Customer_Satisfaction_Presentation_2020.ppt.pptx

Earth Observing System Data
and Information System
2020 Customer Satisfaction Results
December 2020
PSS115
IA# 22017A0

2 © 2020 CFI Group. All rights reserved.
History of CFI Group
 CFI Group: Founded in 1988
 Founding partner of the ACSI*
 Cause and effect methodology / predictive analytics
 Professional services project leads have 20+ years experience
 Serving a global list of clients from 5 offices across 3 continents
 Providing “actionable” customer feedback insights based on the science of the ACSI
*American Customer Satisfaction Index
CFI GROUP
WORLDWIDE
USA – Ann Arbor, MI
(corporate headquarters)
UK – London
ITALY – Milan
CHINA – Shanghai
SWEDEN – Stockholm

Introduction and
Methodology
1

Introduction and Methodology
 Measure customer satisfaction with NASA Earth Observing System Data and Information System (EOSDIS) at a national
level for each Distributed Active Archive Center (DAAC).
 Identify the key areas that NASA can leverage across the DAACs to continuously improve its service to its customers.
 Assess the trends in satisfaction with NASA EOSDIS specifically in the following areas:
› Customer Support
› Product Selection and Order
› Product Search
› Product Documentation
› Product Quality
› Delivery

Survey and Data
Collection
2

Survey and Data Collection
 Questionnaire developed by NASA EOSDIS and CFI Group.
› Measured respondent satisfaction and their experiences with a specific DAAC.
› The survey was designed to allow users to skip over the questions not related to their experience with the specified
DAAC.
› Each DAAC was allowed the opportunity to utilize their own unique supplemental questions (outside of the ACSI
model questions).
 Data collection performed via the web.
› NASA EOSDIS provided multiple lists of email addresses, which were combined, cleaned, and deduped by CFI
Group. 758,000 email invitations were sent.
› A total of 9,178 responses were received, for a response rate of 1.2%.
› The online survey was available September 21st through October 23rd, 2020.

Executive Summary
3

Executive Summary: CSI and Performance Outcomes
 The average aggregate Customer Satisfaction Index (CSI) score for NASA EOSDIS since the start of the survey in 2006
is 77. In 2020, CSI rose one point from last year and posted a score of 79.
 The 2020 future behavior scores matched their aggregate average since 2006 as both Likelihood to Recommend (87)
and Likelihood to use Services in the Future (88) rose one point from last year as well.
 All drivers of satisfaction, were rated at 82 or above, which continues to be a good indicator of consistently strong
performance across the NASA EOSDIS customer experience. Product Quality and Customer Support were the highest
rated satisfaction drivers with a score of 85.
 The 2020 CSI score of 79 outpaced the ACSI aggregate Government score of 68 and was four points above the
National ACSI average of 74.
 CSI across all DAACs were generally on-par with last year’s scores. Individual DAAC scores ranged from a low of 71 to
a high of 83. ASF DAAC, and GES have shown slight but consistent improvement since 2017.
 CSI scores varied by demographic variables:
› Domestic users tended to be more satisfied as users from the USA had scores that were two points higher (80)
than the users from outside the USA (78).
› University Professors again reported the highest level of satisfaction (82) and NASA-affiliated Scientists posted the
lowest score (76).

Key Findings & Actionable Suggestions
Key Findings Actionable Suggestions
Individual DAAC
satisfaction
 CSI scores by DAAC had a twelve-
point variance with a low score of 71
to a high score of 83. This indicates
that not all customers have the
same experience.
 Attempt to understand the customer experience
differences across different DAACs. Uncover best
practice ideas that can be applied across all DAACs by
analyzing the customer experience through user
comments.
Tools used to work
with the data
 Although there are minimal
differences in CSI among all tools
used to work with the data, a
majority of customers use one of
three tools to work with the data
(ArcGIS, Quantum GIS and Excel).
 Tailoring the user experience to align with the most
popular tools used will allow NASA EOSDIS
standardize user familiarity with EOSDIS and improve
the experience for the greatest number of customers.

