2020 ASA for 28 outsourced call 
centers of 39 seconds (page 32).   
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42. 
Callers develop their expectations regarding how long it should take for a call to 
be answered based on their collective experience with all call centers over time, not just call 
centers in the same industry or providing the same service. For example, when an individual 
evaluates how quickly Comcast picked up their call, they would not compare Comcast only to 
Verizon and AT&T. They would also compare it to AllState, Wells Fargo, Aetna, Wayfair, and 
any other company they may have called recently. Because all call center experiences are used 
by customers in evaluating performance, it is standard practice for call center leaders to consider 
cross-industry benchmarks when setting performance targets.  Accordingly, multi-industry call 
center performance data across call center types, like the data contained in the 2021 U.S. Contact 
Center Decision-Maker’s Guide, is the best source for identifying industry-standard performance 
benchmarks that apply to all call centers, including specialty call centers like the Bank’s Claims 
call center.34  
43. 
Although it is my opinion, for the reasons stated above, that multi-industry call 
center performance data is the best source for identifying ASA and abandonment rate 
benchmarks applicable to the Bank’s Claims call center, performance data specific to the Finance 
and Outsourcing industries is available.35 The average ASA reported in 2020 across the 29 
Finance call centers surveyed by ContactBabel was 145 seconds (2.42 minutes).36 The average 
ASA reported in 2020 across the 28 Outsourcers surveyed by ContactBabel was 39 seconds.37 
 
34 See infra ¶¶28-35. 
35 See 2024 U.S. Contact Center Verticals: Finance, ContactBabel; 2024 U.S. Contact Center 
Verticals: Outsourcing, ContactBabel. 
36 2024 U.S. Contact Center Verticals: Finance, ContactBabel, at 31. 
37 2024 U.S. Contact Center Verticals: Outsourcing, ContactBabel, at 32. 
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C. 
Call Center Industry Operational Standards 
44. 
Call center performance is affected by numerous variables that could drive surges 
and declines in call volume. Call centers have a variety of standard industry practices available to 
help forecast changes in call volume and to prepare for and respond to unanticipated events, and 
to ensure they maintain satisfactory call center performance in the face of changing 
circumstances.  
45. 
For example, call centers often employ skilled analysts that, with the assistance of 
forecasting software, predict call volume and staffing requirements from the monthly level down 
to fifteen-minute time intervals.38  It is not uncommon for call volumes to surge dramatically in 
response to external events or other changed circumstances.39  Whenever decisions are being 
considered that could impact incoming call volumes and/or the average time it takes to process a 
transaction, it is a standard practice to first consult these analysts to determine the likely impact 
of such a decision on staffing requirements.  This advance planning enables the client overseeing 
the call center to make whatever staffing changes are necessary in advance of the implementation 
of the decision so customer wait times are not negatively impacted.40  If a planned company 
decision or policy change is expected to drive an increase in call volume, it is industry-standard 
practice to increase staffing levels as needed to maintain satisfactory call center performance 
 
38 See Lori Bocklund, Support Technology without an Analyst Is Like a Car without a Driver, 
CONTACT CENTER PIPELINE (May 2021), https://www.contactcenterpipeline.com/Article/support-
technology-without-an-analyst-is-like-a-car-without-a-driver (detailing the role of skilled, trained 
analysts in using contact center technologies to optimize staffing). 
39 The 2023 US Contact Decision-Makers’ Guide, 15th Edition, ContactBabel, at 31 
(“Understanding how the business will change some months in advance – perhaps for seasonal 
reasons, or with the launch of a new product – will certainly impact on resourcing, and close 
communication and integration between resource planning and day-to-day WFM is desirable.”). 
40 See id. at 30 (“The modern contact center not only requires the basics (having enough people 
to answer interactions in a reasonable amount of time), but also more sophisticated functionality 
such as the ability to forecast and schedule agents in near-real time and handle virtual contact 
centers, mobile resources and home- working resources.”). 
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19 
levels, and to implement those staffing changes before implementing the company decision that 
is anticipated to drive up call volume. 
46. 
When call center operations are outsourced, it is standard industry practice for the 
client and vendor to document the process for establishing and revising staffing levels.  
 
.41  
 
 
. 
VII. 
STATEMENT OF OPINIONS AND BASIS FOR OPINIONS 
A. 
The Bank Subjected EDD Debit Cardholders Seeking Assistance with 
Unauthorized-Transaction Claims to Long Wait Times that Were 
Unprecedented and Fell Far Below Industry Performance Standards. 
47. 
 
 
.42  
 
 
 
 
 
.43 
 
41 See BANA_EDD_MDL-0090040 at -90040, section 3.4; BANA_EDD_MDL-0013086 at -
13086, section 3.4; BANA_EDD_MDL-00013096 at -13097, section 3.5. 
42 See BANA_EDD_MDL-00172236 at -172242. 
43   The Bank’s designee testified
 
 
 See Golden Tr. 99:11-100:1. See also BANA_EDD_MDL-00172236 at -
172243. 
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20 
48. 
During 2020, 
 
 
. As depicted in Appendix F, 
 
 
.  Later in the year, 
 
 
 
 
.   
49. 
While these numbers represent averages, at these exceptionally poor levels of 
performance, almost every caller would have experienced an extremely long wait for service.  
According to the Bank’s data, 
 
 
, meaning that long wait times 
were universal.  Combined with the Banks’ data showing that 
 
, nearly every 
caller during this time period waited far longer than what is considered acceptable by industry 
standards.44 
50. 
In light of the data provided by the Bank, 
 
 
. The Bank’s records show that, 
 
 
 
44 See BANA_EDD_MDL-00719115. 
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21 
.   Because calls from EDD debit 
cardholders 
 
 
 
51. 
Comparing performance in the Bank’s Claims call center to the performance of 
call centers generally during the 2020 pandemic year demonstrates how poor the Bank’s results 
were, in relative as well as absolute terms.  Table 3 and Table 4 below show how the Bank’s 
Claims call center compared to the 214 call centers that participated in the 2021 ContactBabel 
survey, based on the data from Appendix F (Claims Call Center Data 2020-2021).  Figure 1 and 
Figure 2 below show the Bank’s Claims call center performance metrics over the entire 2020 
year.  
52. 
The average ASA in 2020 among the 214 call centers surveyed by ContactBabel 
was 75 seconds (1.25 minutes), which the data shows is on the high end of what is typically 
considered an acceptable ASA and likely reflects the pandemic circumstances in the second 
quarter of 2020. 
 
.45 This means that cardholders who called the Bank and 
were able to reach a Claims CSR waited 
 
than did callers to the surveyed call centers.  The 2020 performance of the Bank’s Claims call 
center is by far the worst I have seen across a full year in my 41 years in the industry, 
 
 
 
45 The weighted average 
 
 based on data from BANA_EDD_MDL-00719115. 
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already far outside the industry norm, 
 
 
 
. 
Normal day-to-day or week-to-week variations in performance are inadequate to explain call 
center performance that was this bad for that long. 
Table 3: Average Speed to Answer Comparison, September 13 – November 21, 2020
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23 
Figure 1:  
 
53. 
 
