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POST OFFICE LTD GROUP EXECUTIVE
Management Information Review - DRAFT
1. Purpose
The purpose of this paper is to update the Group Executive on the findings of the recent
Management Information (MI) Review and to recommend next steps and a longer term route
map to improve the MI which is available to steer the Post Office to meet its strategic
objectives.
2. Background
2.1. A review of Management Information has been carried out at the request of the
Group Executive. This has been carried out over a 7 week period under the
sponsorship of Alisdair Cameron.
2.2. The Terms of Reference for this review are attached in Appendix 1. The overall
objective of the review was to analyse the ‘as-is’ MI landscape and to make
recommendations for improvements in the short, medium and longer term.
2.3. The scope set out in the Terms of Reference was restricted and specifically
excluded:
. data analytics
° individual customer information,
. operational reporting
. programme status reporting
. budgeting / target setting
However, as the review has progressed, it has become increasingly clear that a
holistic approach is required and the above areas have therefore been included at
various levels of detail. In particular, it is important to consider the hierarchy
from high quality core data through to high quality decision making:
D
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3. Activities/Current Situation
3.1. The review has been conducted by Peter Goodman through a series of
interviews / discussions with ET, SLT and other colleagues as set out in the
Terms of Reference. Discussions have taken place with additional colleagues as
appropriate as the review has progressed.
3.2. The findings and recommendations included in this paper have been reviewed
with a number of the original interviewees who have indicated their agreement.
3.3. Due to the limited timescale and resource available for this review, the findings
and recommendations are at a relatively high level. Further work will be required
to develop these in more detail. See section 8 below.
4. Analysis of Current MI landscape
4.1.A common approach to this type of analysis is to present findings against a
number of dimensions using a maturity scale. Due to the lack of a benchmark
against which to rate Post Office MI, this has not been done in a quantified way.
However, the review has identified that Post Office is at the low end of maturity
on all dimensions. The findings against each dimension are detailed in the table
below.
4.2. A more detailed log of findings regarding ‘MI content’ is included in Appendix 2
reflecting comments received during interviews
4.3. A summary of current MI reports, by whom they are produced, and the systems
used to produce them is included in Appendix 3
* Too much focus on sales volume / value and income
e Insufficient focus on
o Back book / retention income
Profit / Margin
Pipeline / conversions
Promotional effectiveness
Cross selling effectiveness
Churn / retention
Trend analysis
External benchmarks
Agent compliance
o Programme reporting
There is no single, comprehensive, reliable view of our customers
There is no single comprehensive, reliable view of our branches
There is no single comprehensive, reliable view of our colleagues
There is no single comprehensive, reliable view of our agents
00000000
MI is too late — this means that actions are delayed
e Information on the previous week is not available until Tuesday c.f.
Monday for most retailers
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Weekly information is incomplete (e.g. no back book info) and does
not build up to monthly numbers such that the first realistic view on
monthly income / profit performance is on WD3 after month end
Client data which is key to providing weekly performance data is
often delayed
Too many versions of the truth (Credence, Finance, local
spreadsheets, client info — e.g. Supply Chain and Royal Mail
maintain own branch databases in addition to Network owned
database)
Lack of clear definitions (e.g. income gross or net of cost of sales)
Lack of confidence in accuracy / completeness of MI — leads to
people creating their own versions
Income factors (notional income) add confusion rather than clarity
particularly in FS as they do not align to real income (e.g. income
factors for mortgages reflect expected future income)
Too many errors e.g. financial mis-postings undermine confidence in
MI
Too many providers of MI — people don’t know where to go
Currently ca. 70 people across multiple teams involved in data
maintenance, MI production and analysis (including outsourced
resources)
No single person / team with a focus on leading to ensure that we
have the right MI for the business.
Very limited resource applied to Digital MI and analytics despite it
being a key growth channel
It is likely that we don’t have the capabilities required (data, MI and
analytics / insight generation). However, it is difficult to assess as the
good people we have are hampered by other factors (particularly
systems) so do not have the capacity to carry out more value added
work
Lacking leading edge capabilities in advanced analytics of
unstructured data
Lack of training and education on what is available, what it means
and how to use it — exacerbated by staff turnover in key areas (the
limited self-service capabilities are not widely used)
There is no single person / team taking a business wide view about
what is needed / prioritisation / strategy / governance
Lack of GE sponsor for MI
There is no cross functional governance group for MI
GE do not insist on information coming from the ‘official sources’ —
behaviours need to change and that needs to start at the top
Getting basic data right is critical to MI, insight and decision making
but it has insufficient focus in the business
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Basic data design / governance is flawed e.g. use of FAD (branch)
codes — there is no clarity on the usage of FAD codes and they are
now being used for FS Specialists etc.
