csb service delivery model review benchmarking report · 9 csb service delivery model review - 2019...

CSB Service Delivery Model Review

Benchmarking Report26th July 2019

CSB Service Delivery Model Review - 20192

7 Background and Approach

36 Insight Themes & Conclusions

3 Executive Summary

12 Current State Observations & Insights

CONTENTS

42 Appendix

3

Executive Summary


Benchmarking the CSB helps to provide insights around how it can improve its current state delivery model and thus suggest

some considerations for its future state.

Executive Summary | Benchmarking Objective and Assumptions

CSB’s primary activities are as a L1 and L2 IT support provider.

• Assume L3 support lies outside CSB

• Metrics that characterise whole-of-IT operations (e.g. system availability) will

not be the focus of benchmarking.

1

Where necessary inferences will be made from a sample set

• If a full population data set is not available, a sample inference has been

made

2

Definitions for metrics and their components are aligned

• As much as possible, metrics from benchmark sources and CSB data have

been aligned

• Wherever this was not possible but a comparison is still necessary,

differences between definitions or caveats have been stated

3

ASSUMPTIONS

The primary objective of the benchmarking exercise is to

help identify key opportunities for improvement within

CSB’s current service delivery model (SDM) and suggest

the implications they have on the future state SDM.

OBJECTIVE

Insight accuracy is dependent on data accuracy

• Insights drawn from metrics where data quality/accuracy issues have been

identified, should have the same level of accuracy attached.

4

APPROACH

The benchmarking exercise has been

conducted by building observations and

insights about the current state and

leveraging them to form conclusions on

what could be incorporated to improve in

the future state.

Observations

Insights

Conclusions

What does the data show?

What trends can be observed?

What might be the root cause for

this observation to occur?

What could be done in the future

state to address this insight?


Six dimensions across service demand and supply have been used to structure current state observations and insights.

Executive Summary | Insight Dimensions

Service Demand DSC Performance DSC Capability DSC Capacity DPT Performance Customer Satisfaction

INT

EN

T

Seeks to understand how

demand for the CSB’s

services has been evolving

and contextualise it against

industry peers.

Seeks to provide insight into

the DSC’s ability to meet

current demand for its

services.

Seeks to understand the

DSC’s level of capability to

resolve customer contacts.

Seeks understand if the

DSC’s resource capacity is

adequate to meet service

demand.


DPT’s ability to meet

service demand and if

resource capacity is

adequate.

Seeks to identify if the

CSB’s customers are

satisfied with the level of

service being provided.

ME

TR

ICS

• Total tickets received by

the CSB

• HHS breakdown of

tickets received

• Tickets received per End

User

• Tickets resolved by the

CSB

• Average speed to

answer (call wait times)

• Abandonment Rate

• First Contact Resolution

Rate

• First Level Resolution

Rate

• Average Handling Time

• Agent Utilisation • Tickets resolved by the

DPT

• Mean Time to Resolve

• Customer Satisfaction

• Incidents resolved within

SLAs

INSIGHT DIMENSIONS

Demand Analysis Supply AnalysisCustomer Outcome

Analysis


Based on the CSB’s metrics in comparison to benchmarks, six key conclusions have been developed around how the CSB

may aim to actively reduce workload on its teams whilst improving its effectiveness and optimisation of capacity. No

conclusions could be drawn about customer satisfaction due to limitations in the data.

Executive Summary | Insights and Conclusions

DSC appears to need additional capacity

particularly around peak periods

DPT may need to reorganise capacity towards

resolving ticket related tasks

CSB customer satisfaction appears to be good

although there are limitations in the data

Demand for DSC services is stable but well below

benchmark averages

DSC workload appears to be higher than its

resource capacity or capability.

DSC appears to be not as effective at resolving

contacts compared to benchmarks

Develop problem management capabilitiesThe CSB should consider opportunities to introduce problem management to identify and address underlying

problems in order to reduce incidents (and therefore service demand).

1

Continue to AutomateThe CSB should continue to automate or divert (e.g. self-service) low complexity, transactional tasks to reduce

service demand on DSC agents.

2

Improve knowledge management and quality of trainingConsider improving usage of tools, knowledge management and quality of training for DSC agents in order to be

more effective at resolving tickets at the first level and first contact.

3

Focus DPT CapacityThere may be opportunities to deploy DPT capacity in order to assist with service demand management strategies

or with some of the first level workload.

4

Additional call handling resources around peak periodsAdditional call handling resources might be needed in order to handle current service demand particularly around

peak periods (depending on the efficacy of the previous recommendations).

5

Improve cross CSB and eHealth collaborationCloser collaboration between CSB teams and the wider eHealth organisation may be needed to optimise and

improve customer experience, by reducing ticket handoffs and thus delays to resolve customer tickets.

6

INSIGHT THEMES CONCLUSIONS

7

1 | Background and Approach


Benchmarking the CSB helps to provide insights around how it can improve its current state delivery model and thus suggest

some considerations for its future state.

Background and Approach | Benchmarking Objective and Assumptions

CSB’s primary activities are as a L1 and L2 IT support provider.

• Assume L3 support lies outside CSB

• Metrics that characterise whole-of-IT operations (e.g. system availability) will

not be the focus of benchmarking.

1

Where necessary inferences will be made from a sample set

• If a full population data set is not available, a sample inference has been

made

2

Definitions for metrics and their components are aligned

• As much as possible, metrics from benchmark sources and CSB data have

been aligned

• Wherever this was not possible but a comparison is still necessary,

differences between definitions or caveats have been stated

3

ASSUMPTIONS

The primary objective of the benchmarking exercise is to

help identify key opportunities for improvement within

CSB’s current service delivery model (SDM) and suggest

the implications they have on the future state SDM.

OBJECTIVE

Insight accuracy is dependent on data accuracy

• Insights drawn from metrics where data quality/accuracy issues have been

identified, should have the same level of accuracy attached.

4

APPROACH

The benchmarking exercise has been

conducted by building observations and

insights about the current state and

leveraging them to form conclusions on

what could be incorporated to improve in

the future state.

Observations

Insights

Conclusions

What does the data show?

What trends can be observed?

What might be the root cause for

this observation to occur?

What could be done in the future

state to address this insight?


IT support can be broken into service demand and service supply. When these factors are balanced, positive customer

outcomes can reached and optimal performance will be seen in metrics.

Background and Approach | Analysing IT Support Metrics

Service Demand

Service DemandWorkload from customers/end-users

that IT support needs to service

irrespective of its source or form.

Service Supply

PerformanceIT support’s ability to provide an adequate level of service that meets demand for its services. Consists of an

organisation’s capability and capacity.

CapabilityIT support’s efficacy in processing customer

issues. Product of skills, processes, experience,

knowledge, tools and collaboration within the

organisation.

Capacity IT support’s baseline level of resources that is

delivering services to customers. Includes

optimising the utilisation of resources.

Customer Outcome

Customer SatisfactionCustomer’s satisfaction with IT

support, first level support and IT as a

whole organisation.

Over-performing • Over-resourced or over-skilled organisation

• Positive customer outcomes

Optimally performing• Optimally resourced and skilled organisation

• Positive customer outcomes

Under-performing • Under-resourced or under-skilled organisation

• Negative customer outcomes

Cost/Price per

Contact

Quantity of Contacts

“Optimal” performance point

Service Supply

Quantity of Contacts

Price per

Contact

Service Demand OutcomesMatched against supply

Underperforming

Over-performing


Six dimensions across service demand and supply have been used to structure current state observations and insights.

Background and Approach | Insight Dimensions

Service Demand DSC Performance DSC Capability DSC Capacity DPT Performance Customer Satisfaction

INT

EN

T

Seeks to understand how

demand for the CSB’s

services has been evolving

and contextualise it against

industry peers.