. Abandonment rate is another 
commonly reported metric reflecting call center performance. In 
 
,46 and in 2020 the average 
abandonment rate among the 214 surveyed companies was 6.1%.  In my experience these figures 
are consistent with—albeit on the high end of—industry norms.  
 
  In short, 
 
 
 than at companies participating in the 2020 
ContactBabel survey.  While this is already far outside the industry norm, 
 
 
 
46 See BANA_EDD_MDL-0090040 at -90046. 
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24 
 
 
. These are extraordinarily high levels of abandonment that any competent call center 
operator would be expected to undertake enormous efforts to avoid.   
54. 
Another performance metric, related to abandonment rate, is average abandon 
time (also known as “abandon delay”). This metric measures how long on average a caller who 
ultimately abandons the call waited on hold before hanging up.  
 
.47  
 
 
.48    
55. 
 
 
 
.  ASA data 
only accounts for the time spent waiting on a call that ultimately connects and does not include 
this time that callers wait before abandoned or disconnected calls. As such, ASA understates the 
total wait time 
 
. 
 
47 Because call centers strive to keep abandonment rate as low as possible and generally under 
5%, average abandon time is a less frequently reported metric because, in most call centers, it 
reflects the experience of a very small number of callers. As such, the ContactBabel survey did 
not include this metric. 
48 Average abandon time weighted on abandoned calls as provided in BANA_EDD_MDL-
00719115. 
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Table 4: 
 
Figure 2: 
  
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56. 
 
 
 
 
. 
57. 
To summarize, 
 
 
 
 
 
 
 at surveyed companies.   With no service options available 
other than the Claims call center, these callers had no recourse other than calling and waiting. 
B. 
The Bank’s Practice of Understaffing Its Claims Call Center in the Face of 
Foreseeable Call Volume Surges Was Inconsistent with Industry Standard 
Practices. 
58. 
Understaffing was the root cause of the poor service in the Claims call center 
from April 26, 2020 to June 13, 2020 and again from September 13, 2020 to November 21, 2020, 
and the Bank’s documents and testimony 
 
.  Long wait times and high abandonment rates are 
entirely avoidable, even in the face of call volume surges, if a call center properly forecasts call 
volume and staffs its call centers accordingly.  
59. 
While the COVID-19 pandemic clearly caused a surge in call volume for the 
Bank, the impact  in the Spring of 2020 would have been minimal had staffing action 
commenced as soon as forecasts were updated.  Despite this delayed reaction, staffing finally 
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caught up with call demand in July of 2020, making the pandemic irrelevant to the Bank’s poor 
performance in Fall 2020. The 2020 ContactBabel survey and my experience with clients 
grappling with similar pandemic-related issues show that many call centers were able to provide 
adequate services to their customers within weeks of the onset of the pandemic.  
60. 
Wait times are a function of call volume, average handle time (the time required 
to complete a call, including post-call documentation), and call center staffing. When call 
volume increases are projected, call center staffing needs to be correspondingly increased in 
order to maintain adequate levels of service. 
 
 
.49  
Such a practice is well outside of industry-standard operating procedures, certain to result in sub-
standard performance, and not adopted by competent call center operators.   
61. 
 
 
.50  
 
 
.51  
 
 
. 
62. 
The Bank’s designated witnesses have testified that the Bank’s standard practice 
was to 
 
 
49 See Golden Tr. 66:8-68:6. 
50 See Golden Tr. 69:5-20. 
51 Id.  
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.52  
 
 
 
 
 
 
 
. 
63. 
The Bank’s admitted practice of 
 
 is well outside of standard operating procedures, because staffing lags of weeks at a 
time are known to be the cause of performance results that are widely viewed in the industry as 
unacceptable.  There is, in fact, an entire field of specialization within the call center industry 
known as Workforce Management (WFM) dedicated to forecasting volumes and calculating 
staffing needs in advance.53  WFM teams employ skilled analysts using forecasting and 
scheduling software for the purpose of being appropriately staffed at all times.54  Documents 
produced by the Bank in this litigation show 
 
 
 
52 Golden Tr. 66:8-68:6. 
53 See 2021 Decision-Makers’ Guide at 47-49 (describing the importance of forecasting to call 
center operations and the many inputs that can contribute to more accurate staffing forecasts).  
54 Of the call centers surveyed by ContactBabel, 90 percent of large call centers (more than 200 
agents) and 67 percent of Finance call centers used specialized workforce management 
forecasting and scheduling software. See 2021 Decision-Makers’ Guide at 54-55. As virtually all 
call centers at this size engage in forecasting, other call centers are likely using Excel or other in-
house tools.  
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29 
 
.    
64. 
As early as April 2020, 
 
 
.55 
 
 
.56  
 
 
 
.   
65. 
The method used by the Bank’s 
 
.  Most call centers depend on a multitude of factors (e.g., 
business growth rate, seasonality factors, event timing, etc.) to project future volume.  This 
process is not only complex, but it requires that all of these factors align with expectations to 
produce a reliable forecast.  Here, by contrast, the Bank was able 
 
 
 
.57  The R squared value is a measure of how well one data set (
 
) explains variance in another (
). 
The R squared value can be used to compare any two sets of data, with readings ranging from a 
 
55 See, e.g., BANA_EDD_MDL-0060339, tab labeled “
,” 
 
 labeled “
.” 
56 See, e.g., BANA_EDD_MDL-0060339, tab labeled “
,” 
. 
57 See, e.g., BANA_EDD_MDL-0060339, tab labeled “
,”
 
.” 
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minimum of 0 (meaning no correlation) to a maximum of 1.0 (perfectly correlated).  Over my 
career, I have run these types of calculations for dozens of call centers, and I have never 
encountered this high of a correlation from a single factor.  For call center forecasting, an R2 of 
.7 will deliver monthly forecasts with error rates typically below 10%, meeting the target 
accuracy rate used most often in the industry.  
 
 
 
 
 
 
.  
66. 
 
 
 
 
.58  To ignore a forecast 
with proven reliability is completely outside of normal operating procedure for a call center. 
67. 
 
 
 
.59  
 
 
58 See supra ¶¶61-63. 
59 A Full Time Equivalent (FTE) represents 40 hours of CSR logged in time for the week.  For 
example, two CSRs that each log 20 hours would be considered one FTE. 
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. 
68. 
As displayed in Table 5 and Figure 3 below, 
 
 
 
 
 
 
 
.   
69. 
 
 
 
 
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32 
 
  
 
 
. 
70. 
Call center industry CSR turnover actually dropped in 2020 compared to the 
previous year,60 making it even more clear that neither normal nor pandemic-related turnover can 
explain what happened from July to September 2020 in the Claims call center. As shown in 
Table 5 below, at a time when the call center industry was experiencing a staffing attrition rate of 
30% over 52 weeks (0.58% per week, on average),61 the Bank 
 
 
 
 
 
.  The huge gap between the Bank’s reduction in staff over this time period 
compared to typical call center attrition rates makes it clear that the reduction was not due to 
typical attrition.    
 
60 See 2021 Decision-Makers’ Guide, figure 190 at page 390. 
61 Id. 
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Table 5: 
 
71. 
 