There is a significant amount of missing / out of date data (e.g.
customers, employees, agents)
Data is fragmented as some originates with clients and there is no
process / system to bring it together
Lack of self-serve availability (both reporting and data maintenance).
Need to submit a request and wait for results
Significant level of manual work to capture data (e.g. sales leads)
and to produce MI
Heavy reliance on Excel, MS Access and local data stores.
Need more flexibility on how we cut the data. Changes in org
structure take too long to flow through to MI
Credence is not fit for purpose — too slow due to data volumes
Sales people (e.g. FS, Crown, Agency area managers) don’t have
the tools they should have for conversations with managers (e.g.
lpad with all info in one place). They have laptops with multiple
sources of info
Too much data — too little information / insights
Too much time on reporting, too little time on analysis / insights /
challenge
Lack of comms approach to MI —i.e. converting from numbers into a
story which is actionable
Processes do not use MI available e.g. product teams plan without
any customer insights
5. Proposed End-state MI landscape
5.1. The following table presents a ‘from-to’ analysis. The ‘Findings’ are repeated
from section 4 for reference. The ‘Recommended End State’ is the proposed long
term end state based on an internal view of best practice as well as input from
the interviews carried out
5.2. More details regarding a future MI suite is included in appendix 4
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.
Too much focus on sales volume / value and income
Insufficient focus on
Back book / retention income
Profit / Margin
Pipeline / conversions
Promotional effectiveness
Cross selling effectiveness
Churn / retention
Trend analysis
External benchmarks
Agent compliance
o Programme reporting
There is no single, comprehensive, reliable view of our
customers
There is no single comprehensive, reliable view of our
branches
There is no single comprehensive, reliable view of our
colleagues
There is no single comprehensive, reliable view of our
agents
MI is too late — this means that actions are delayed
Information on the previous week is not available until
Tuesday c.f. Monday for most retailers
Weekly information is incomplete (e.g. no back book info)
and does not build up to monthly numbers such that the
first realistic view on monthly income / profit performance
is on WD3
000000000
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Integrated MI suite driven by strategy priorities starting
with high level KPls and a hierarchy cascading down to
MI for branch managers, product managers, etc.
MI to include leading indicators / drivers and to be
balanced across financial, sales and other measures,
including key ratios and penetration / conversion rates
More focus on performance relative to market and
external benchmarks
An approach which aligns to accountability and influence
and reconciles to financials (e.g. branch recognised for
future value of a mortgage sale but that value not
included for profit tracking)
Asingle comprehensive, reliable view of customers,
branches, colleagues and agents
Daily view of previous day's sales volume / value
Weekly view of sales volume / value and income and
profit (contribution) available Monday which build up to
month actuals
Client data received to same time scales as internal data
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Client data which is key to providing weekly performance
data is often delayed
No daily sales visibility
Too many versions of the truth (Credence, Finance, local
spreadsheets, client info)
Lack of clear definitions (e.g. income gross or net of cost
of sales)
Lack of confidence in accuracy / completeness of MI —
leads to people creating their own versions
Income factors (notional income) add confusion rather
than clarity particularly in FS as they do not align to real
income (e.g. income factors for mortgages reflect
expected future income)
Too many errors e.g. financial mis-postings undermine
confidence in MI
Too many providers of MI — people don’t know where to
go
Currently ca. 40 people across multiple teams involved in
data maintenance, MI production and analysis (plus
outsourced resources)
No single person / team with a focus on leading to ensure
that we have the right MI for the business
Very limited resource applied to Digital MI and analytics
despite it being a key growth channel
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Single version of the truth
All MI sourced from a single well governed ‘repository’ —
likely to be multiple sources / physical data stores due to
business model but must be brought together
consistently and seamlessly
A single agreed definition for all measures
Rigorous focus on ‘right first time’ transaction processing
and data quality
A single ‘Business Intelligence (Bl)’ team within the CFO
function, very closely aligned to IT, covering:
o Data governance (definitions, agreed
ownership, quality metrics, maintenance
processes, etc.)
o Ml governance (definition of MI suite, agreed
report ownership, change control, set client
standards, agree standard analytics tools)
o Ml production
o Basic analytics e.g. overhead variance analysis
o IT requirements
Local small, specialist analytics teams in Commercial,
FS, Network deriving insights from centrally provided
data / MI. This should include a s small pool of highly
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It is likely that we don’t have the capabilities required
(data, MI and analytics / insight generation). However, it
is difficult to assess as the good people we have are
hampered by other factors (particularly systems) so do
not have the capacity to carry out more value added work
Lacking leading edge capabilities in advanced analytics
of unstructured data (e.g. social media)
Lack of training and education on what is available, what
it means and how to use it — exacerbated by staff
turnover in key areas (the limited self-service capabilities
are not widely used)
There is no single person / team taking a business wide
view about what is needed / prioritisation / strategy /
governance
Lack of GE sponsor for MI
There is no cross functional governance group for MI
GE do not insist on information coming from the ‘official
sources’ — behaviours need to change and that needs to
start at the top
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skilled ‘data scientists’ to interrogate data
Data and MI is a discrete workstream in all major change
programmes e.g. Front Office
Highly skilled central BI team — combination of upskilling
some existing resources and supplementing with new
resources
Highly skilled local analytics teams.
