A business data analysis of e-Solution's quality of service delivered from 2016 to 2017. The resulting analysis were listed in our prescribed service quality matrix which is based on industry standards on measuring service quality.

1. E-Solution
Service Quality Analysis
Prepared by: Diamond Rose Lumba
Master of Information Systems

2. AGENDA
2
Project
Overview
Metric Dive
Solution
Presentation
Group
Recommends

3. “
To be informed of Deakin e-Solution’s
service is of QUALITY [Trienekens et al., 2004]
What is the Project aiming to solve?
3

4. “
Prescribe a SERVICE QUALITY MATRIX
What are we proposing?
4

5. Service Quality Matrix
Scale Importance & Weight
Customer Satisfaction Rate
Interaction per Resolution
Time to Resolution
First Contact
Resolution
First Level
Resolution
Repeat Calls
5

6. SERVICE QUALITY
METRICS
6

7. CUSTOMER SATISFACTION
A measure of how service is
meeting (or exceeding)
customer expectation
Assessed through surveys of
customers via telephone call,
email or post [Joshi et al, 2011]
7

8.  99% of the tickets were
accepted by customers
 17 tickets were not
accepted
 99% of the tickets were
accepted by customers
 41 tickets were not
accepted
Analysis Results
8

9. Against the
Industry Global
Standards
HOW ARE YOU FARING? [Harmon et al., 2006]
Against Internal
Benchmark
 Customer satisfaction
rate is at 86% [Firuta, 2017]
 Customer satisfaction
KPI is set at 85%
 Maintained 99% in 2
years
9

10. INTERACTION per RESOLUTION
Measures the occurrence of incorrectly assigned
tickets.
It can result in delays to resolution process &
decrease customer satisfaction [Navvia, 2014]
10

11. 24462
4580
782
282
91
44
15
8
2
3
2
3
0 1 2 3 4 5 6 7 8 9 10 12
COUNT
NUMBER OF REASSIGNMENT
INTERACTION PER RESOLUTION
2016
 81% were resolved
without re-assigning
 15% had been re-
assigned once
35586
5055
922
348
137
42
18
9
7
4
3
2
1
0 1 2 3 4 5 6 7 8 9 10 11 13
COUNT
NUMBER OF REASSIGNMENT
INTERACTION PER RESOLUTION
2017
 84% were resolved
without re-assigning
 12% had been re-
assigned once
Analysis Results
11

12. Against the
Industry Global
Standards
HOW ARE YOU FARING? [Harmon et al., 2006]
Against Internal
Benchmark
 Interaction per
Resolution global
standard rate is at 86%
[Leggett, 2017]
 Interaction per
Resolution KPI was not
set
 Yearly improvement
12

13. TIME TO RESOLUTION
Measures the average time it takes
to resolve a problem
Though may differ depending on
complexity and urgency, aim to
shorten time [Ivarsson, 2013]
13

14. 1
10
100
1000
10000
100000
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
81
86
94
100
105
116
126
133
140
147
163
178
190
198
226
258
COUNT
DAYS TO RESOLUTION
TIME TO RESOLUTION 2016
1
10
100
1000
10000
100000
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
108
117
127
132
147
162
COUNT
DAYS TO RESOLUTION
TIME TO RESOLUTION 2017
 69% were resolved within
24 hours
 Maximum days to resolve
issue is 349
Analysis Results
 69% were resolved within
24 hours
 Maximum days to resolve
issue is 188
14

15. Against the
Industry Global
Standards
HOW ARE YOU FARING? [Harmon et al., 2006]
Against Internal
Benchmark
 Time to Resolution global
standard is at 66% within
3 days upon reporting
[Sharkasi, 2009]
 Time to Resolution KPI
was not set
 Yearly improvement
15

16. FIRST CONTACT RESOLUTION
Measures the number or
percentage of problems
resolved during the first
customer call.
An indicator of service’
effectiveness in handling
issues the first time. [Ramburg,
2011]
16

17. Analysis Results
 27% of the tickets were
resolved in their first
contact from January to
June 2016
 FCR rate from January
to June 2016 is 37%
 25% of the tickets were
resolved in their first
contact from January to
June 2017
 FCR rate from January
to June 2017 is 34%
30% 29% 41% 32% 34% 29%
0
2000
4000
6000
8000
10000
12000
14000
Jan Feb Mar Apr May Jun
Tickets Resolved in First Contact 2017
Total Calls First Contact Resolved FCR Calculation
31% 36% 45% 41% 34% 33%
0
1000
2000
3000
4000
5000
6000
7000
8000
Jan Feb Mar Apr May Jun
Tickets Resolved in First Contact 2016
Total Calls First Contact Resolved FCR Calculation
17

18. Against the
Industry Global
Standards
HOW ARE YOU FARING? [Harmon et al., 2006]
Against Internal
Benchmark
 First Contact Resolution
global standard is at 85%
[Narayanan, 2008]
 First Contact Resolution
KPI is set at 64%
 Yearly decline
18

19. FIRST LEVEL RESOLUTION
Measures rate of issues
being resolved by quickly by
service desk without
escalation.
However, can be misleading
therefore analyze conjointly
with repeat calls received.
[Atkinson, 2013]
19