 
 
. 62  
 
 
.63 
 
. 
72. 
 
 
 
 
62 See Golden Tr. 60:14-61:14 (
 
”). 
63 Id.; see also BANA_EDD_MDL-0090040 at -90046 
 
; BANA_EDD_MDL-
0012875 at -12883 (
). 
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.64   
73. 
The Bank’s implementation of the 
 
 
 
 
 
 
 
.     
74. 
In other words, a 
 
 
 
 
 
 
 
64 Golden Tr. 75:14-76:2; 124:11-125:1. 
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.”65  The Bank’s witness acknowledged 
 
.66 
75. 
Deliberately subjecting customers to intentionally prolonged wait times is 
unheard of in the call center industry.  In my 41 years in the industry, I have never heard of a call 
center deliberately taking such actions to increase wait times and impede the provision of 
customer service. This is especially true where the affected customer population is seeking a 
critical service, such as (in this case) public benefits recipients seeking customer service to help 
them recover money that has been stolen from their account. 
76. 
I have consulted for approximately a dozen clients that answer calls that I would 
consider to be of a similar level of importance to the calls directed to the Bank’s Claims call 
center, ranging from suicide hotlines (at the highest level of importance) to other critical 
customer service roles such as physical/mental health care advisors and utility companies. In my 
experience, such companies and service providers recognize they have particularly vulnerable 
customers and take care to balance their legitimate business concerns against the significant 
harms that could result from inadequate customer service. During my career, I have never 
heard—and cannot imagine hearing—of any supervisor or executive in one of those call centers 
suggesting that 
 
.   
 
65 See, e.g., BANA_EDD_MDL-00118438 at -118438 (
 
; BANA_EDD_MDL-
00106092 at -106093-94 
 
 
 
).  
66 Golden Tr. 85:25-87:2. 
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Table 6:  
: 
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Figure 3:  
: 
77. 
As Table 6 and Figure 3 demonstrate, 
 
 
 
 
 
 
 
 
. 
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78. 
Not only did the Bank cause these exceptionally long wait times due to its 
deliberate failure to maintain proper staffing levels, but the Bank also failed to deploy commonly 
available technologies that would have at least mitigated some of the harm to cardholders by not 
forcing them to waste their time waiting on hold indefinitely with no assurance of when, if ever, 
their calls would be answered. 
79. 
For decades, telephone systems (
 
) have offered an Estimated Wait Time (EWT) application that 
would provide callers in queue an estimate of how long before their call would be answered.  
This application allows callers to make better decisions concerning whether to wait on hold, and 
if they choose to wait, they might at least be able to get some tasks done before the time the call 
should be answered.  Despite the availability of EWT technology, 
 
 
 
.  
80. 
In addition, for several decades, many phone systems have also offered virtual 
queuing.  This application provides each caller the EWT, and then offers to hold a caller’s place 
in queue and to call back when it is the caller’s turn, thereby allowing the caller to attend to other 
matters until the system initiates the scheduled callback.67  This feature is now native to many 
phone systems, but even those systems without it can add it on from a third-party vendor, making 
 
67 See Jason Barro, Rahul Sethi, Alison Leibovitz, and Blair Markell, “Please Don’t Hold”: A 
Better Banking Call Center Experience, BAIN & CO.: INSIGHTS (Feb. 16, 2023), 
https://www.bain.com/insights/please-dont-hold-better-banking-call-center-experience-snap-
chart/ (“[B]anks can boost satisfaction during disputes by giving customers the option to receive 
a callback rather than holding. Customers who chose a call back gave a Net Promoter Score of 
28 points, compared with a Net Promoter Score of –3 from those who didn’t have a choice.”). 
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the feature available to any call center willing to purchase it.  
 
 
.68  
81. 
It is difficult to overstate just how poor the Bank’s Claims call center performance 
was in 2020 compared to typical call center performance, even after factoring in the pandemic.  
With no other service channel available to address Claims calls, and no other option but to 
remain on hold waiting for a CSR with no idea how long the remaining wait time would be, 
callers seeking assistance with their unauthorized-transaction claims were forced to endure the 
exceptionally long waits experienced in 2020.  Despite speed-to-answer metrics being cited as 
one of the three most important metrics by 36% of call centers,69 the Bank took the opposite 
approach and imposed long wait times on its EDD debit cardholders, 
 
 
70  The Bank’s documents reflect that a 
 
 
.71  In my 41 years of experience in the industry, I have never seen a call center managed 
with such total disregard for the customer’s experience and need for service.   
 
68 See Golden  97:5-98:19. 
69 See 2021 Decision-Makers’ Guide at 45. 
70 See  BANA_EDD_MDL-00118438 at -118438 
 
 
. 
71 See BANA_EDD_MDL-00106092 at -106094. To the extent that the Bank’s primary purpose 
in understaffing the Claims call center was to deter fraudulent claims, it is not clear from the 
Bank’s documents or testimony 
 
 
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C. 
The Substandard Performance Levels in the Bank’s Claims Call Center from 
September 13, 2020 through November 21, 2020 Reflected the Bank’s 
Broader Systemic Failure to Operate Its Prepaid Call Centers Consistent 
with Industry Standards. 
82. 
Although the Customer Service Class is limited to EDD debit cardholders who 
called the Bank and were routed through the Main call center to the Claims call center between 
September 13, 2020 and November 21, 2020, the substandard performance level in the Claims 
call center during this period was not an isolated occurrence for the Bank. Rather, it is 
representative of systemic failures by the Bank to provide call-center customer service to its 
EDD debit cardholders that was consistent with industry standards. 
 
 
 
. In my 
experience, the hold times to which the Bank subjected EDD debit cardholders are virtually 
unprecedented in the call center industry. The Bank also engaged in a variety of other reckless 
and irresponsible call center practices that are far outside the norms of industry standard 
practices.  
Failure to Plan for Disasters Leading to Excessive Disconnected Calls 
83. 
The Bank failed to engage in reasonable planning for its Prepaid call centers for 
potential staffing-intensive events like the pandemic.  As a result of those failures, during the 
early months of the pandemic, callers to the Bank’s Prepaid call centers were more likely to be 
disconnected or dropped than to actually get through to a CSR in the Main call center (which, as 
previously discussed, was a necessary step before a caller could speak with a CSR in the Bank’s 
Claims or Fraud call centers).  
84. 
The Bank was 
 
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.72  That provisioning 
involved determining how many phone lines are needed and then obtaining them from the phone 
company.  Calculating the correct number of these lines requires, among other factors, a forecast 
of the busiest phone hour expected in the future.  
 