Seeks to provide insight into

the DSC’s ability to meet

current demand for its

services.


DSC’s level of capability to

resolve customer contacts.

Seeks understand if the

DSC’s resource capacity is

adequate to meet service

demand.


DPT’s ability to meet

service demand and if

resource capacity is

adequate.

Seeks to identify if the

CSB’s customers are

satisfied with the level of

service being provided.

ME

TR

ICS

• Total tickets received by

the CSB

• HHS breakdown of

tickets received

• Tickets received per End

User

• Tickets resolved by the

CSB

• Average speed to

answer (call wait times)

• Abandonment Rate

• First Contact Resolution

Rate

• First Level Resolution

Rate

• Average Handling Time

• Agent Utilisation • Tickets resolved by the

DPT

• Mean Time to Resolve

• Customer Satisfaction

• Incidents resolved within

SLAs

INSIGHT DIMENSIONS

Demand Analysis Supply AnalysisCustomer Outcome

Analysis


Background and Approach | Metric Definitions

Metric Definition

Benchmarked

Metrics

Tickets received per end-user (TPEU) Average number of tickets received per end-user over a year period

Average Handling Time (AHT) Average duration of agent handling time from connecting with customer until end of “after call work” (e.g. inclusive of both).

Average Speed to Answer (ASA)Average duration between when customer connects to the IT Service Desk (e.g. via IVR) and when a live agent picks up.

Averaged over all incoming handled calls.

Abandonment Rate (AR) Percentage incoming calls that hang up or disconnect before they are answered

First Level Resolution Rate (FLR) Percentage of total tickets resolved by the DSC

First Contact Resolution (FCR)Percentage of DSC tickets received from customers that are resolved upon initial contact (excludes any type of hand-off

including warm or blind). Initial contact is defined as tickets resolved within 1 hour and with no hand-offs.

Mean Time To Resolve (MTTR)Elapsed time taken to resolve an incident within ticketing system (resolve is defined as close) which requires the assistance

of an on-site technician (e.g. DPT).

Agent Utilisation Average proportion of handle time to time spent on shift for each DSC agent.

Incidents resolved within SLA agreed

timeframesProportion of total tickets resolved within SLA timeframes as specified within ServiceNow

Customer SatisfactionPercentage of satisfied or very satisfied responses to end-of-ticket surveys (e.g. sum of satisfied and very satisfied

responses).

Annual Contacts per DSC FTE Total annual inbound contacts handled by DSC agents per DSC FTE

Supporting

Metrics

Total tickets resolved by CSB/DPT Total tickets that are resolved by the CSB/DPT over a 3 year period.

HHS breakdown of tickets received

Comparison of non-ieMR and ieMR HHS service demand, after removing effects of user growth (ieMR HHS defined as

having at least, one hospital which has had a full stack ieMR rolled out, non-ieMR does not have a hospital with full stack

ieMR rolled out).

Total tickets received by CSB Total tickets that are received by the CSB over a 3 year period. Breakdown is an estimate only.

12

2 | Current State Observations & Insights


Service Demand


Demand for CSB services across teams and channels (including the DSC and DPT) has been stable across the past 2 years.

Tickets received by the CSB

BENCHMARK DATA

• Volumes of tickets received for CSB services has been

relatively stable over the past 2 years (since January

2017)

• This trend is consistent across all channels

including the DSC and DPT

• There is a clear annual cycle to the volume of tickets

received

• Significant drop-off in volumes occur in December

of each year

CURRENT STATE OBSERVATIONS

CURRENT STATE INSIGHTS

Overall demand for CSB services has been relatively

stable across channels and teams

• The introduction of new systems and services do not

appear to have affected the overall volume of tickets

being received by the CSB

Demand for DPT’s services has been relatively stable over

the last 2 years, which means that DPT’s overall ticket

based workload has not changed. Further analysis (e.g.

from problem management) may be required to continue

control of overall demand.

• Regular end user device hardware refresh cycles may

possibly be helping to limit growth in demand for DPT

services.1 CSB Data | ServiceNow Ticket Extract Received 29/4/2019 from CSB Manager, Reporting and Analysis, SM&I

* incl. OPS, Portal, Auto resolved (e.g. Amazon Connect), Email and OITS

0

20,000

40,000

60,000

80,000

100,000

120,000

Jan-16 Jan-17 Jan-18 Jan-19M

onth

ly T

icket V

olu

mes R

eceiv

ed b

y C

hannel

Historical Volume of Total CSB Tickets Received (Opened)1

DSC (estimated) DPT (estimated) Online * (estimated) Total CSB

Date when ticket

opened

Note that breakdowns are estimates only.

Note: See

Appendix for detail

as to how the

ServiceNow ticket

data was used to

generate the

graphs shown.


Introduction of full ieMR to a HHS does not appear to have a significant impact on the service demand for the CSB’s services

from the relevant HHS.

HHS breakdown of tickets received (1 of 2)

BENCHMARK DATA

• There is no distinguishable change in tickets received

resulting from the introduction of a full stack ieMR hospital

to a HHS.

• Metro South HHS appears to have a notable

increase in total tickets received (this is explored

further on page 16).

• There is no significant patterns that distinguish a non full

stack ieMR HHS’s tickets received volume over time

compared to an ieMR HHS’s tickets received over time.

• Mackay and Cairns & Hinterland HHSs have been selected

as examples since they receive similar volumes of tickets



ieMR has no discernible impact on the overall volume of

tickets received from a HHS, meaning it is unlikely that the

introduction of ieMR to new HHSs will significantly

change workload for the CSB.

• Tickets received is not by itself, a direct measure of

workload for the CSB however,

• Data suggests that there should not be a

significant peak in volumes as a result of

ieMR.

• Relatively low volumes of ieMR tickets

received may not affect CSB workload since it

is not the primary responsible resolver1 CSB Data | ServiceNow End-of-Ticket Survey Results Extracts Received 29/4/2019 from CSB Manager, Reporting and

Analysis, SM&I

Mackay Average Decline per year (Jan 17 to Mar 19) : ~ 4.86%

Cairns Average Decline per year (Jan 17 to Mar 19) : ~ 1.35%

0

1,000

2,000

3,000

4,000

5,000

6,000

Jan-17 Jan-18 Jan-19

Month

ly T

icket V

olu

mes R

eceiv

ed f

rom

Cairns &

H

inte

rland H

HS

Non full-ieMR Example HHS (Cairns & Hinterland) - CSB Received Tickets1

0

500

1,000

1,500

2,000

2,500

3,000


Month

ly T

icket V

olu

mes R

eceiv

ed

from

Mackay

HH

S

Full ieMR Example HHS (Mackay) - CSB Received Tickets1


0

2,000

4,000

6,000

8,000

10,000

12,000

14,000


Month

ly T

icket V

olu

mes R

eceiv

ed f

rom

Metr

o S

outh

H

HS

Full ieMR HHS (Metro South) - CSB Received Tickets1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


Month

ly T

icket V

olu

mes R

eceiv

ed p

er

End U

ser

from

M

etr

o S

outh

HH

S

Full ieMR HHS (Metro South) - CSB Received Tickets per End User1

Although the Metro South HHS has seen an increase in volume of tickets received, this is likely due to the rise in the number

of end users rather than due to the introduction of new ieMR systems.

HHS breakdown of tickets received (2 of 2)

BENCHMARK DATA

• There has been an average increase in volume of

tickets received from Metro South HHS of 10.36% since

January 2017

• Multiple ieMR hospitals in Metro South HHS have

gone live during this time period.

• There has been no change in the number of tickets

received per end-user from Metro South HHS since

January 2017



An increase in the number of end-users is likely the

primary driver for the increase in volume of tickets

received from the Metro South HHS.