Customer Satisfaction
Model Results
4

2020 NASA EOSDIS – Customer Satisfaction Model
Satisfaction Drivers CSI
Scores represent your performance as rated by customers.
Impacts show you which driver has the most/least leverage – where improvements
matter most/least to your customers
Scores for FY20 YTD; impacts for FY19
83 1.3
Product Selection and
Order
85 1.1 Product Quality
82 1.1 Product Documentation
82 1.0 Product Search
85 0.7 Customer Support
84 0.5 Delivery
Likelihood to Recommend 4.0 87
Likelihood to Use Services
in Future
3.6 88
Overall Satisfaction: 82
Compared to Expectations: 76
Compared to Ideal: 77
n = 9,178
79
Future Behaviors

SIEA Member Satisfaction Priority Matrix
 Drivers in the Top Priority
quadrant have a high impact on
CSI and a relatively low score.
These are the drivers where the
organization can achieve
significant improvements and see
positive changes in customer
satisfaction.
 Strengths are high impact drivers
that also have high scores. There
is less room for improvement with
these drivers than the Top
Priorities, however, these drivers
have high impact on satisfaction.
 Maintain identifies high-scoring
drivers that do not have high
impact on customer satisfaction.
Maintaining the already high
scores for these drivers is
important.
 Secondary Opportunities are
drivers that have low impact on
satisfaction and are relatively low
scoring.
Maintain Strength
Secondary
Opportunity
Top Priority
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
[CELLRANGE]
75
80
85
90
0.0 0.5 1.0 1.5 2.0
Driver
Score
Impact onto Satisfaction

CSI and Performance Outcomes: Four-Year Trending
 Although both CSI and Future
Behaviors increased one point
from last year, all have
remained relatively steady
over the last four years. 77 78 79 78 79
2016 2017 2018 2019 2020
Sample Size 7133 7505 2778 6337 9178
Customer Satisfaction Index 77 78 79 78 79
Likelihood to Recommend 87 87 88 86 87
Likelihood to Use Services in
Future
88 89 89 87 88
Customer Satisfaction Index
Indicates change is significant at 90% confidence

Benchmarks
 NASA EOSDIS outscored
both the national ACSI and
Government average scores.
 Scores in blue represent CSI
scores for other Federal
Government Agencies while
scores in aqua represent
summary scores.
86
83
80
79
74
73
68
68
National Weather Service - 2019
National Park Service website 2019
Department of Education, FAFSA - 2019
NASA EOSDIS - Aggregate 2020
National ACSI - Q3 2020
Veteran's Affairs Employee IT Services
Centers for Medicare/Medicaid Services
Federal Government - Overall 2019

CSI by DAAC and Other
Segments
5

CSI and Frequency by DAAC
 Most DAAC scores and
response percentages
remained relatively steady
from last year.
2019
%
2019
N
2019
CSI
2020
%
2020
N
2020
CSI
Data center evaluated
ASDC-LaRC 11% 673 76 13% 1,205 77
ASF DAAC 12% 787 82 13% 1,239 83
CDDIS 3% 159 79 3% 281 77
GES DISC 13% 800 78 12% 1,146 79
GHRC 7% 425 74 7% 616 71
LP DAAC 29% 1,860 79 23% 2,069 80
MODAPS LAADS 12% 787 78 14% 1,254 78
NSIDC DAAC 3% 200 78 3% 298 79
OB.DAAC 1% 77 76 2% 222 81
ORNL DAAC 2% 106 83 1% 125 82
PO DAAC-JPL 3% 168 78 4% 349 76
SEDAC 5% 295 76 4% 374 75
Number of Respondents 6,337 9,178

CSI: Four-Year Comparison by DAAC
 Only a few DAACs
experienced a statistically
significant change in scores
from last year.
 ASF DAAC, and GES have
shown slight but consistent
improvement since 2017.
77
83
77
79
71
80
76
82
79
78
74
79
74
82
79
77
73
80
77
80
77
77
72
79
50 60 70 80 90
ASDC-LaRC
ASF DAAC
CDDIS
GES DISC
GHRC
LP DAAC
2020 2019 2018 2017
78
79
81
82
76
75
78
78
76
83
78
76
77
79
85
81
82
76
78
79
76
82
80
75
50 60 70 80 90
MODAPS LAADS
NSIDC DAAC
OB.DAAC
ORNL DAAC
PO DAAC-JPL
SEDAC