 
 
.  By mid-April, 
 
 
 
 
 
.73  Another report from the Bank showed that 
 
 
.74  It is not clear how long the Bank continued to generate  
 
, but this significant gap 
 
 generated disastrously high levels of disconnects.   
85. 
In late March/early April 2020, 
 
.75  
, though, even 
in non- pandemic circumstances, which may be why the 
 
76  The Bank’s 
 
 
72 See e.g., BANA_EDD_MDL-00382181 at -382184, sections 5.5.1 to 5.5.4. 
73 See BANA_EDD_MDL-00352985 at -352986. 
74 See BANA_EDD_MDL-00288637 at -288640. 
75 See BANA_EDD_MDL-00288637 at -288641, item 3. 
76 Ibid. 
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42 
 
. 
86. 
Facing a significant number of disconnects and an unknown timeframe to 
provision additional phone lines, 
 
.77  
 
 
 
 
.78  A 
 
.79  
 
 
 
 
 
.80  Eventually, 
 
 
.81   
 
77 Ibid at -288640, item 1. 
78 See BANA_EDD_MDL-00103075 at -103078. A “
”
 
. 
79 Ibid. 
80 See BANA_EDD_MDL-00288637 at -288637. 
81 See BANA_EDD_MDL-00103075. 
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87. 
In place of the 
 
82 which in the industry is more commonly referred to as call diverting or 
call blocking, and which many callers perceive as being hung up on.  
,83 
 
 
 
 
 
. 
88. 
By mid-April 2020, 
 
 
 
 
.  
While no call center wants to invoke a disconnect message, prudent disaster recovery planning 
measures include the potential use of such a message.  Knowing this, AT&T has for decades 
provided call center operators an option to set up such a message in advance, in the AT&T 
network cloud, to be deployed immediately and with minimal effort through an online portal or a 
call to AT&T.  This capability is well known to industry professionals and is utilized by many 
call center operators using the AT&T network.  In fact, 
 
.84  
 
 
82 See ibid at -103076. 
83 See BANA_EDD_MDL-00424944 at -424944. 
84 See, e.g., BANA_EDD_MDL-00718992 at -718999. 
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44 
 
.85  
 
 
 
 
 
 
.86 
89. 
 despite the Bank’s contractual promise, made 
when securing the EDD’s business, that “no call” would be “transferred to voicemail or 
automatically disconnected from the queue.”87  A genuine commitment to an objective of “no 
disconnects” would have required the Bank to analyze potential volume surge scenarios, to 
invest in enough phone lines to handle the increased traffic projected in these scenarios, and to 
have back-up plans ready to implement quickly if call volume were to expand beyond what was 
projected.  
 
.   
90. 
This lack of 
 
 
 
 
85 See, e.g., BANA_EDD_MDL-00021102 at -21114 and BANA_EDD_MDL-00718992 at -
718999. 
86 See BANA_EDD_MDL-00424944  at -424947. 
87 See 2015 EDD-BANA Contract at 185, requirement 203. 
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88 suggests a lack of concern for those the Bank was contracted to serve.  This lack of 
attention to disaster planning resulted in a slow response to the surge caused by the pandemic, 
and EDD cardholders paid the price. 
Understaffing Leading to Excessive Wait Times in the Fraud Call Center 
91. 
Although the Customer Service Class is limited to EDD debit cardholders who 
called the Bank and were routed to the Claims call center between September 13, 2020 and 
November 21, 2020, that is not the only call center and not the only time period when the Bank’s 
Prepaid debit cardholders were subjected to wait times that far exceeded industry norms.  
92. 
The Bank’s other specialty call center for Prepaid cards, the Fraud call center, 
similarly performed at substandard levels rarely seen in the call center industry. 
93. 
 
 
 
 
 
 
 
 
. 
94. 
 
 
 
 
88 See Golden Tr. 334:22-335:23. 
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46 
 
 
 
 
 
.  
Figure 4:  
 
95. 
 
 
 
 
 
 
 
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47 
 
.  
Figure 5:  
 
96. 
The principal cause of the 
 
 
 
 
 
 
 
 
 
 
 
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48 
 
.   
Figure 6:  
: 
97. 
Just as in the Claims call center, 
 
 
 
 
 
 
 
. 
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49 
98. 
 
 
,89 
 
90 
.   
99. 
 Comparing 2020 performance in the 
 call centers 
to the performance of other call centers during this pandemic year helps quantify how truly poor 
the Bank’s results were.  Callers to 
 
 
 
 
 
 is by far the worst I have seen across a full year in my 41 years in the industry.  
Lax Security Protocols in the Transition to Work-from-Home 
100. 
Call centers often have access to and utilize personal and confidential customer 
information to complete transactions.  Phone representatives see this information on screen and 
hear it in discussions with callers.  Call center systems use this information across various 
applications, such as customer databases, IVR systems, and call recording equipment.  
Maintaining security over this information is vitally important, and many industries have 
documented standards to be followed by call centers to provide the appropriate level of security.  
In the financial services industry, the Payment Card Industry Data Security Standard (PCI DSS) 
 
89 See Appendix G, sum of abandoned calls for the time period divided by offered calls. 
90 See Appendix G, calculated from data in column labeled ASA, weighted by calls handled. 
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defines security controls to protect payment card data throughout the transaction lifecycle,91 and 
the Bank was required by contract with the CA EDD to “be certified as PCI DSS compliant.”92  
Version 3.2.1 of the PCI DSS, updated in May of 2018, was the version in place when the 
pandemic began in March 2020.93  Section 12.7 of the standards requires the screening of 
personnel, and lists examples of the types of screening, including employment history, credit 
history and reference checks.  Contrary to the PCI DSS, 
 
. 
101. 
With the onset of the pandemic, 
 
 
.94  Given 
the increased risk of security breaches, it should have been all the more important for the Bank to 
perform other checks that remained available, such as employment and credit checks. But the 
 
 
 
. 
102. 
 
.95  The 
 
91 See Information Supplement:  Protecting Telephone Based Payment Card Data, Version 3.0, 
Protecting Telephone Based Payments Special Interest Group of the PCI Security Standards 
Council at 1, 
https://listings.pcisecuritystandards.org/documents/Protecting_Telephone_Based_Payment_Card
_Data_v3-0_nov_2018.pdf. 
92 See 2015 EDD-BANA Contract at 250, requirement 394. 
93 Available at www.pcisecuritystandards.org.  
94 See BANA_EDD_MDL-00517105 at -517115. 
95 See id. 
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51 
Bank 
 
 
,96 
 
.   
103. 
 At the same time the Bank 
 
 
 
 
 
. 
104. 
  Those call centers already using or testing WFH were able to identify concerns 
with maintaining the confidentiality of information.  As a result, most call centers utilizing WFH 
instituted rules such as i) requiring that calls be handled from a hard walled office with a door 
that could be closed for privacy, ii) restricting cameras from the WFH work space, and iii) 
ensuring staff followed a “clean desk” work policy where no paper or writing instruments were 
allowed in the work area. Policies such as these have become standard WFH requirements in the 
call center industry and are routinely documented in vendor contracts.  
105. 
The Bank 
 
 
 
 
 