• An ieMR implementation could contribute to an

increase in demand if there is a corresponding

increase in end users

• The change in volume of tickets received from

Metro South has been gradual

• Seasonal spikes (as noted on page 14)

appear to have a more significant impact

than periods when an ieMR

implementation has “gone live”.

1 CSB Data | ServiceNow End-of-Ticket Survey Results Extracts Received 29/4/2019

Metro South Average Increase per year (Jan 17 to Mar 19) : ~ 10.36%

Metro South Average Increase per end user per year (Jan 17 to Mar 19) : ~0%


The DSC has been receiving a relatively low demand for its services compared to benchmarks. This may be due to the

implementation of relatively reliable systems.

DSC Tickets received per End User

BENCHMARK DATA

• The DSC’s tickets per end-user (TPEU) is well below the

benchmark 25th percentile

• The DSC is receiving a decreasing TPEU which has been

largely driven by a decrease in DSC TPEU

• DSC TPEU rate of decline has reduced since

January 2017 until present



Demand for DSC services is low compared to industry

benchmarks and is relatively stable, possibly resulting

from the implementation of relatively reliable systems.

• Relatively reliable of supported systems means

that users need to raise relatively fewer tickets

• This may be due to the timely removal of

problematic legacy systems and implementation of

more evergreen solutions (e.g. such as Office 365

and Windows 10).

• The CSB likely has a relatively high number of

“latent users” (e.g. users who need to engage with

IT support on a less regular basis) compared to

benchmark organisations

1 Benchmark Data | Gartner - 2019 - IT Key Metrics Data 2019 Key Infrastructure Measures IT Service Desk Analysis2 CSB Data | ServiceNow Ticket Extract Received 10/4/2019 & End User numbers Received 24/4/2019 both from CSB

Manager, Reporting and Analysis, SM&I

0

5

10

15

20

25

Serv

ice D

esk E

ncounte

rs p

er

User

per

Year

DSC Service Desk Tickets per End-User1,2

DSC Average

(Mar 18 to Mar 19)Benchmark AverageInter-Quartile

Range

19.4 – Est. 75th Percentile

8.3 – Est. 25th Percentile

15.9 - Median

3.59

0

1

2

3

4

5

6

7

Jan-16 Jan-17 Jan-18 Jan-19

Tic

kets

Receiv

ed p

er

End U

ser

History of DSC Tickets Received per End-User (Estimate for Year)1


0

20,000

40,000

60,000

80,000

100,000

120,000

140,000


Tic

kets

resolv

ed b

y C

SB

(to

tal)

Tickets resolved by the CSB (Monthly Totals)1

CSB incl. Auto CSB excl. Auto

Date when ticket

resolved

CSB tickets resolved by live agents has been dropping over the previous 3 years – likely due to the implementation of more

automated systems.

Tickets resolved by the CSB

BENCHMARK DATA

• CSB tickets resolved by a live agent (e.g. DPT, DSC or

SMI), has been decreasing over the previous 3 years

• Tickets auto resolved (e.g. OPS, automated

password resets) have been increasing



Volume of live agent resolved tickets has been

decreasing, possibly resulting from automation of some

services and a potential decline in effectiveness of live

agents.

• Introduction of automation, streamlined processes

and other service management improvements

means tickets are resolved by methods other than

live agents

• Additionally live agents may be becoming less

effective as automation leaves only higher

complexity, non-transactional contacts

• The decrease in resolved tickets over the past 3

years is in line with decreasing demand (see page

14).

1 CSB Data | ServiceNow Ticket Extract Received 10/4/2019 from CSB Manager, Reporting and Analysis, SM&I


DSC Performance


0

100

200

300

400

500

600

Incom

ing C

onta

ct

Avera

ge S

peed t

o A

nsw

er

(s)

Average Speed to Answer (ASA)1,2

Although average speed to answer has been sharply decreasing over the previous 12 months, on average it has been

significantly higher than benchmarks suggesting that there may be capacity or effectiveness issues within the DSC.

Average Speed to Answer

434.3 s

564.3 s

BENCHMARK DATA

46 s - Average

DSC 24/7 Average

(Mar 18 to Mar 19)

Benchmark Average

63.62 s - 75th Percentile


DSC Core Hours

Average

(Mar 18 to Mar 19)

• The DSC’s inbound call average speed to answer (ASA)

is much higher than benchmark maximums and

averages (~8-10 times the benchmark average)

• The DSC’s ASA has been relatively steady between

December 2016 and February 2018 but has seen a sharp

increase between April 2018 and July 2018.

• From July 2018 to present there has been a sharp

decline in ASA, improving approximately 33.2% (when

incorporating callbacks as “0” waiting time).



High ASA indicates either resource capacity issues or

first level effectiveness issues within the DSC.

• High wait times suggests the DSC does not have

adequate resources or effectiveness to ensure it

can support the demand it is receiving

Customer wait times have been decreasing since July

2018, potentially due to the implementation of call-backs

and automation of low complexity, transactional contacts.

• The CSB has been successful in leveraging

automation to reduce the volume of customers

handled by DSC agents.

0

100

200

300

400

500

600

700


DS

C A

vera

ge S

peed t

o A

nsw

er

(s)

Historical DSC Average Speed to Answer (24/7 Average)2

1 Benchmark Data | Gartner - 2019 - IT Key Metrics Data 2019 Key Infrastructure Measures IT Service Desk Analysis2 CSB Data | Amazon Connect Extracts Received 10/4/2019 from CSB Manager, Reporting and Analysis, SM&I

Inter-Quartile

Range


0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Incom

ing C

onta

ct

Abandonm

ent

Rate

(%

)

Abandonment Rate (AR)1,2

The DSC’s abandonment rate is significantly higher than benchmarks. This result correlates with higher than benchmark ASA

and supports the insight that the DSC is facing challenges to meet demand for its services.

Abandonment Rate

34.40 %

40.86 %

BENCHMARK DATA

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%


Incom

ing C

onta

ct

Abandonm

ent

Rate

(%

)

Historical DSC Average Abandonment Rate (24/7 Average)2

7 % - Average

8.92 % - 75th Percentile


DSC 24/7 Average

(Mar 18 to Mar 19)

Benchmark AverageInter-Quartile

Range

DSC Core Hours

Average

(Mar 18 to Mar 19)

• The DSC’s inbound call abandonment rate (AR) is much

higher than benchmark maximums and averages (~6-7

times the benchmark average)

• Although it rapidly increased between December 2016 and

March 2017, following this period the DSC’s AR has

stabilised around its current average



The DSC’s high abandonment rate is likely being driven

by its high call wait times, potentially during peak periods

• Benchmarks indicate that in general there should

be a near proportional correlation between ASA

and AR (~7.19 seconds ASA per % AR).

• For reference the DSC’s current ratio is 9.1

seconds ASA per % AR indicating that there is

good correlation between ASA and AR

• Further investigation is required to quantify AR and

ASA during peak periods (and compare to non-

peak periods).

1 Benchmark Data | Gartner - 2019 - IT Key Metrics Data 2019 Key Infrastructure Measures IT Service Desk Analysis2 CSB Data | Amazon Connect Extracts Received 10/4/2019 from CSB Manager, Reporting and Analysis, SM&I


DSC Capability


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

First Level R

esolu

tion R

ate

(%

)

First Level Resolution (FLR) Rate1,2

The CSB’s first level resolution rate is relatively low compared to benchmark averages and has been steadily decreasing

over the past 12 months suggesting DSC agents aren’t adequately equipped to resolve contacts they are encountering.

First Level Resolution Rate

48.65 %

BENCHMARK DATA


73 % - 25th Percentile

77.3% - Average

• The CSB’s first level resolution rate (FLR) is

significantly lower than benchmark minimums

• CSB FLR has been relatively steady from July 2016 to

December 2017

• The CSB’s FLR has been steadily declining over the

previous 16 months (from January 2018 to Present).