CSI and Driver Scores: USA vs. All Other Countries
 CSI is two points higher for
domestic respondents; driven
primarily by significantly
higher scores in Customer
Support.
 Product Search, Product
Selection and Product
Documentation were areas
where non-domestic
respondents scored higher
than USA respondents.
USA All Others Difference
Significant
Difference
Sample Size 1,304 7,874
Product Search 80 83 -3
Product Selection and Order 82 83 -1
Delivery 85 84 1
Product Quality 86 85 1
Product Documentation 80 82 -2
Customer Support 90 84 6
Customer Satisfaction Index 80 78 2
Likelihood to Recommend 88 87 1
Likelihood to Use Services in Future 90 87 3

Yearly CSI Trend by Location
77 77
79
77
79
78
79
80 80 80 80
77 77
76 76
78
76
77
78 78 78 78
27% 29%
25% 24% 25%
19%
16% 17% 16% 14% 14%
0%
20%
40%
60%
80%
100%
70
71
72
73
74
75
76
77
78
79
80
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
USA All Other % USA

CSI and Frequency by Type of User
 Consistent with past studies,
Earth Science Researchers
(33%) represented the single
most common user type.
 University Professors again
report the highest level of
satisfaction (82).
2019
%
2019
N
2019
CSI
2020
%
2020
N
2020
CSI
Type of user~
General Public 18% 1,161 78 19% 1,760 78
Elementary, Middle, High School Teacher 2% 102 77 2% 197 77
University Professor 15% 945 82 18% 1,638 82
University Undergraduate Student 11% 721 75 11% 968 77
University Graduate Student 29% 1,863 77 25% 2,314 77
Other Education and Outreach 8% 493 78 8% 696 78
Earth Science Researcher 34% 2,127 80 33% 2,987 80
Earth Science Modeler 11% 680 78 10% 892 78
NASA-affiliated Scientist 2% 98 80 1% 134 76
Non-NASA-affiliated Scientist 5% 346 78 5% 468 79
NASA Science Team Member 1% 77 79 1% 123 78
Data Provider or Tool Developer/Decision Support
Systems Analyst
10% 625 78 9% 825 77
Data Tool Developer/Provider 0% 0 -- 0% 0 --
Decision Support Systems Analyst 0% 0 -- 0% 0 --
Interdisciplinary user 0% 0 -- 0% 0 --
Applications Scientist 0% 0 -- 0% 0 --
Other User Type 9% 545 78 9% 856 77
~Multiple responses allowed. Percentages may sum to more than
100.

Areas/Disciplines Need/Use Earth Science Data and Services
 Two-thirds of respondents
indicated they use the data
and services for Land study.
Atmosphere (37%) and
Ocean (20%) were also
common uses.
 There is little CSI variation
among the different
areas/disciplines of use.
2019
%
2019
N
2019
CSI
2020
%
2020
N
2020
CSI
General areas need or use Earth science data and
services~
Atmosphere 34% 2,186 79 37% 3,420 78
Biosphere 19% 1,202 79 19% 1,756 79
Calibrated radiance 8% 535 80 8% 753 78
Cryosphere 7% 412 79 7% 632 79
Human dimensions 15% 969 78 16% 1,466 78
Land 71% 4,477 78 66% 6,038 79
Near-real-time applications 16% 997 77 18% 1,621 78
Ocean 16% 1,033 79 20% 1,831 79
Space geodesy 12% 774 78 13% 1,194 79
Other general area 10% 635 77 9% 852 78
Not Applicable 1% 39 72 1% 58 74
100.