20. 0
1000
2000
3000
4000
5000
6000
7000
8000
Jan Feb Mar Apr May Jun
First Level Resolution 2016
Total Tickets Received Resolved - First Level
Analysis Results
0
2000
4000
6000
8000
10000
12000
14000
Jan Feb Mar Apr May Jun
First Level Resolution 2017
Total Tickets Received Resolved - First Level
 76% were resolved within
the first level support
 82% were resolved within
the first level support
20

21. Against the
Industry Global
Standards
HOW ARE YOU FARING? [Harmon et al., 2006]
Against Internal
Benchmark
 First Level Resolution
global standard rate is
at 74% [Rumburg, 2011]
 FLR KPI is set at 70%
21

22. REPEAT CALLS
Measures the percentage
of tickets representing
recurring issues for the
same customer.
Indicator of a recurring flaw
in the system or service
[Atkinson, 2013] that may warrant
further root cause analysis.
22

23. Analysis Results
 39% of the total calls
received were repeat
calls
 Top 3 categories of
repeat calls are issues
relating to configuration
 46% of the total calls
received were repeat
calls
 All except application
tickets have configuration
issues
25%
23%
22%
18%
12%
Top 5 Category of Tickets with
Repeat Calls
User Access
Network
Software
Application
Teaching
Spaces
50%
19%
18%
7%
6%
Top 5 Category of Tickets with
Repeat Calls
Software
Application
User Access
Network
Hardware
23

24. Against the
Industry Global
Standards
HOW ARE YOU FARING? [Harmon et al., 2006]
Against Internal
Benchmark
 Acceptable repeat call
rate is at 10% [Rumburg, 2011]
 Repeat call KPI was not
set
 Yearly decline, repeat
call increased
24

25. PROPOSED
SOLUTION
25

26. Service Quality Matrix
Scale Importance & Weight
Customer Satisfaction Rate Percentage Medium | 10%
Interaction per Resolution Percentage Medium | 10%
Time to Resolution Days Medium | 10%
First Contact
Resolution
First Level
Resolution
Repeat Calls
Percentage
Percentage
Percentage
High | 30%
High | 20%
High | 20%
26

27. RECOMMENDATIONS
Moving forward.
27

28. IMPROVE KNOWLEDGE DATABASE
Self-Serve Online Library
◦ Addition of a Knowledge
Management Software
Benefits:
 Budget Friendly
 Adds Flexibility
 Consistent Service
 Higher Resolution Rate
 Lower Costs
28

29. ROOT-CAUSE ANALYSIS METHODOLOGY
Implement an Optimisation
Model to correlate incident
tickets that will be based on 3
criteria:
* Category-based correlation
* Correlating Configuration
items
* Augment Scheduled
Resource Data Collection
29

30. IMPROVE DATA QUALITY
Data quality must be
considered as part of Business
Strategy
Key Points to Consider:
 Use Statistical Analysis to
scrutinize data
 Devise staff training and
awareness program
 Minimize missing data
30

31. THANK YOU!
ANY QUESTIONS?
31

32. REFERENCES
Atkinson, R, 2013, ‘Metrics for the New World of Support’, UBM LLC, retrieved 23
September 2017, <http://www.thinkhdi.com/~/media/HDICorp/Files/White-
Papers/whtppr-1213-metrics-new-world-support.pdf>.
Firuta, J, 2017, ‘Customer Service Report 2017’, Livechat, weblog post, June 10, retrieved
10 September 2017, <https://www.livechatinc.com/livechat-resources/customer-service-
report-2017/>.
Harmon, E.P., Hensel, S.C. and Lukes, T.E., 2006. Measuring performance in services.
McKinsey Quarterly, 1, p.30.
Ivarson, J, 2013. ‘Quality Management for IT Support Services’, Master of Science Thesis
in Quality and Operations Management, Chalmers University of Technology, Sweden.
Navvia, 2014, ‘Incident Management for Stanford University’, Consulting Portal Inc..,
retrieved 23 September 2017,
<https://uit.stanford.edu/sites/default/files/2015/12/09/Incident_Management%20Docume
nt%20-%20Version%201.pdf>.
Narayanan Kumbakara, (2008) "Managed IT services: the role of IT standards", Information
Management & Computer Security, Vol. 16 Issue: 4, pp.336-359, https://doi-org.ezproxy-
b.deakin.edu.au/10.1108/09685220810908778

33. REFERENCES
Rumburg, J, 2011, ‘Metric of the Month: First Level Resoluttion’, MetricNet, retrieved 23
September 2017, <https://www.thinkhdi.com/~/media/HDICorp/Files/Library-
Archive/Insider%20Articles/First%20Level%20Resolution.pdf>.
Tomek Soluch, (2012), Helpdesk - taka a good advice, get the right device [ONLINE].
Available at: https://dribbble.com/shots/676690-Helpdesk-taka-a-good-advice-get-the-
right-device [Accessed 27 September 2017].
Trienekens, J.J., Bouman, J.J. and Van Der Zwan, M., 2004. Specification of service level
agreements: Problems, principles and practices. Software Quality Journal, 12(1), pp.43-57.