DSC agents potentially do not have the optimum training,

knowledge or tools usage to resolve tickets compared to

industry peers.

• Peer organisations appear to have a greater

capability to resolve tickets at the first level

compared to the DSC

• DSC agents may not be as adequately equipped

through tools or training/knowledge to service

customers as a peer level 1 agent

1 Benchmark Data | MetricNet 2014 Service Desk Benchmark Data 2 CSB Data | ServiceNow Ticket Extract Received 10/4/2019 from CSB Manager, Reporting and Analysis, SM&I

DSC Average

(Mar 18 to Mar 19)Benchmark AverageInter-Quartile

Range

0%

10%

20%

30%

40%

50%

60%

70%


First Level R

esolu

tion R

ate

(%

)

Historical CSB First Level Resolution Rate over Time2


The DSC’s first contact resolution rate is relatively low compared to benchmark averages and has been steadily decreasing

over the past 12 months although this may be due to a combination of ServiceNow configuration and auto-password resets

First Contact Resolution Rate

BENCHMARK DATA

• The DSC’s first contact resolution rate (FCR) is below

benchmark averages assuming zero re-allocations in the

Service Now data.

• ServiceNow is currently configured to report a re-

allocation when the auto-generated assignment is

changed by the first DSC call agent

• Taking into account the potential re-allocation prior to

first contact, and assuming a time of 15 minutes is a

reasonable proxy for First Contact Resolution,

increases the apparent FCR by close to 20%.

• The DSC’s FCR has been declining since April 2018

regardless of this ServiceNow configuration feature.



DSC agents potentially do not have the optimum training,

knowledge or tool usage to quickly diagnose and resolve

customer incidents compared to industry peers. This trend

is exacerbated by the use of automation removing tickets

that are more straightforward to resolve at first contact.

• If agents are unable to resolve contacts at first interaction

they may need to hand off tickets to other levels of support

which do have the training, knowledge or tools to resolve

tickets.

• Reconfiguration of ServiceNow may make service

performance data more comparable with peer benchmarks

in future.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

First Level R

esolu

tion R

ate

(%

)

First Contact Resolution (FLR) Rate1,2,3

49. 5%


73 % - 25th Percentile

77.3% - Average

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%


DS

C F

irst C

onta

ct

Resolu

tion R

ate

(%

)

Historical DSC First Contact Resolution Rate2

FCR (15 Minutes, 0 - 1 re-allocations)

FCR (all durations, 0 re-allocations)

Auto Password Resets

75%

DSC Average Mar 18 - Mar 19

(0 re-allocations)

Benchmark Average

Inter-Quartile Range

DSC Average Mar 18 to Mar 19

(0 - 1 re-allocations)

Note that for dates prior to June 2017 the FCR data source is different from

the current FCR data source and thus cannot be meaningfully compared

1 Benchmark Data | Gartner - 2019 - IT Key Metrics Data 2019 Key Infrastructure Measures IT Service Desk Analysis2 CSB Data | ServiceNow Ticket Extract Received 10/4/2019 from CSB Manager, Reporting and Analysis, SM&I3 Benchmark Data | Computer Economics 2018 – Help Desk Staffing Ratios – Healthcare Industry


The DSC’s first contact resolution rate appears low when applying the strict definition of FCR due largely to the way in which

ServiceNow is configured.

First Contact Resolution Rate – ServiceNow Configuration

User calls DSC

IVR to direct call

DSC agent receives

call

Agent raises ticket

using template

Template based

allocation occurs

DSC agent re-

allocates back to

DSC and resolves

Can

DSC

resolve

?

Ticket allocated to

appropriate group

Re-allocation count = 1

Clock starts

Clock stops

Non-DSC

DSC agent resolves

Re-allocation count = 0

Clock stopsDSC

Both scenarios should fall into the FCR

category but only the re-allocation count = 0

would under the strict definition of FCR (shown

as FCR(all durations, 0 re-allocations) in slide

24.

When a ticket is raised in ServiceNow, the DSC agent chooses a template on

which to base the ticket. The templates are configured such that the ticket is auto-

allocated to a support team based on the template chosen.

However, in some instances, the DSC agent may be able to resolve the ticket and

so re-assigns the ticket back to DSC. This results in an apparent re-allocation

count of 1 in the ServiceNow data which does not accurately represent the path

the ticket has taken.

To obtain a realistic view of FCR, such tickets should also be included in the FCR

count. However, a re-allocation count of 1 may also be a valid re-allocation rather

than due to the feature described above. It is unlikely, however, that such genuine

cases would be resolved in a short timeframe.

To this end, tickets with a re-allocation count of 0 or 1 and a resolution time of 15

minutes or less have been included in the FCR (15 minutes, 0 – 1 re-allocations)

data set shown in slide 24.

Reconfiuration of ServiceNow to remove template based auto-allocation may

make DSC’s FCR data more readily comparable with industry bencharks in future.

YesNo


0

100

200

300

400

500

600

700

800

Inbound A

vera

ge H

andlin

g T

ime (

s)

Average Handling Time (Inbound)1, 2

The DSC’s average handling time is below benchmark averages but has been increasing over the last 3 years. This

correlates with the low FCR - agents are unable to resolve jobs at first contact and thus need to hand off relatively quickly.

Average Handling Time

441.7 s

BENCHMARK DATA

462.3 s

544.8 s - Average



• The DSC’s inbound call average handling time (AHT) is

below the benchmark 25th percentile but above

minimums

• The DSC’s AHT has been gradually increasing over the

last 3 years (since October 2016)



DSC agents are handling contacts faster than

benchmarks potentially because they are not able to

resolve customer contacts at the same rate as peers.

• Combined with a lower than benchmark FLR and

FCR, a lower than benchmark AHT suggests

agents are spending less time on calls as they are

handing them off to other resolver groups

Over the long term the DSC agents are handling more

time consuming, non-transactional contacts.

• Non-transactional tasks such as trouble-shooting

are innately more time consuming due to additional

complexity to resolve

The dip in AHT from September 2018 may be due to the

implementation of callbacks.

• Callbacks may be shorter in duration because end-

users are self-helping (and no longer need DSC

assistance) or callbacks are reaching voicemail.

0

100

200

300

400

500

600


Avera

ge H

andlin

g T

ime (

s)

Historical DSC Average Handling Time (24/7 Average)2

1 Benchmark Data | MetricNet 2014 Service Desk Benchmark Data 2 CSB Data | Amazon Connect Extracts Received 10/4/2019

DSC 24/7 Average

(Mar 18 to Mar 19)

Benchmark AverageDSC Core Hours

Average

(Mar 18 to Mar 19)

Inter-Quartile

Range


DSC Capacity


Benchmark Average

Inter-Quartile

Range

DSC Average

Utilisation

(Estimated)

(Mar 19)

0%

10%

20%

30%

40%

50%

60%

70%

80%

Agent

Utilis

ation p

er

day (

%)

Agent Utilisation1,2

The DSC is over-utilising its agents compared to industry benchmarks – the average DSC agent utilisation is above the

maximum benchmark range. This suggests the DSC may find it difficult to further optimise utilisation of its resources.

Agent Utilisation

BENCHMARK DATA

55.7 %Average

62.3% - 75th Percentile


74.45 %

• The DSC’s agent utilisation is much higher than the

benchmark average and above the benchmark range

maximum.



The DSC may not have additional capacity to handle

incoming service demand.

• Individual agent utilisation cannot be increased in

order to better handle service demand without

further exceeding benchmark maximums

• More agents may be required to adequately handle

incoming service demand

High agent utilisation in the DSC may drive higher

turnover rates resulting in the loss of knowledge. This

could contribute to the decreasing FCR observed earlier.

• MetricNet analysis3 suggests that agent utilisation

should be directly proportional to staff turnover

• High staff turnover results in tacit knowledge and

experience leaving the organisation whilst bringing

fresh replacement agents who are less efficient or

capable of resolving contacts (hence lower FCR).