96 See Golden Tr. 289:10-290:7. 
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52 
.97  
 
 
.98  Keeping other 
 
.99  Despite claims 
that 
 
 
.100  In my experience, based on reviewing well over 50 call center SOWs, 
policies such as these are routinely included in vendor contracts.  In my opinion, it is 
irresponsible for a company handling sensitive customer information to rely on call center 
vendors to follow critical security policies without contractually defining or regularly auditing 
the requirements.  
D. 
The Bank’s Understaffing Choice Harmed Cardholders While Saving the 
Bank Money. 
106. 
The Bank’s EDD debit cardholders paid the price of the excessive wait 
times caused by the Banks’s severe understaffing in the Claims call center from September 13, 
2020 through November 21, 2020.  The cardholders calling the Claims call center who are 
members of Plaintiffs’ Customer Service class and assert an injury from the Bank’s conduct 
 
 
 
 
 
 
97 See e.g., BANA_EDD_MDL-00013111; BANA_EDD_MDL-00012816; BANA_EDD_MDL-
0090040; BANA_EDD_MDL-00382181. 
98 See Golden Tr. 286:19-287:19. 
99 See Golden Tr. 285:3-289:2. 
100 See Golden Tr. 285:3-289:9. 
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53 
.101  
 
,102 
 
 
.   
107. 
Given no 
 
 
. As explained above, callers that managed to 
reach a Claims CSR during the applicable class period waited, on average, 
 longer 
per call than they would have had the Bank provided industry standard wait times.  The total 
number of excess hours that Customer Service class members waited on hold for the Bank’s 
Claims call center can thus be calculated from data in the Bank’s records by multiplying 
 
 times the total number of calls from Customer Service class members to the Bank’s 
Claims call center during the applicable time period. This figure can then be multiplied by the 
value of class members’ time, which I understand is a topic on which Plaintiffs’ labor economist 
expert David Levine will offer testimony.   
108. 
The Bank’s poor call center performance also allowed the Bank to pay far 
less for call center charges than it would have had it provided industry-standard service.  
 
.103  Under this 
 
101 See, e.g., BANA_EDD_MDL-00205620 at -205620 (
 
 
). 
102 See, e.g., BANA_EDD_MDL-00012816 at -12841-42, sections 21.2 and 21.7, and 
BANA_EDD_MDL-00013111 at -13135-36, sections 21.2 and 21.7.  
103 See e,g, BANA_EDD_MDL-00090040 at -90043 and BANA_EDD_MDL-00013086 at -
13090 defining a Paid Production Hour as 
 
 
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54 
structure, the largest block of payable time is workload (the time agents spend processing calls).  
The second largest block of payable time, for call centers providing industry-standard service, is 
agent idle time (time CSRs are available and waiting for the next call to arrive).  The 
unprecedently poor performance by the Claims call center from September 13, 2020 to 
November 21, 2020 allowed the Bank to avoid paying for a significant amount of both workload 
and agent idle time.   
109. 
Calculating the total cost the Bank avoided requires first estimating the 
amount of distinct caller demand in the Claims call center from September 13, 2020 until 
November 21, 2020.  Distinct caller demand calculations are done to account for the fact that 
some percent of abandoned callers will call back later in another attempt to get service, and thus 
one distinct caller shows up in the data as two offered calls.104  Normally, when a call center is 
providing service at or close to industry standard, the number of abandoned calls are small 
enough that attempting to isolate and account for these callbacks is deemed unnecessary because 
the added effort will have little impact on outcomes (i.e., distinct caller demand will be within a 
few percentage points of offered call totals).  With the exceptional levels of abandonment seen at 
the Claims call center, though, an estimate of distinct caller demand will generate a more 
accurate assessment of the productive hour charges avoided by the Bank. 
110. 
Distinct caller demand is calculated by adding the percentage of 
abandoned calls that did not call back to the total number of answered calls.  The portion of 
abandoned calls that represent distinct callers, rather than redials by callers that were ultimately 
 
104 For some call center calculations, like the excess wait time calculations detailed in the prior 
paragraphs, there is no need to determine distinct caller demand (an hour of excess wait is one 
hour of wasted time whether spread among one or two distinct callers).  Other calculations, such 
as comparison of payable hours charges at low abandonment vs. high abandonment, will be more 
accurate using distinct caller demand estimates rather than using offered or answered calls.  
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handled, can be calculated most accurately on a classwide basis by using the Bank’s own call 
record data during the time period in question to isolate individual ANI phone numbers that 
called once, abandoned the call, and then never called back (i.e. the full call record for the time 
period shows only one record for a given ANI, and that record shows the call abandoned).  
Unfortunately, the Bank 
, so it is not possible to 
determine distinct caller numbers from Bank data.  In lieu of this, Table 9 below 
 
 using call record data supplied by consulting clients of my firm, 
Service Agility.  Appendix H shows how this data was utilized to determine the recall rates in 
Table 9. 
Table 9:  
 
 
111. 
Multiplying true caller demand by average handle time provides the total 
workload hours that would have been charged to service these callers with industry-standard 
performance levels.  As shown in Table 10 below, those hours are multiplied by the hourly rate 
Notes:
1 - Data Source:  BANA_EDD_MDL-00719115
2 -
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56 
paid to call center vendors to obtain a figure showing the Bank’s savings from callers who 
abandoned their calls and never succeeded in reaching a CSR. 
Table 10: Avoidance of Paid Workload Hours 
 
112. 
Second, cumulative with the avoidance of cost associated with workload 
hours, the Bank also avoided cost associated with agent idle time. This avoided cost can be 
calculated on a class-wide basis, using the industry standard measure of the Erlang-C formula. 
The Erlang-C formula is used in a variety of circumstances where demand or traffic varies over 
time (for example, restrooms in a stadium), but is most commonly employed by call centers to 
estimate staffing needs.  The necessary inputs for performing this calculation are
 
.105  The requirement to 
build in (and ultimately pay for) agent idle time in order to achieve contracted-for and industry-
standard ASA is a mathematical fact in call centers. Nonetheless, performance in the 
 
 
105 See Rahul Awati, Erlang C, TechTarget (accessed Aug. 22, 2024), 
https://www.techtarget.com/searchunifiedcommunications/definition/Erlang-C (“Erlang C is a 
traffic modeling formula, primarily used in call center scheduling to calculate delays and to 
predict waiting times for callers. . . . It is widely considered a standard calculation for managing 
call centers.”). 
Notes:
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57 
 
 
.106  This lack of idle time is highly unusual and directly caused by the 
understaffing that generated such poor performance.   
113. 
The amount of necessary idle time can be calculated by applying the 
industry-standard Erlang-C formula to half-hourly distinct call volume and handle time 
distributions (together, call intensity) for an average day for each week.  Based on call intensity, 
the Erlang-C formula can be used to calculate the number of staff required to deliver an industry-
standard ASA for the interval. The difference between the total staffed hours and the workload 
(again, the time agents spend processing calls) is the agent idle time.  Appendix I provides an 
example of this methodology for the week of September 13, 2020, while Table 11 below 
provides the calculations showing the week-to-week hours and charges the Bank avoided paying 
by keeping their Claims call center severely understaffed. 
Table 11:  Avoidance of Paid CSR Idle Time Hours 
 
106 See BANA_EDD_MDL-00719115 (
 
 
. 
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Notes:
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VIII. CONCLUSION  
114. 
The Bank’s decision to allow staffing levels far below what was required in its 
Claims call center, while implementing its Claim Fraud Filter policy that foreseeably led to a 
surge in calls to its Claims call center, was inconsistent with industry standards and caused 
substandard performance in its Claims call center. The performance of the Bank’s Claims call 
center from September 13, 2020 through November 21, 2020 was so substandard that it 
subjected cardholders to wait times rarely, if ever, seen in the call center industry. 
 