All relevant people (product managers, sales managers,
etc.) able to self-serve for basic MI / analysis
Across functional Data and MI Steering group chaired by
CFO or ClO and Operations Director with SLT members
from each function supports the Head of BI in delivering
the changes required to achieve the recommended end
state. This Steering Group would also approve all MI and
data governance standards (see next two bullet points)
Strong Data governance (definitions, agreed ownership,
quality metrics, maintenance processes, etc.)
Strong MI governance (definition of MI suite, agreed
report ownership, change control, set client standards,
agree standard analytics tools)
A cross functional ‘working group’ supports the Steering
Group and Head of MI
GE mandates that all information presented to it is based
on the central MI sources and for regular reporting does
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Getting basic data right is critical to MI, insight and
decision making but it has insufficient focus in the
business
Basic data design / governance is flawed e.g. use of FAD
(branch) codes — there is no clarity on the usage of FAD
codes and they are now being used for FS Specialists
etc.
There is a significant amount of missing / out of date data
(e.g. customers, employees, agents)
Data is fragmented as some originates with clients and
there is no process / system to bring it together
Lack of self-serve availability (both reporting and data
maintenance). Need to submit a request and wait for
results
Significant level of manual work to capture data (e.g.
sales leads) and to produce MI
Heavy reliance on Excel, MS Access and local data
stores.
Need more flexibility on how we cut the data. Changes in
org structure take too long to flow through to MI
Credence is not fit for purpose — too slow due to data
volumes
Sales people (e.g. FS, Crown, Agency area managers)
don’t have the tools they should have for conversations
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not use any information other than from the central team
Mandated to 3rd parties how they hold / provide data
The importance of high quality core data as a driver of
quality decision making (See pyramid in section 2) is
recognised by GE and appropriate funding and resources
are committed
Data quality and improvement thereof is driven by the
central BI team but it’s importance is recognised and
supported across the business
Data quality metrics and targets are used to drive
improvement and are reported to GE on regular basis
Data definitions are clear, unambiguous and consistent
Regardless of its source or physical location, there is a
single logical set of core data used to drive MI and
analytics
Systems enable a single logical view of core data
(products, branches, customers, financials, etc.)
regardless of its source or physical location
Systems enable a single point of access for all MI
Systems enable analysis of core data and MI as well as
unstructured data (social media, web analytics, external
data, etc. — ‘big data’) — potentially different systems for
the two requirements
Systems provide ‘push and pull’ functionality i.e. users
prompted by e-mail that standard reports are available for
them to view as well as reports on demand
Systems are ‘tomorrow's technology’ — not extensions of
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with managers (e.g. Ipad with all info in one place). They today’s technology
have laptops with multiple sources of info
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6. Recommendations
6.1. To achieve the recommended end state will require a multi-year change programme. The following table presents a proposed high
level plan as to what changes could be implemented in the short (0-6 month), medium (6-18 months), longer term (18 months+)
timescales
6.2. As organisation is a key component of the proposals, more detail on this aspect is included in section 7 below.
Structured analysis of MI Complete design of medium/long e __ Deliver full MI suite automatically
requirements hierarchy based on term MI suite and underlying data
business strategy (to feed into / systems to support
medium term) e Deliver key components of
« Commence design of medium / required MI suite — moving from
long term MI suite and underlying manual preparation to automation
data / systems to support over the period
* Continue development of offline
pipeline / conversion MI
« Implement weekly contribution
reporting (initially excel based)
e Review opportunities for e Complete improvement in
enhanced daily sales reporting timeliness of all reporting
and deliver
e Review opportunities to accelerate
weekly reporting to Monday and
deliver subject to feasibility
e Set WD improvement targets for
all key reports and start delivery of
improvement
e Engage with clients to improve
timeliness of their data
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Income reporting from single
source (SAP Bl) — may extend to
12 months due to complexity of
targets
Commence standard definition of
measures
Initiate ‘right first time’ initiative to
eliminate errors
Appoint head of BI
Create single central BI team and
local analytics teams
Ensure data and information is a
key workstream in Front Office
programme
Initiate Data and MI Steering
Group (SLT level)
Redefine Terms of Reference for
existing Information and Data
Improvement Steering Group to
support SLT Group
Increase focus on Data and MI at
Transformation Committee and
TMG — Head of BI to join TMG
and Design Authority
Structured analysis of core data
required to support MI
requirements (to feed into medium.
term)
Initiate core data quality
improvement project
May 2015
Complete definition of all
measures
Embed new organisation
Ensure data and information is
given sufficient focus in future
programmes
Once new organisation is in place,
carry out full capability analysis
and address gaps through people
changes and training / education
Set up full Data and MI
governance framework
(definitions, ownership, quality
metrics, maintenance processes,
change control, etc.)