1 Benchmark Data | MetricNet 2014 Service Desk Benchmark Data2 CSB Data | DSC Roster (31 Dec 18 to 30 Jun 19) & DSC Agent Handle Times (Mar 19) both received 15/4/2019 from

DSC FAMMIS Team Leader3 MetricNet “The Seven Most Important Performance Indicators for the Service Desk”, Jeff Rumburg & Eric Zbikowski


DPT Performance


Date when ticket

resolved

The DPTs have been resolving a constant volume of tickets over the past 3 years. Combined with the relatively constant

volume of tickets received by the DPT, this indicates that the DPT is performing well under the workload.

Tickets resolved by the DPT

BENCHMARK DATA

• Total DPT resolved ticket volumes have remained

relatively constant over the previous 3 years

• There has been a slight incline since January

2017, however this increase is not significant,

particularly when compared to the overall volume

of tickets resolved by the CSB (see page 18).



Given that both tickets resolved and received volumes

have remained constant, this implies there has not been

any significant accumulation of tickets in the DPT’s

backlog.

• If DPTs are resolving tickets at the same rate as

they are receiving them, its backlog should not be

growing significantly.

1 CSB Data | ServiceNow Ticket Extract Received 10/4/2019 from CSB Manager, Reporting and Analysis, SM&I

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000


Tic

kets

resolv

ed b

y C

SB

(to

tal)

Tickets resolved by the CSB and DPT (Monthly Totals)1

CSB incl. Auto DPT Only


Benchmark AverageInter-Quartile

Range

0

20

40

60

80

100

120

140

160

180

200

Mean T

ime to R

esolu

tion (

Busin

ess H

ours

)

Mean Time to Resolution (On-Site Incidents)1,2

The DPT’s mean time to resolution is well above benchmark maximums. This indicates that the DPT may be spending more

time on non-ticket related tasks compared to peer organisations – although data quality issues may be skewing the metric.

Mean Time To Resolve

BENCHMARK DATA

17.8 h - 75th Percentile

3 h - 25th Percentile

8.42 h - Average

• The DPT’s mean time to resolve incidents (MTTR)

appears to be far above benchmark averages and

maximums

• The DPT’s MTTR has been gradually increasing from

September 2016 to present.



The DPT’s increasing and relatively high MTTR, indicates

there could be delays in DPT coordinating resolution with

other support teams or that the DPT is spending time on

non-ticket tasks.

• DPT MTTR is calculated from tickets the DPT resolves,

regardless which group it is raised with first, i.e. averages

are calculated inclusive of time taken for the ticket to

eventually reach a DPT resolver group.

• The DPT reports an average 1.48 hours effort to

resolve a ticket (as opposed to elapsed time).

• It is possible the DPT is experiencing delays (as per

page 30) and, anecdotally, this may be due to the

recent CWP rollout although such work is reported

as being supported by additional resources so

should not represent a resource constraint.

• Data quality could be compromised if there are large

numbers of unclosed tickets. This may be obscuring a view

of additional available DPT capacity.DPT Filtered (< 30

day) Average (Mar 18

to Mar 19)

DPT Unfiltered

Average (Mar 18 to

Mar 19)

175 h

107.05 h

1 Benchmark Data | MetricNet 2012 Service Desk Benchmark Data2 CSB Data | ServiceNow Ticket Extract Received 10/4/2019 from CSB Manager, Reporting and Analysis, SM&I

1.48 h - Average DPT

Effort to Resolve0

50

100

150

200

250

Jan-17 Aug-17 Feb-18 Sep-18 Mar-19

Mean T

ime to R

esolv

e I

ncid

ents

(h)

Historical DPT MTTR (Incidents)2


The much higher MTTR than peer benchmarks could be due to a number of potential causes

Mean Time To Resolve – Discussion

Ticket allocated to

DPT

DPT investigate

ticket

DPT resolve incident

Ticket resolved and

recorded as closed

Does

DPT

close

ticket?

Ticket resolved but

not recorded as

closed

Clock running

Non-DSC Clock stops

Potential Cause – Late Ticket Closure

A ticket may be resolved but not closed by the DPT. This restores service

for the affected user(s) but this restoration is not recorded in ServiceNow.

At some indeterminate point in future, the ticket is closed but this

extended period significantly skews the MTTR result which is based on

the period between ticket opening and closing.

YesNo

Clock continues

running

Potential Cause – DPT Utilisation

The increase in MTTR may also be due to DPT staff being engaged in Fee

for Service (FFS) work. Such work may not engage external resources to

either undertake the work or to backfill the DPT resources. This would

reduce the DPT capacity available to resolve tickets and thus increase the

average MTTR. Anecdotally, however, it is reported that significant FFS is

usually supported with external resources. As such, the FFS work should

not represent a significant additional impost on DPT capacity.

Summary

The ServiceNow data does not support analysis of the extent to which

either of these potential causes may be the root cause of high MTTR.

Further analysis is therefore required to ascertain the reason for CSB’s

MTTR being so much higher than peer benchmarks.


Customer Service


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Custo

mer

Satisfa

ction (

positiv

e r

esponse,

%)

Customer Satisfaction1,2

CSB customer satisfaction appears to be above benchmark averages – however better data quality is required in order to

draw more accurate insights about customer satisfaction.

Customer Satisfaction

BENCHMARK DATA

78.2 % - Average

84.8% - 75th Percentile


89.77%

86.87%

• The DSC and eHealth’s overall customer satisfaction is

above the 75th percentile but less than benchmark

maximums

• From the data the CSB has provided Customer

Satisfaction appears to be relatively stable across the

previous 12 months



The CSB customer satisfaction data may not reflect true

customer sentiment due to the higher than average

abandonment rate – customers who have abandoned

calls are likely not satisfied.

• This conclusion aligns with workshops and

interviews with a broad range of CSB stakeholders

• There should be strong correlation between ASA

and customer satisfaction

• Industry trends demonstrate an expected drop-off

in customer satisfaction when the ratio of ASA on

AHT surpasses ~10% (MetricNet)

• For reference the CSB’s ASA on AHT ratio as of

April 2019 is close to 48% DSC Average

Satisfaction

(Dec 18 to Jun 19)

Benchmark AverageeHealth Average

Satisfaction

(Dec 18 to Jun 19)

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

May-18 Jul-18 Aug-18 Oct-18 Dec-18 Mar-19

Custo

mer

Satisfa

ction (

% +

ve)

Recent History of Customer Satisfaction2

DSC Average Customer Satisfaction

eHealth Average Customer SatisfactionInter-Quartile

Range1 Benchmark Data | MetricNet 2014 Service Desk Benchmark Data 2 CSB Data | ServiceNow End-of-Ticket Survey Results Extracts Received 5/4/2019 from CSB Review Senior BA


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Perc

enta

ge o

f In

cid

ents

Resolv

ed w

ithin

SLA

Tim

efr

am

es

(%)

Incidents Resolved within SLA timeframes1,2

CSB Average

The CSB is resolving incidents within SLA timeframes set in ServiceNow indicating it is meeting agreed customer

expectations – although this depends on priority reporting consistency and leniency of SLAs compared to industry.

Incidents resolved within SLA Timeframes

BENCHMARK DATA

• The CSB is resolving incidents within SLA timeframes at

a rate within benchmark upper and lower bounds (no

median/average available).

• The CSB has been resolving incidents within SLA

timeframes at a steady rate over the last 12 months,

although it has been slightly decreasing



The CSB appears to be resolving incidents within the SLA

timeframes set in ServiceNow.

• CSB resolver groups appear to be effectively

resolving incidents according to customer

expectations.