Executed on March 4, 2025, at Harleysville, Pennsylvania. 
 
 
 
 
 
 
 
_______________________________ 
 
 
 
 
 
 
 
Jay Minnucci 
 
 
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Appendix A:  Curriculum Vitae 
Jay Minnucci 
 
 
President, Service Agility, jaym@serviceagility.com 
(267) 733-8778 
 
 
 
 
 
 
 
 
Summary 
 
Jay Minnucci is the President and Founder of Service Agility, a consulting and training 
company dedicated to improving customer service and contact center operations.  In this role, 
he provides strategic and tactical guidance across all industries for enterprises that seek to 
optimize customer interactions.  His client list ranges from small start-up operations to large 
Fortune 500 corporations.  He is well-known throughout the industry as an accomplished 
consultant, trainer, speaker and author on all subjects relevant to contact center best practices. 
 
Experience 
 
April 2008 – Present:  Owner of Service Agility, Inc. 
 
March 2000 – April 2008: Vice President of Consulting, International Customer 
Management Institute (ICMI).  Built the consulting division from the ground up, leading a 
staff of 12.  Furthered the ICMI brand through various speaking, training and writing 
engagements.  
 
October 1983 – March 2000:  Various management and executive positions in the contact 
center supporting the AARP Group Health Insurance Program.  This operation was run by 
Prudential Insurance from 1983 to 1996, when The Hartford took over. 
 
Education 
 
BBA from Temple University, May 1984, graduated Magna Cum Laude 
 
Consulting Experience 
 
Jay leads up to 20 consulting/training assignments a year in the United States and abroad, 
including past projects in China, Australia, England, Canada, The Czech Republic, Portugal, 
Malaysia, Singapore and Dubai.  Some of the clients that Jay has worked with include: 
 
Federal Reserve Bank 
Gartner Group 
Duke Energy 
Hyatt 
Discover Financial 
Kaiser Permanente 
BMW 
Charles Schwab 
Canon 
Michigan BCBS 
Wells Fargo 
American Diabetes Assoc 
Government of Australia Allstate 
UBS 
Vodafone 
Thrift Savings Plan 
United Healthcare 
Speaking Engagements 
 
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Past speaking engagements include the following: 
 
 Annual Call Center Exhibit (ACCE) 2003 – 2008 
 China Call Center Conference 2007 
 Middle East Contact Center Forum 2013 
 Help Desk Institute 2007, 2009, and 2011 
 Contact Center Association Conference 2011 – 2013 
 Contact Centre Association of Singapore Symposium – 2013 
 J.D. Power Customer Service Conference 2013 and 2015 
 NM Credit Union Association 2015, 2018, 2019 and 2022 
 Avail Technologies Customer-Centric Services Summit, 2024 
 
Training 
 
Jay has provided training in both public and private venues on topics that include the 
following:   
 
 Crafting a Contact Center Strategy 
 Contact Center Management Principles 
 Enhancing Efficiency 
 Workforce Management Principles 
 Metrics and Objectives 
 Managing a Small Contact Center 
 
Writing 
 
Jay is a member of the editorial board for the industry journal Contact Center Pipeline.  
From 2010 to 2018, he wrote a popular monthly column for the Pipeline.  He has been 
published in many other journals as well, including Call Center Magazine, Customer 
Management Insight, Foresight, Contact center Management Review, Business 
Communications Review, and the International Journal of Contact centers.  He has written 
numerous articles for Service Agility's and ICMI’s websites, and was a contributor to the 
latest edition of Contact Center Management on Fast Forward, by Brad Cleveland. 
 
 
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Appendix B:  Prior Publications (March 2014 to Present) 
All of the articles listed below were published in Contact Center Pipeline, an industry 
publication: 
“A Second Chance”, March 2024 
“Revisiting Schedule Adherence,” July 2019 
“Taking a Deep Dive into FCR,” November 2018 
“Survey Erosion,” July 2018 
“Re-Gifting for 2018,” December 2017 
“Consultative WFM,” November 2017 
“What the Frontline Supervisor Survey Says,” October 2017 
“The SBR Balancing Act,” September 2017 
“Technology Worth Following,” August 2017 
“Cost Efficient 2018,” July 2017 
“Selling and Promoting the Contact Center,” June 2017 
“Nailing Priorities,” May 2017 
“Optimizing the Exceptions,” April 2017 
“Valuing the Agent,” March 2017 
“Balancing the Contact Center Brain,” February 2017 
“Looking Ahead to 2020,” January 2017 
“Re-Gifting for 2017,” December 2016 
“A Culture of Trust,” November 2016 
“The Moment of Truth,” October 2016 
“First Step: Showing Up,” September 2016 
“Getting Closer to the Customer,” August 2016 
“The Work Environment 2016,” July 2016 
“Five Ways to Improve Engagement,” June 2016 
“When Dysfunction Strikes,” May 2016 
“Conversing or Transacting?,” April 2016 
“The Profession of Contact Center Management,” March 2016 
“The Phone System App,” February 2016 
“Accountability in the Center,” January 2016 
“Re-Gifting for 2016,” December 2015 
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“Welcome To Our Contact Center!,” November 2015 
“Coaching: The Radical Next Steps,” October 2015 
“Speech Rec…or…Speech Wreck?,” September 2015 
“Four Ways to Expand the Value of Quality Monitoring,” August 2015 
“Outbound Calling in Today's Contact Center,” July 2015 
“Prepping for Next Year's Budget,” June 2015 
“Setting Service Level Objectives,” May 2015 
“Top Performer Modeling,” April 2015 
“Some Love for ANI,” March 2015 
“To NPS or Not NPS,” February 2015 
“The (Near) Death of Workforce Management,” January 2015 
“Re-Gifting for 2015,” December 2014 
“Organizational Structure: Survey Highlights,” November 2014 
“The Here and Now,” October 2014 
“Agent Attrition: Time for a Change,” September 2014 
“Long Term Cost Management,” August 2014 
“Video in the Contact Center,” July 2014 
“The Outsourcing Decision,” June 2014 
“Speech Analytics and Quality Monitoring,” May 2014 
“Proactive Do's and Don'ts,” April 2014 
“Training Frontline Leaders,” March 2014 
 
 
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Appendix C:  Prior Testimony (January 2020 to Present) 
Alorica v. Sam’s Club, JAMS, JAMS Reference No. 1200056432  
 Deposed, May 7, 2021. 
 Testified, September 7, 2021. 
 
RDI v. Dentalplans.com, 17th Judicial Circuit of the County of Broward, Florida, Case No. 
CACE 18-29136  
 Deposed, October 21, 2021. 
 