Driver Detail: Product
Quality
6

Product Quality
 Product Quality has scored 85
since 2017.
 Product Quality has a high
impact on satisfaction (1.1)
and should be considered a
strength for NASA EOSDIS.
83 85 85
0
85
2016 2017 2018 2019 2020
Sample Size 4435 4679 1737 0 4466
Product Quality 83 85 85 -- 85
Ease of using the data
product(s) in the delivered
format(s)
82 84 83 -- 84
The degree the data product(s)
matched what you originally
intended to order
83 85 86 -- 86
Degree data product helped
accomplish intended goals
84 86 86 -- 86
Product Quality
Indicates change is significant at 90% confidence Question not asked in 2019

Product Quality: Four-Year Comparison by DAAC
 Product Quality performance
in 2020 varies slightly by
DAAC.
83
88
85
78
86
82
87
89
83
82
86
84
86
89
84
80
86
50 60 70 80 90
ASDC-LaRC
ASF DAAC
CDDIS
GES DISC
GHRC
LP DAAC
2020 2019 2018 2017
82
86
88
85
85
81
81
84
87
86
88
83
83
83
88
89
87
81
50 60 70 80 90
MODAPS LAADS
NSIDC DAAC
OB.DAAC
ORNL DAAC
PO DAAC-JPL
SEDAC

Software Tools/Packages Used to Work with Data
 Just over three-quarters of
respondents reported using
software packages to work
with the data while just under
a third made their own tool.
 For the 30% of respondents
who reported programming
their own tool, Python was the
most popular language.
2018
%
2018
N
2018
CSI
2020
%
2020
N
2020
CSI
Used software tools or packages to work with data~
Yes, I used software tools or packages to work with data 81% 1,410 81 76% 3,418 83
Yes, I made my own using a programming language 30% 516 81 30% 1,341 82
No, I couldn’t find what I needed 2% 27 60 2% 80 67
No, I couldn’t understand how to use it 2% 30 76 2% 106 74
No, I did not need software tools 5% 93 83 7% 302 84
Programming languages generally use~
C 10% 52 85 10% 130 84
C++ 13% 69 82 13% 169 82
C# 3% 18 82 3% 34 83
Fortran 77 6% 30 84 6% 78 84
Fortran 90 15% 78 79 11% 144 83
IDL 15% 76 84 11% 145 84
Java 7% 38 84 8% 107 81
Perl 4% 23 84 3% 46 83
PHP 3% 13 85 3% 34 81
Python 63% 326 81 62% 833 82
Julia 0% 0 -- 2% 25 83
R 0% 0 -- 31% 418 82
Other programming languages 34% 174 82 20% 271 81
Don't know/Not applicable 1% 7 86 1% 17 84
Number of Respondents 516 1,341
100.
Question not asked in 2019

Tools Used to Work with Data
 ArcGIS is the most used
software tool/package at 60%,
followed by Quantum GIS
(43%), and Excel (29%).
 There are only minimal
differences in CSI among the
most popular tools.
2018
%
2018
N
2018
CSI
2020
%
2020
N
2020
CSI
Used software tools or packages to work with data~
ArcGIS 64% 898 81 60% 2,050 83
Convert to Vector 6% 80 80 5% 180 82
ENVI 32% 450 82 26% 884 84
ERDAS/IMAGINE 20% 278 82 17% 574 83
Excel 29% 409 81 29% 994 82
Ferret 1% 10 77 1% 36 86
Geomatica 4% 53 78 4% 127 86
Global Mapper 15% 206 81 13% 432 83
GrADS 3% 46 83 3% 103 84
GRASS 12% 174 82 11% 393 85
HDFLook 2% 27 84 1% 23 87
HDFView 10% 138 79 6% 203 85
HEG 1% 20 81 1% 47 87
IDL 7% 100 83 0% 0 --
IDV 1% 12 86 1% 39 85
IDRISI 7% 96 81 5% 179 84
MapReady 2% 22 85 1% 35 85
MATLAB 18% 255 81 15% 526 84
MODIS Reprojection Tool (MRT) 9% 126 81 0% 0 --
NCL 3% 47 84 2% 84 85
Panoply 9% 121 80 8% 269 83
Quantum GIS (QGIS) 42% 587 81 43% 1,476 83
R 22% 315 80 0% 0 --
SeaDAS 3% 46 81 4% 147 85
GDAL 0% 0 -- 18% 617 84
Jupyter Notebooks 0% 0 -- 10% 353 84
Other/open source 23% 320 81 24% 820 83
Don't know/Not applicable 1% 8 88 1% 22 77
100.
Question not asked in 2019