• SLAs defined according to incident priority level

entered into ServiceNow – SLAs are met if tickets

are closed within timeframe associated to priority

level. The CSB’s performance against benchmarks

therefore depend on,

• Consistency with which tickets are

assigned priority

• Alignment of CSB SLAs with industry SLAs

• ServiceNow ticket SLAs differ from SLAs applying

to DSC performance (e.g. for AHT etc.).

88.6 % CSB Average

(Apr 18 to Apr 19) 90 % - Upper

Bound

81 % - Lower

Bound

1 Benchmark Data | Deloitte Sourced Industry Benchmark (2018)2 CSB Data | DSC Roster (31 Dec 18 to 30 Jun 19) & DSC Agent Handle Times (Mar 19) both received 15/4/2019 from

DSC Analytics

Benchmark Range

LimitsBenchmark

Range

40%

50%

60%

70%

80%

90%

100%

Apr-18 Jul-18 Oct-18 Jan-19

Perc

enta

ge o

f In

cid

ents

Resolv

ed w

ithin

SLA

T

imefr

am

es (

%)

Recent History of Incidents Resolved within SLA Timeframes2

36

3 | Insight Themes & Conclusions


The following insight themes have been drawn for each dimension, from the range of insights gathered by benchmarking the

CSB against its industry.

Insight Themes & Conclusions | Insight Themes (1 of 2)

SUPPORTING INSIGHTSINSIGHT DIMENSION & THEME

• Overall demand for DSC services is stable (Total tickets)

• Demand for DPT and overall CSB services is also relatively stable

• There is no observable impact of introducing ieMR on a HHS’s demand for CSB services (see page 15).

Demand for DSC services is

stable but well below

benchmark averages

Service

Demand

• The DSC is still being overwhelmed with the volume of contacts they are receiving (ASA, AR) despite a lower than

benchmark average demand. This result could be driven to peak demand periods.

DSC workload appears to be

higher than its resource

capacity or capability.

DSC

Performance

• The DSC appears to be less effective than benchmarks at handling tickets at first contact (through chat or phone)

(FCR).

• Potentially as a consequence of lower effectiveness, the DSC appears to need to hand off more contacts than other

peer organisations (FLR). DSC agents are thus spending shorter than benchmark times on calls (AHT)

• High agent utilisation may be also be causing a loss in effectiveness (e.g. by driving staff turnover) (AU).

DSC appears to be not as

effective at resolving contacts

compared to benchmarks

DSC

Capability


• The DSC is being overwhelmed with the volume of contacts they are receiving (ASA, AR) on average

• This may be primarily driven by inadequate DSC capacity during peak periods

• The DSC’s agents are being highly utilised which means that at its current capability level (AU), it may be difficult to

further optimise existing capacity within the DSC.

DSC appears to need

additional capacity particularly

around peak periods

DSC

Capacity

The following insight themes have been drawn for each dimension, from the range of insights gathered by benchmarking the

CSB against its industry.

Insight Themes & Conclusions | Insight Themes (2 of 2)

INSIGHT DIMENSION & THEME SUPPORTING INSIGHTS

• DPT may be expending a high proportion of its capacity on non-ticket tasks compared to benchmark averages

(MTTR, Effort to Resolve)

• The DPT may also be waiting relatively long to receive the tickets it resolves, likely due to challenges co-

ordinating with other teams

• It is likely that the DPT’s backlog is not growing significantly since the volumes of tickets received and

resolved have both been stable (Tickets received by the DPT, Tickets resolved by the DPT).

DPT may need to reorganise

capacity towards resolving

ticket related tasks

DPT

Performance

• The DSC and eHealth has received above benchmark average customer satisfaction ratings (CS) from end-of-ticket

surveys

• Data collected via this methodology does not capture abandoning customer sentiments which is likely to be

lower than benchmarks due to high ASA etc.

• The CSB as a whole is resolving incidents within ServiceNow ticket SLA timeframes at above benchmark averages

(SLAs)

• Insight limited by dependency on leniency of the SLAs entered in ServiceNow and data quality

CSB customer satisfaction

appears to be good although

there are limitations in the data

Customer

Satisfaction


Problem management and automation can help to the CSB to actively reduce the volume and complexity of tickets it

receives.

Insight Themes & Conclusions | Conclusions (1 of 3)

How can the CSB actively

reduce the complexity and

volume of tickets it receives?

Service Demand

1 Develop problem management capabilitiesThe CSB should consider opportunities to introduce problem management to identify and address underlying problems in order to reduce incidents (and

therefore service demand)

• DSC agents are handling an increasing proportion of time consuming, non-transactional contacts

• First Level Resolution Rate has been steadily decreasing over the last 12 months and is below benchmarks

• Average Handling Time has been gradually increasing from October 2016

• Although demand is stable, problem management may help to make the DSC more effective by reducing the complexity of contacts they

need to handle

• DSC agents potentially do not have the optimum training, knowledge or tools usage to resolve tickets

• First Contact Resolution Rate is below benchmark averages

• First Level Resolution Rate has been steadily decreasing over the last 12 months and is below benchmarks

2 Continue to AutomateThe CSB should continue to automate or divert (e.g. self-service) low complexity, transactional tasks to reduce service demand on DSC agents

• Customer wait times have been decreasing potentially due to the implementation of call-backs and automation of low complexity, transactional

contacts

• Average Speed to Answer (e.g. call waiting times) has been rapidly decreasing since July 2018

• Proportion of tickets resolved by automated services has been increasing (e.g. helping to decrease the workload on CSB live agents).


How can the CSB improve

its current state Service

Delivery Model?

Service Supply

Improving the effectiveness of first level support through strong knowledge management and cross team collaboration can

help to optimise the CSB’s supply of services.


Improve knowledge management and quality of trainingConsider improving usage of tools, knowledge management and quality of training for DSC agents in order to be more effective at resolving tickets at the first

level and first contact.

• DSC agents potentially do not have the optimum training, knowledge or tools usage to resolve tickets

• First Level Resolution Rate is below benchmark averages


• Average Handling Time has been gradually increasing from October 2016

• High agent utilisation in the DSC is likely driving high turnover rates and thus loss of knowledge from DSC.

• Agent Utilisation is close to benchmark maximums

1

Focus DPT CapacityThere may be opportunities to deploy DPT capacity in order to assist with service demand management strategies or with some of the first level workload

• There appears to be opportunities for DPT to reorganise capacity towards ticket related tasks.

• DPT Mean Time to Resolve is above benchmark maximums although, there appears to be a low effort to resolve as a proportion of MTTR

• DPT Tickets resolved and received has been relatively stable indicating the DPT backlog is likely not growing

2


Improving the effectiveness of first level support through strong knowledge management and cross team collaboration can

help to optimise the CSB’s supply of services.


Additional call handling resources around peak periodsAdditional call handling resources might be needed in order to handle current service demand particularly around peak periods (depending on the efficacy of

the previous recommendations)

• The DSC appears to be handling more tickets than it is capable of resolving

• Average Speed to Answer (e.g. call waiting times) is much higher than average benchmarks

• Abandonment Rate is much higher than average benchmarks

• The DSC does not have capacity to handle incoming service demand

• Agent Utilisation is close to benchmark maximums

3

Improve cross CSB and eHealth collaborationCloser collaboration between CSB teams and the wider eHealth organisation may be needed to optimise and improve customer experience, by reducing ticket

handoffs and thus delays to resolve customer tickets

• There may be delays in DPT coordinating resolution with other support teams

• DPT Mean Time to Resolve is above benchmark maximums and increasing despite consistent DPT performance

• Stronger collaboration within the CSB and wider eHealth organisation may help to improve first level support effectiveness

• First Level Resolution Rate is below benchmark averages


4

How can the CSB improve

its current state Service

Delivery Model?