Genesys v. Talkdesk, United States District Court for the Southern District of Indiana, Case No. 
1:19-cv-00695-TWP-DML   
 Deposed, June 3, 2021. 
 Testified February 28, 2023 
 
Skyview Capital and Continuum Global Services v. Conduent, Supreme Court of the State of 
New York, County of New York, Case No. 650761/2020 
 Deposed, May 25, 2023 
 
 
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Appendix D:  Materials List 
In addition to sources cited in the Report and accompanying Exhibits, I considered the following 
in developing my opinions: 
Declarations and Deposition Transcripts and Accompanying Exhibits 
Declaration of William Golden, April 20, 2021 
Deposition of William Golden, February 22, 2024 
Deposition of Shane Daniels, February 6, 2024  
Declaration of Stephen Hindle, October 24, 2024 
Declaration of Kelley Lorenzen, October 24, 2024 
 
Call Center Industry Documents and Other External Sources 
Avaya Call Management System Database Items and Calculations, Release 18, July 2016 
The 2021 U.S. Contact Center Decision-Makers Guide, ContactBabel, 13th Edition, 2021 
The 2023 U.S. Contact Center Decision-Makers Guide, ContactBabel, 15th Edition, 2023 
U.S. Contact Center Verticals: Finance, ContactBabel, 2024 
U.S. Contact Center Verticals: Outsourcing, ContactBabel, 2024 
https://slate.com/business/2020/06/delta-airlines-customer-service.html 
https://www.royalcaribbeanblog.com/2020/05/27/royal-caribbean-hires-back-over-100-laid-
workers-help-long-phone-hold-times 
https://www.talkdesk.com/resources/infographics/tips-for-getting-better-customer-service/   
https://thepointsguy.com/news/how-to-reach-delta  
Payment Card Industry Data Security Standard, Requirements and Security Assessment 
Procedures, Version 3.2.1, May 2018 
Protecting Telephone-Based Payment Card Data, PCI Security Standards Council, Version 3.0, 
November 2018 
https://investor.bankofamerica.com/profile as of February 26 2024 
https://www.bankofamerica.com/deposits/rebate-cards/commercial-prepaid-card/ as of February 
26 2024 
Support Technology without an Analyst Is Like a Car without a Driver, Lori Bocklund, CONTACT 
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CENTER PIPELINE (May 2021) 
Metrics Roundup, Susan Hash, CONTACT CENTER PIPELINE (Oct. 2019) 
cc-Modeler Lite, Erlang-C calculator by KoolToolz 
Pleadings and Other Case Documents  
First Amended Master Consolidated Complaint, June 13, 2023 
Consent Order in the Matter of:  Bank of America, N.A., Consumer Financial Protection Bureau, 
File No. 2022-CFPB-0004, July 14, 2022 
Bank of America’s Responses and Objections to Plaintiff Yick’s Fifth Set of Interrogatories, 
February 2, 2024 
Defendant’s Memorandum of Points and Authorities in Opposition to Plaintiffs’ Motion for 
Class Certification, In re: Bank of America California Unemployment Benefits Litig., No. 3:21-
md-02992-GPC-MSB, United States District Court for the Southern District of California, 
October 24, 2024 
February 9, 2024 Letter from Bank Counsel to Plaintiffs Counsel re: Call Center Data 
 
 
 
 
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Documents Produced by Defendant (1 of 3) 
BANA_EDD_MDL-00001502 
BANA_EDD_MDL-00001935 
BANA_EDD_MDL-00002018 
BANA_EDD_MDL-00012792 
BANA_EDD_MDL-00012793 
BANA_EDD_MDL-00012797 
BANA_EDD_MDL-00012800 
BANA_EDD_MDL-00012803 
BANA_EDD_MDL-00012816 
BANA_EDD_MDL-00012875 
BANA_EDD_MDL-00013074 
BANA_EDD_MDL-00013082 
BANA_EDD_MDL-00013086 
BANA_EDD_MDL-00013096 
BANA_EDD_MDL-00013110 
BANA_EDD_MDL-00013111 
BANA_EDD_MDL-00013174 
BANA_EDD_MDL-00019264 
BANA_EDD_MDL-00019266 
BANA_EDD_MDL-00019362 
BANA_EDD_MDL-00019373 
BANA_EDD_MDL-00019476 
BANA_EDD_MDL-00019555 
BANA_EDD_MDL-00021102 
BANA_EDD_MDL-00025811 
BANA_EDD_MDL-00027536 
BANA_EDD_MDL-00028134 
BANA_EDD_MDL-00038694 
BANA_EDD_MDL-00056667 
BANA_EDD_MDL-00056832 
BANA_EDD_MDL-00057091 
BANA_EDD_MDL-00060229 
BANA_EDD_MDL-00060339 
BANA_EDD_MDL-00060417 
BANA_EDD_MDL-00061383 
BANA_EDD_MDL-00061582 
BANA_EDD_MDL-00062838 
BANA_EDD_MDL-00065924 
BANA_EDD_MDL-00065985 
BANA_EDD_MDL-00066408 
BANA_EDD_MDL-00067169 
BANA_EDD_MDL-00067224 
BANA_EDD_MDL-00067590 
BANA_EDD_MDL-00067592 
BANA_EDD_MDL-00068789 
BANA_EDD_MDL-00068790 
BANA_EDD_MDL-00068834 
BANA_EDD_MDL-00069576 
BANA_EDD_MDL-00076747 
BANA_EDD_MDL-00090023 
BANA_EDD_MDL-00090034 
BANA_EDD_MDL-00090040 
BANA_EDD_MDL-00090498 
BANA_EDD_MDL-00090577 
BANA_EDD_MDL-00090657 
BANA_EDD_MDL-00091919 
BANA_EDD_MDL-00091923 
BANA_EDD_MDL-00092646 
BANA_EDD_MDL-00093262 
BANA_EDD_MDL-00093422 
BANA_EDD_MDL-00094056 
BANA_EDD_MDL-00095514 
BANA_EDD_MDL-00100634 
BANA_EDD_MDL-00101725 
BANA_EDD_MDL-00102812 
BANA_EDD_MDL-00103075 
BANA_EDD_MDL-00103150 
BANA_EDD_MDL-00103312 
BANA_EDD_MDL-00104599 
BANA_EDD_MDL-00105110 
BANA_EDD_MDL-00105477 
BANA_EDD_MDL-00106092 
BANA_EDD_MDL-00106236 
BANA_EDD_MDL-00106487 
BANA_EDD_MDL-00107706 
BANA_EDD_MDL-00108517 
BANA_EDD_MDL-00109298 
BANA_EDD_MDL-00115804 
BANA_EDD_MDL-00118438 
BANA_EDD_MDL-00118460 
BANA_EDD_MDL-00126413 
BANA_EDD_MDL-00126428 
BANA_EDD_MDL-00126635 
BANA_EDD_MDL-00127312 
BANA_EDD_MDL-00127367 
BANA_EDD_MDL-00127457 
BANA_EDD_MDL-00127480 
BANA_EDD_MDL-00127980 
BANA_EDD_MDL-00127988 
BANA_EDD_MDL-00130192 
BANA_EDD_MDL-00130232 
BANA_EDD_MDL-00140261 
BANA_EDD_MDL-00140417 
 