Documentation
7

Product Documentation
 Sixty-seven percent of
respondents looked for or
obtained documentation
related to the data, which is
six percentage points lower
than last year.
 Scores have increased slowly
but steadily over the last three
years with ‘Technical level’
posting the highest score.
79 80 81
0
82
2016 2017 2018 2019 2020
Sample Size 5000 5258 1988 0 6036
Product Documentation 79 80 81 -- 82
Overall quality of the document 79 80 81 -- 82
Technical level -- -- 83 -- 84
Organization -- -- 81 -- 82
Clarity and usefulness -- -- 80 -- 81
Data documentation helped you
use the data
79 80 81 -- 81
Product Documentation

Product Documentation: Four-Year Comparison by DAAC
 DAAC scores have shown
some minor fluctuations over
the last four years.
81
85
81
82
79
83
78
84
81
80
78
82
81
83
79
79
78
80
50 60 70 80 90
ASDC-LaRC
ASF DAAC
CDDIS
GES DISC
GHRC
LP DAAC
2020 2019 2018 2017
81
82
83
83
78
81
78
80
85
82
83
79
79
80
78
85
82
78
50 60 70 80 90
MODAPS LAADS
NSIDC DAAC
OB.DAAC
ORNL DAAC
PO DAAC-JPL
SEDAC

Selection and Order
8

Product Selection and Order
 Sixty-eight percent of
respondents
requested/acquired data
products from a DAAC in the
last year. This is a drop of 11
percentage points from 2018.
 Product Selection and Order
has the highest influence on
CSI (1.3).
82 83 83
0
83
2016 2017 2018 2019 2020
Sample Size 4654 5001 1856 0 4883
Product Selection and Order 82 83 83 -- 83
Ease of selecting data products 82 82 83 -- 82
Ease of requesting or ordering
data products
83 83 83 -- 83
Direct downloads -- 84 84 -- 84
Product Selection and Order

Product Selection and Order: Four-Year Comparison by
DAAC
 Product Selection and Order
scores by DAAC have
remained relatively consistent
over the past several years.
 Individual 2020 DAAC scores
ranged from 78 to 87.
81
87
83
83
78
84
82
87
84
80
79
84
82
86
88
80
78
84
50 60 70 80 90
ASDC-LaRC
ASF DAAC
CDDIS
GES DISC
GHRC
LP DAAC
2020 2019 2018 2017
80
84
86
84
81
81
80
82
87
82
84
80
82
81
85
86
85
83
50 60 70 80 90
MODAPS LAADS
NSIDC DAAC
OB.DAAC
ORNL DAAC
PO DAAC-JPL
SEDAC

Driver Detail: Customer
Support
9

Customer Support
 Thirteen percent of
respondents indicated that
they contacted a DAAC User
Services Office or interacted
with DAAC personnel in the
past year, which is three
percentage points below
2018.
85 87 86
0
85
2016 2017 2018 2019 2020
Sample Size 1237 1195 420 0 1168
Customer Support 85 87 86 -- 85
Professionalism 86 89 88 -- 87
Technical knowledge 86 88 88 -- 87
Helpfulness in resolving a
problem
84 87 85 -- 85
Speed of response 83 86 85 -- 84
Customer Support

Customer Support: Four-Year Comparison by DAAC
 Scores for Customer support
rose for ASDC, ASF, CDDIS,
and GES this year.
82
87
86
78
85
80
88
79
85
81
87
85
90
86
89
86
50 60 70 80 90
ASDC-LaRC
ASF DAAC
CDDIS
GES DISC
GHRC
LP DAAC
2020 2019 2018 2017
85
89
82
82
86
88
89
84
90
90
89
50 60 70 80 90
MODAPS LAADS
NSIDC DAAC
OB.DAAC
ORNL DAAC
PO DAAC-JPL
SEDAC
90
91
90
90