Service Supply

42

Appendix


Appendix A | Benchmark Calculations (1 of 2)

Metric Calculation Components

Tickets received per end-user (TPEU)

1. Total number of tickets received by CSB channels (DSC comprises of Chat, Auto Password Reset and Phone, Other units/remainder

estimated from proportions of “last assigned resolver group” tickets) between March 2018 and March 2019 (inclusive of start date)

2. Total number of end users supported by the CSB (total number of active accounts on AD)

Average Handling Time (AHT)1. Total handling time between March 2018 and March 2019 (inclusive of start date)

2. Total incoming contacts handled (not inclusive of automated password reset or callbacks)

Average Speed to Answer (ASA)1. Total waiting time for queued contacts between March 2018 and March 2019 (inclusive of start date)

2. Total contacts handled (inclusive of callbacks – therefore assumes callbacks represent zero waiting time).

Abandonment Rate (AR)1. Total abandoned contacts between March 2018 and March 2019 (inclusive of start date)

2. Total incoming contacts queued (not inclusive of automated password reset or callbacks)

First Level Resolution Rate (FLR)1. Total number of tickets provided from ServiceNow tickets provided

2. DSC resolved tickets

First Contact Resolution (FCR)

1. DSC resolved tickets regardless of final resolver

2. DSC resolved tickets with “0” reassignments (or “1” reassignment) and resolved within 1 hr (assumes first contacts resolved should last

less than an hour in duration – this is likely fair since AHT ~ 10 minutes).

3. DSC tickets Note: filter is “actual restore hours” as it is an indicator of how long a ticket takes to close

Mean Time To Resolve (MTTR)

1. DPT resolved incidents irrespective of number of hand-offs or hours spent to resolve.

2. Total hours elapsed between when a ticket is opened and when a ticket is resolved

3. Note: filter is “actual restore hours” as it is an indicator of how long a ticket takes to resolve

Agent Utilisation 1. DPT resolved incidents irrespective of number of hand-offs or hours spent to resolve.

2. Total hours spent to resolve the incident

Incidents resolved within SLA agreed

timeframes

1. Total number of incident tickets logged in ServiceNow

2. Incidents resolved within SLA time frames according to priority and SLAs inputted into ServiceNow (using “actual restore hours”)

Customer Satisfaction 1. Positive responses to end-of-ticket survey expressed as a percentage of total responses

DSC to DPT Hand-Offs 1. Proportion of the total tickets the DSC hands off, that is assigned to the DPT (e.g. proportion of level 1 hand-offs to level 2).


Appendix A | Benchmark Calculations (2 of 2)

Metric Calculation Components

Annual Contacts per Service Desk FTE1. Total number inbound contacts handled by DSC for 2018

2. Total FTE for DSC (note, 2019 figure).

EUDs per EUC FTE1. Total registered eHealth Queensland EUDs (including smart devices, printers, desktops and laptops)

2. Total DPT FTEs recorded as a role within an EUC team (by Org Chart) for 2019 (inclusive of TSB EUC FTEs)

Service Desk FTE to IT FTE ratio1. Total DSC FTEs for 2019

2. Total eHealth Queensland FTEs for 2019

EUC FTE to IT FTE ratio1. Total DPT FTEs recorded as a role within an EUC team (by Org Chart) for 2019 (inclusive of TSB EUC FTEs)

2. Total eHealth Queensland FTEs for 2019


ST

EP

DE

FIN

ITIO

NA

CT

IVIT

IES

Benchmarking has been conducted through the first three weeks of the CSB SDM review in an iterative process of

identifying, sourcing and analysing metrics.

Appendix B | Benchmarking Process

Agree on metrics that best align

with the agreed purpose of

benchmarking

• Consolidate and agree on list of

benchmarks

• Estimate effort required to

extract/consolidate metrics

• Identify key contacts and

systems where CSB metrics1

need to be extracted from

1Key dependency, CSB data to be provided

no later than CoB Friday 12 April 2019.

• Define and agree on the

benchmarking objectives

• Outcomes needed to

support SDM design

• Define and agree on benchmarking

assumptions

Set the context and objectives for

benchmarking that will guide how

metrics will be chosen/analysed.

1. Contextualise 2. Define3. Extract &

Consolidate

4. Compare &

Analyse

Work with key CSB contacts to

extract agreed metrics.

• Ensure extracted metrics align

with benchmark definitions

• Determine and agree on

alternative or additional

metrics/proxies as necessary

• Iterate and refine

benchmarking approach

Benchmark against analyst

reports/data made available to eHealth

• Finalise visualisation of data

• Add contextual analysis (may

require some additional engagement

with stakeholders)

• Develop benchmarking draft report

which will include,

• Current state insights

• Future state considerations

Some iteration may be necessary as the list of metrics

to be benchmarked or used is refined


The majority of DSC hand-offs are to DPT teams. Other major “2nd level” support channels include the HHSs and Business

Application teams.

Appendix C | DSC to DPT hand-offs

BENCHMARK DATA

20%

30%

40%

50%

60%

70%

80%

Mar-18 May-18 Jul-18 Sep-18 Nov-18 Jan-19

Pro

port

ion o

f D

SC

Tic

kets

Handed

-Off

to D

PT

(%

)

Recent History of DSC to DPT Hand-off Proportions

• The majority of DSC hand-offs are to DPT teams

• The proportion of DSC to DPT hand-offs is relatively

steady and does not display a trend to increase


2.74%

57.74%

7.82%

20.34%

7.07%

2.68% 1.62%

DSC Ticket Hand Off Channels (Feb 18 to Feb 19 Avg)

Contemporary Workspace Program

DPT

HHS

Business Applications

Other

Hosting & Directories

ieMR


0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Annual C

onta

cts

per

Serv

ice D

esk F

TE

Contacts handled per Service Desk FTE (Annual)1,2

The DSC is handling fewer contacts per FTE than the benchmark average. This may mean the DSC is adequately resourced

compared to benchmark peers, or that the DSC is not adequately equipped for the volume of contacts it handles.

Appendix D | Staffing Ratios - Annual Contacts per Service Desk FTE

BENCHMARK DATA

• The DSC is handling fewer contacts per FTE than the

benchmark average

• Total Contacts handled by the DSC has been relatively

constant (steady), particularly over the past 2 years.

• This trend is in line the DSC’s TPEU (see page 13)



Given that the DSC is handling fewer contacts per FTE

than benchmarks –the DSC may be facing first level

effectiveness issues and/or it may be adequately

resourced compared to peer organisations.

• Relatively high contact complexity compared to

peer organisations may be causing first level

effectiveness issues

• First level effectiveness issues is evident in

other benchmarked metrics (FCR, FLR).

• Fewer contacts per FTE compared to benchmarks

may imply that the DSC has adequate resources,

however this insight runs counter to,

• High ASA and AR compared to

benchmarks

• High AU compared to benchmarks

• Therefore it is more likely that the DSC is

facing first level effectiveness issues

4, 337

9, 125 - 75th Percentile

4, 049- 25th Percentile

6, 846 - Average

1 Benchmark Data | Gartner - 2019 - IT Key Metrics Data 2019 Key Infrastructure Measures IT Service Desk Analysis 2 CSB Data | Amazon Connect Extracts Received 10/4/2019 from CSB Manager, Reporting and Analysis, SM&I, DSC FTE

number received from

DSC Average

(2018)Benchmark AverageInter-Quartile

Range

0

100,000

200,000

300,000

400,000

500,000

600,000

2016 2017 2018

Tota

l C

onta

cts

Handle

d b

y D

SC

per

Year

Historical Contacts Handled by DSC2


In terms of FTEs, the DSC’s size relative to eHealth Queensland’s size is close to benchmark averages. This suggests that

the DSC is close to adequately staffed compared to peer organisations.