 
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Documents Produced by Defendant (2 of 3) 
BANA_EDD_MDL-00140419 
BANA_EDD_MDL-00140627 
BANA_EDD_MDL-00140691 
BANA_EDD_MDL-00141482 
BANA_EDD_MDL-00141458 
BANA_EDD_MDL-00141618 
BANA_EDD_MDL-00141919 
BANA_EDD_MDL-00142001 
BANA_EDD_MDL-00142490 
BANA_EDD_MDL-00142764 
BANA_EDD_MDL-00143155 
BANA_EDD_MDL-00145888 
BANA_EDD_MDL-00148891 
BANA_EDD_MDL-00155082 
BANA_EDD_MDL-00162954 
BANA_EDD_MDL-00163453 
BANA_EDD_MDL-00171905 
BANA_EDD_MDL-00172236 
BANA_EDD_MDL-00172538 
BANA_EDD_MDL-00172855 
BANA_EDD_MDL-00173603 
BANA_EDD_MDL-00173993 
BANA_EDD_MDL-00175335 
BANA_EDD_MDL-00177361 
BANA_EDD_MDL-00182381 
BANA_EDD_MDL-00187216 
BANA_EDD_MDL-00187433 
BANA_EDD_MDL-00188414 
BANA_EDD_MDL-00188572 
BANA_EDD_MDL-00191003 
BANA_EDD_MDL-00198653 
BANA_EDD_MDL-00199253 
BANA_EDD_MDL-00200185 
BANA_EDD_MDL-00206316 
BANA_EDD_MDL-00210000 
BANA_EDD_MDL-00217469 
BANA_EDD_MDL-00217549 
BANA_EDD_MDL-00218450 
BANA_EDD_MDL-00225648 
BANA_EDD_MDL-00234529 
BANA_EDD_MDL-00235537 
BANA_EDD_MDL-00235706 
BANA_EDD_MDL-00235714 
BANA_EDD_MDL-00236111 
BANA_EDD_MDL-00236846 
BANA_EDD_MDL-00236848 
BANA_EDD_MDL-00236865 
BANA_EDD_MDL-00240054 
BANA_EDD_MDL-00243363 
BANA_EDD_MDL-00246363 
BANA_EDD_MDL-00261052 
BANA_EDD_MDL-00261375 
BANA_EDD_MDL-00261795 
BANA_EDD_MDL-00261884 
BANA_EDD_MDL-00262582 
BANA_EDD_MDL-00263582 
BANA_EDD_MDL-00263806 
BANA_EDD_MDL-00288598 
BANA_EDD_MDL-00288637 
BANA_EDD_MDL-00288640 
BANA_EDD_MDL-00290235 
BANA_EDD_MDL-00293070 
BANA_EDD_MDL-00314421 
BANA_EDD_MDL-00315852 
BANA_EDD_MDL-00352749 
BANA_EDD_MDL-00352985 
BANA_EDD_MDL-00355584 
BANA_EDD_MDL-00356316 
BANA_EDD_MDL-00358108 
BANA_EDD_MDL-00359360 
BANA_EDD_MDL-00359361 
BANA_EDD_MDL-00359362 
BANA_EDD_MDL-00359363 
BANA_EDD_MDL-00359364 
BANA_EDD_MDL-00359365 
BANA_EDD_MDL-00368111 
BANA_EDD_MDL-00372269 
BANA_EDD_MDL-00373514 
BANA_EDD_MDL-00374770 
BANA_EDD_MDL-00375717 
BANA_EDD_MDL-00376191 
BANA_EDD_MDL-00377118 
BANA_EDD_MDL-00379986 
BANA_EDD_MDL-00379991 
BANA_EDD_MDL-00382181 
BANA_EDD_MDL-00383694 
BANA_EDD_MDL-00405979 
BANA_EDD_MDL-00424944 
BANA_EDD_MDL-00436069 
BANA_EDD_MDL-00438429 
BANA_EDD_MDL-00455530 
BANA_EDD_MDL-00455608 
BANA_EDD_MDL-00455649 
 
 
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69 
Documents Produced by Defendant (3 of 3) 
BANA_EDD_MDL-00482670 
BANA_EDD_MDL-00491397 
BANA_EDD_MDL-00491410 
BANA_EDD_MDL-00498025 
BANA_EDD_MDL-00503278 
BANA_EDD_MDL-00510120 
BANA_EDD_MDL-00517105 
BANA_EDD_MDL-00522406 
BANA_EDD_MDL-00548592 
BANA_EDD_MDL-00549619 
BANA_EDD_MDL-00589505 
BANA_EDD_MDL-00616368 
BANA_EDD_MDL-00616664 
BANA_EDD_MDL-00618616 
BANA_EDD_MDL-00639950 
BANA_EDD_MDL-00640402 
BANA_EDD_MDL-00641997 
BANA_EDD_MDL-00643469 
BANA_EDD_MDL-00648284 
BANA_EDD_MDL-00657359 
BANA_EDD_MDL-00657367 
BANA_EDD_MDL-00657370 
BANA_EDD_MDL-00659985 
BANA_EDD_MDL-00662478 
BANA_EDD_MDL-00669718 
BANA_EDD_MDL-00678495 
BANA_EDD_MDL-00681274 
BANA_EDD_MDL-00681616 
BANA_EDD_MDL-00694815 
BANA_EDD_MDL-00694825 
BANA_EDD_MDL-00694835 
BANA_EDD_MDL-00694845 
BANA_EDD_MDL-00694847 
BANA_EDD_MDL-00694848 
BANA_EDD_MDL-00694849 
BANA_EDD_MDL-00697114 
BANA_EDD_MDL-00697318 
BANA_EDD_MDL-00712063 
BANA_EDD_MDL-00718992 
BANA_EDD_MDL-00719114 
BANA_EDD_MDL-00719115 
BANA_EDD_MDL-00719116 
BANA_EDD_MDL-00643363 
BANA_EDD_MDL-00002286 
BANA_EDD_MDL-00153667 
BANA_EDD_MDL-00061799 
BANA_EDD_MDL-00107356 
BANA_EDD_MDL-00205620 
BANA_EDD_MDL-00859710 
BANA_EDD_MDL-00001044 
BANA_EDD_MDL-00001361 
BANA_EDD_MDL-00056916 
BANA_EDD_MDL-00190443 
BANA_EDD_MDL-00291083 
BANA_EDD_MDL-00809945 
BANA_EDD_MDL-00812294 
 
 
Also, the excel file titled “TTEC Servicing and Fraud Call Metrics 2020-2021” (no Bates No.) 
 
 
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70 
Appendix E:  Survey Results, The 2021 US Contact Center Decision Makers’ Guide, Page 
46 
 
 
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71 
Appendix F:  
  
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72 
Appendix F:  
 
 
1 - Abandoned percent is derived by dividing the number of calls abandoned into the number of calls offered.
 
Source:  BANA_EDD_MDL-00719115
 
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73 
  
Appendix G:   
 
 
 
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74 
Appendix G:   
 
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75 
Appendix H: Calculating Distinct Demand 
 
 
 
Notes:
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76 
Appendix I – 
 
 
 
Notes:
 
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