Search
10

Product Search
 Product Search scores have
remained consistent over the
past four years.
80 82 81
0
82
2016 2017 2018 2019 2020
Sample Size 5886 6351 2328 0 7084
Product Search 80 82 81 -- 82
Ease of using search
tool/capability
79 81 81 -- 81
How well the search results met
your needs
81 82 82 -- 83
Product Search

Product Search: Four-Year Comparison by DAAC
 Most DAACs saw a slight rise
in Product Search scores in
2020.
81
87
81
82
77
83
79
85
77
79
76
83
81
84
81
79
78
82
50 60 70 80 90
ASDC-LaRC
ASF DAAC
CDDIS
GES DISC
GHRC
LP DAAC
2020 2019 2018 2017
81
81
85
84
79
79
79
80
90
79
80
78
81
80
84
84
82
78
50 60 70 80 90
MODAPS LAADS
NSIDC DAAC
OB.DAAC
ORNL DAAC
PO DAAC-JPL
SEDAC

Driver Detail: Delivery
11

Delivery
 Delivery scored 84 for the
fourth consecutive year.
84 84 84
0
84
2016 2017 2018 2019 2020
Sample Size 4395 4621 1725 0 4384
Delivery 84 84 84 -- 84
Convenience of delivery method 84 85 84 -- 85
Speed of delivery method 84 84 84 -- 84
Delivery

Delivery: Four-Year Comparison by DAAC
 Most DAACs posted Delivery
scores in the 80s with ASF
leading the way with 88.
82
88
86
84
78
85
83
86
88
81
82
85
83
87
87
81
80
85
50 60 70 80 90
ASDC-LaRC
ASF DAAC
CDDIS
GES DISC
GHRC
LP DAAC
2020 2019 2018 2017
81
87
87
86
83
83
79
84
87
87
89
86
82
83
87
89
86
85
50 60 70 80 90
MODAPS LAADS
NSIDC DAAC
OB.DAAC
ORNL DAAC
PO DAAC-JPL
SEDAC

Appendix
12

Appendix
2020 Scores
Aggregate
Impact
Sample Size 9,178
Product Search 82 1.0
Ease of using search tool/capability 81 --
How well the search results met your needs 83 --
Product Selection and Order 83 1.3
Ease of selecting data products 82 --
Ease of requesting or ordering data products 83 --
Direct downloads 84 --
Delivery 84 0.5
Convenience of delivery method 85 --
Speed of delivery method 84 --
Product Quality 85 1.1
Ease of using the data product(s) in the delivered format(s) 84 --
The degree the data product(s) matched what you originally intended to
order
86 --
Degree data product helped accomplish intended goals 86 --
Product Documentation 82 1.1
Overall quality of the document 82 --
Technical level 84 --
Organization 82 --
Clarity and usefulness 81 --
Data documentation helped you use the data 81 --

Appendix
2020 Scores
Aggregate
Impact
Customer Support 85 0.7
Professionalism 87 --
Technical knowledge 87 --
Helpfulness in resolving a problem 85 --
Speed of response 84 --
GES DISC - Data How-To 81 N/A
GES DISC - Ease of using Data How-to 81 --
GES DISC - How Data How-to helped accomplish intended goals 81 --
GHRC - Data Recipes -- N/A
GHRC - Ease of using the Data Recipes -- --
GHRC - Correctness of steps -- --
GHRC - How Data Recipes helped accomplish intended goal -- --
LP DAAC Tutorial 81 N/A
LP DAAC - How Tutorial helped accomplish intended goal 81 --
Customer Satisfaction Index 79 N/A
Overall Satisfaction 82 --
Expectations 76 --
Ideal 77 --
Likelihood to Recommend 87 4.0
Likelihood to recommend 87 --
Likelihood to Use Services in Future 88 3.6
Likelihood to use services in future 88 --