Appendix D | Staffing Ratios – Service Desk FTEs to IT FTEs Ratio

BENCHMARK DATA

• DSC FTEs as a percentage of total IT FTEs is close to

benchmark averages (for 2019)

• Compared to industry benchmarks (specifically for

healthcare and for similarly sized IT organisations)

the CSB’s ratio of DSC to total IT FTEs is slightly

above averages



The DSC appears to be close to adequately staffed

compared to benchmarks and industry averages.

• Relative to the number of eHealth FTEs, there is a

close to benchmark average amount of DSC FTEs

compared to industry and benchmark averages

• Service Desk FTEs to IT FTEs ratio is a more

direct comparator of resource capacity for a

Service Desk (compared to Agent Utilisation).

0%

5%

10%

15%

20%

25%

30%

Perc

enta

ge o

f S

erv

ice D

esk (

DS

C)

FT

Es o

f T

ota

l IT

F

TE

s (

eH

ealth)

(%)

Service Desk FTEs on Total IT FTEs Ratio1,2,3

8.05%



10.6 % - Average

5.2 % - Similar Sized IT

Organisation Median

1 Benchmark Data | Gartner - 2019 - IT Key Metrics Data 2019 Key Infrastructure Measures IT Service Desk Analysis 2 CSB Data | CSB FTE Data Received 10/5/2019 from CSB CCEO, DSC FTE Data Received 10/5/2019 from CSB CCEO3 Benchmark Data | Computer Economics 2018 – Help Desk Staffing Ratios

Benchmark Average

Inter-Quartile

Range

DSC Average 2019

6.5 % - Healthcare

Industry Average


0

100

200

300

400

500

600

700

End U

Ser

Com

puting D

evic

es p

er

End U

ser

Com

pute

F

TE

EUDs per EUC FTE1,2,3

Proportional to the number of EUC FTEs, the DPT supports more end-user devices compared to peer organisations. This is

potentially contributing to the DPTs workload away from direct ticket related activities.

Appendix D | Staffing Ratios – EUDs per EUC FTE

BENCHMARK DATA

249Average

322 - 75th Percentile

168 - 25th Percentile

612.3

• The EUDs to DPT EUC FTE ratio is much higher than

benchmark averages although closer to similarly sized IT

organisations and health specific organisations

• The number of EUDs supported include any

eHealth Queensland asset registered and in use

(assets purchased but not deployed, and retired

assets are not included).

• Assets include desktops, smart devices and

printers



Given that the ratio of devices to DPT staff supporting

them is high compared to benchmark – this may mean

that the DPT’s asset management workload is higher than

peer organisations.

• A higher overall number of devices means there

could be a proportionally higher asset management

(non-ticket related) workload particularly around,

• Asset monitoring (e.g. lifecycle)

• Vendor management

• Purchases and invoicing

• The true number of EUDs supported by the DPT

may be even higher than shown right if considering

non-eHealth devices that DPTs still provide

services for.1 Benchmark Data | Gartner – 2018 - IT Key Metrics Data 2018 Key Infrastructure Measures End User Computing Analysis2 CSB Data | ServiceNow Ticket Extract Received 10/4/2019 from CSB Manager, Reporting and Analysis, SM&I 3 Benchmark Data | Computer Economics 2018 – Desktop Staffing Ratios

449 - Similar Sized IT

Organisation Median

545 - Healthcare

Industry Average

Benchmark Average

Inter-Quartile

Range

eHealth Average

2019 (incl. TSB

Presentations Team)

eHealth Average

2019 (excl. TSB

Presentations Team)

667.8


0%

5%

10%

15%

20%

25%

30%

Perc

enta

ge o

f E

UC

(D

PT

) F

TE

s o

f T

ota

l IT

FT

Es

(eH

ealth)

(%)

EUC FTEs on Total IT FTEs Ratio1,2,3

DPT EUC FTEs as a proportion of total IT FTEs is close to benchmark averages. This suggests the DPT may be adequately

staffed compared to benchmarks – if EUC FTEs are a fair representation of the DPT’s size.

Appendix D | Staffing Ratios – EUC FTEs to IT FTEs Ratio

BENCHMARK DATA

10.3 %Average



13.3 %

• DPT EUC FTEs as a percentage of total IT FTEs is

close to benchmark averages (for 2019)

• Ratio is between industry specific averages (higher

than healthcare but lower than similarly sized

organisations).



The DPT may be adequately staffed or slightly under-

staffed compared to benchmarks.

• The DPT may have a slightly higher than average

ratio of EUC FTEs to total eHealth FTEs due to,

• The need to support significantly more

EUDs compared to benchmarks

• The CSB’s geographical spread compared

to benchmark organisations requiring a

greater “on-site technician” presence (this

appears to be shown by the CSB’s slightly

lower ratio compared to industry specific

medians).

• This insight assumes that EUC FTEs proportion of

IT FTEs is a fair representation of the DPT’s overall

proportion of IT FTEs.

• Note that TSB EUC FTEs (Presentations

Team) is equivalent to approx. 13 FTEs1 Benchmark Data | Gartner – 2018 - IT Key Metrics Data 2018 Key Infrastructure Measures End User Computing Analysis2 CSB Data | CSB FTE Data Received 10/5/2019 from CSB CCEO, DPT EUC FTE Data Received 10/5/2019 from CSB

Business Support Officer 12/4/20193 Benchmark Data | Computer Economics 2018 – Desktop Staffing Ratios

12.7 % - Healthcare

Industry Average

15.5 % - Similarly

Sized IT Organisation

Benchmark Average

Inter-Quartile

Range

eHealth Average

2019 (incl. TSB

Presentations Team)

eHealth Average

2019 (excl. TSB

Presentations Team)

12.2 %


Demand for CSB services across teams and channels (including the DSC and DPT) has been stable across the past 2 years.

Tickets received by the CSB – Data Analysis Approach

BENCHMARK DATA

• The ServiceNow ticket data does not provide a view as to the

first assigned group for a ticket; only the last assigned group

• A significant number of tickets are raised to undertake internal

jobs and should therefore not be included in the overall ticket

count as this is specifically concerned with user demand. These

internal jobs will include tasks undertaken by a number of eHQ

branches and units, e.g. the SIM team.

• Given the ServiceNow data, distinguishing these internal tickets

from genuine user jobs is difficult.

DATA CONSIDERATIONS

APPROACH TAKEN TO ANALYSIS

In the absence of data to identify the first point of contact of a

ticket, the following approach has been taken to generating the

graphs shown:

• The last assigned group of the ServiceNow ticket data has been

used as a proxy for whether a ticket was raised with CSB.

• This approach is based on an underlying assumption that if a

ticket was last assigned to a CSB unit, then it was also raised

with CSB. This assumption makes sense as the tickets assigned

to CSB units are those that constitute the majority of the load for

CSB. Other tickets will either never be assigned to CSB or will

be directed to another unit with minimal CSB effort.

• The graph shows the number of tickets with a last assigned

group equal to DSC, DPT or SM&I. The DPT and Online

breakdowns have been estimated using the number of DPT and

online tickets as a percentage of the total ticket volumes and

applying this percentage to the subset of CSB specific tickets.1 CSB Data | ServiceNow Ticket Extract Received 29/4/2019 from CSB Manager, Reporting and Analysis, SM&I

* incl. OPS, Portal, Auto resolved (e.g. Amazon Connect), Email and OITS

Date when ticket

opened

Note that DPT and Online breakdowns are estimates only.

0

20,000

40,000

60,000

80,000

100,000

120,000

Jan-16 Jan-17 Jan-18 Jan-19M

onth

ly T

icket V

olu

mes R

ecie

ved b

y C

hannel

Historical Volume of Total CSB Tickets Received (Opened)1

DSC DPT (estimated) Online * (estimated) Total CSB


0

1000

2000

3000

4000

5000

6000

2014-2015 2015-2016 2016-2017 2017-2018

Devic

es

Years

Cairns End User Device Fleet

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