Appendix
ASDC-LaRC ASF DAAC
2017
Scores
2018
Scores
2019
Scores
2020
Scores
2017
Scores
2018
Scores
2019
Scores
2020
Scores
Product Search 81 79 -- 81 84 85 -- 87
Product Selection and Order 82 82 -- 81 86 87 -- 87
Delivery 83 83 -- 82 87 86 -- 88
Product Quality 84 82 -- 83 86 87 -- 88
Product Documentation 81 78 -- 81 83 84 -- 85
Customer Support 85 80 -- 82 90 88 -- 91
Customer Satisfaction Index 77 74 76 77 80 82 82 83
CDDIS GES DISC
2017
Scores
2018
Scores
2019
Scores
2020
Scores
2017
Scores
2018
Scores
2019
Scores
2020
Scores
Product Search 81 77 -- 81 79 79 -- 82
Delivery 87 88 -- 86 81 81 -- 84
Product Quality 89 89 -- 91 84 83 -- 85
GHRC LP DAAC
2017
Scores
2018
Scores
2019
Scores
2020
Scores
2017
Scores
2018
Scores
2019
Scores
2020
Scores
Product Search 78 76 -- 77 82 83 -- 83
Delivery 80 82 -- 78 85 85 -- 85
Product Quality 80 82 -- 78 86 86 -- 86

Appendix
MODAPS LAADS NSIDC DAAC
2017
Scores
2018
Scores
2019
Scores
2020
Scores
2017
Scores
2018
Scores
2019
Scores
2020
Scores
Product Search 81 79 -- 81 80 80 -- 81
Delivery 82 79 -- 81 83 84 -- 87
Product Quality 83 81 -- 82 83 84 -- 86
OB.DAAC ORNL DAAC
2017
Scores
2018
Scores
2019
Scores
2020
Scores
2017
Scores
2018
Scores
2019
Scores
2020
Scores
Product Search 84 90 -- 85 84 79 -- 84
Delivery 87 87 -- 87 89 87 -- 86
Product Quality 88 87 -- 88 89 86 -- 85
PO DAAC-JPL SEDAC
2017
Scores
2018
Scores
2019
Scores
2020
Scores
2017
Scores
2018
Scores
2019
Scores
2020
Scores
Product Search 82 80 -- 79 78 78 -- 79
Delivery 86 89 -- 83 85 86 -- 83
Product Quality 87 88 -- 85 81 83 -- 81

About Federal Consulting Group
The Federal Consulting Group (FCG) specializes in organizational development, change, and strategy
execution. Team FCG brings clarity to complexity by helping federal executives focus on the purpose and
strategic direction of their agency’s core mission. We work with federal agencies to improve the services
and performance they deliver to or on behalf of the American people.
Executive
Coaching
Consulting
Performance
Measurement and
Satisfaction
Leadership
Effectiveness
Creating a citizen-centric, results-oriented government
EXPERTS AT

About Federal Consulting Group
Since 2001, FCG has partnered with CFI Group to help government agencies measure satisfaction with
federal agencies and programs
Using insights provided by CFI Group research, FCG professionals are available to consult with
agencies to develop strategic plans for improving performance in fulfilling agency missions
Contact your FCG point of contact today to discuss how you can schedule follow up sessions to turn
your CSI findings and insights into action plans
ACCESS TO
EXPERTS
EXCEED
PERFORMANCE
MANDATES
EXPEDITE
OMB
CLEARANCE
3 REASONS
TO WORK WITH
TEAM FCG
1 2 3

THANK YOU
FEDERAL CONSULTING GROUP
Rafael Willams-
Contracting Officer’s Representative (COR)
202-748-3770 (tel)
rafael_williams@ios.doi.gov
Jessica Reed
Director
202-208-4699 (tel)
Jessica_reed@ios.doi.gov
PSS115
IA# 22017A0
Delivered By CFI GROUP
3916 Ranchero Drive
Ann Arbor, MI 48108
734.930.9090 (tel)
www.cfigroup.com
Mark Galauner– Customer Insights Consultant
mgalaunerr@cfigroup.com
734-623-1384
Kelly Stallard – Program Director
kstallard@cfigroup.com
734-623-1305

Customer_Satisfaction_Presentation_2020.ppt.pptx

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