Detailed data strategy developed
defining master (reference) data
sources, data flows, and related
system requirements
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e Define MI/ Data systems strategy e Implement interim MI portal e Complete implementation of Long
/architecture subject to feasibility Term Data and MI solution
e Define requirements / commence e Design and commence
design for ‘Virtual Data and implementation of Long Term
Information Warehouse’ Data and MI solution
e Early consideration of Dataand MI e Decommission legacy applications
implications of Front Office (potentially MDM, Credence,
programme (especially with Access)
respect to future of Credence) e Implement standard analytical
e Conduct feasibility study into tools
interim MI portal e Deliver requirements for sales
e Review analytical tools and teams (subject to business case)
recommend standard tool (s)
e Define requirements for sales
teams
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7. Organisation
7.1. The organisation below is proposed, to bring together the multiple teams
currently engaged in MI production. The head of BI could potentially report to
CFO or Transformation Director.
7.2. To create the proposed organisation (excluding the programme team which is
assumed to be funded from ‘Transformation funding’ — see section 9 below)
would require an estimated additional 6 roles:
. Head of Business Intelligence (SLP)
. MI / Data Governance and Maintenance Manager (band 4)
. MI / Data governance specialists (5 x band 3A/3B, of which 1 already exists
in Finance, covering Customer, Branch / Agent, Product, Finance, Other
domains)
7.3.As further detailed work progresses, synergistic headcount reduction
opportunities may be identified. However, it is prudent to assume that any such
savings should be re-invested in driving improved analytics and insights in the
short to medium term.
7.4. There is a risk that once the central BI team is created, skills gaps will be
identified. It is not possible to assess this currently without a more detailed
capability assessment.
7.5. The proposed organisation does not include provision for any increase in local
analytics teams which would need to be considered separately
' I I New resources
Head of BusinessI MB exist
Intelligence ‘xisting resources
; I Programme resources
I
MI production MI / data MI / Data
/basic analysis governance and programme
(ca. 30 FTE) maintenance mgr manager / team
Data governancem™ Centralised data
: Local analytics
(5 FTE o/w4 maintenance maintenance
teams (multiple)
new) (ca. 5 FTEs) (ca. 10 FTEs)
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8. Recommended implementation approach
8.1. If the GE accepts the recommendations in this paper, the proposed next steps
would be:
. Appoint a Head of Business Intelligence
. Set up business as usual Data and MI governance
. Appoint a GE sponsor for this initiative
. Following approval through appropriate TMG Clearing House gates, initiate a
programme team with appropriate governance (e.g. Steering Committee) to
start delivering against the recommendations contained in section 6
«As part of the programme, procure an external Data / MI partner to bring
external best practice and assist in the design of the proposed solutions
. Ensure that the programme is appropriately integrated with other
programmes, particularly new Front Office, Back Office and Back Office
Tower implementation.
9. Commercial Impact/Costs
9.1. Itis always difficult to quantify the financial benefit from high quality Management
Information. It is clear, however, that a combination of the scale of transformation
required in Post Office over the coming years, the number of different markets in
which we operate, and the fast pace of change in those markets, means that
there will be many commercial decisions to be made. Even a relatively modest
improvement in decision making in a £1bn business will have a significant impact
on profitability.
9.2. The high level nature of the work to date does not provide the basis for an
estimate of the costs to implement the proposed changes. What can be said at
this point is that it will require a significant, multi-million investment. An estimate
of cost will need to be made in the next phase as a business case is prepared.
9.3. The 3 year plan includes provision for ca. £8m of funding in 2015/16 and 2016/17
for improvements in Data and Analytics within the IT enablement funding stream.
There is currently no specific provision for MI. To deliver the improvements
identified in this paper will require significant funding and an element of re-
prioritisation may be required.
9.4. The additional BAU cost for the Head of BI plus the Governance team (6 roles) is
estimated as £0.5m. Additional cost savings over and above those already
targeted will need to be identified across the business to fund this.
9.5. It is recommended that in the short to medium term the focus is on driving the
quality of MI rather than on BAU cost reduction. In the longer term cost reduction
may be possible.
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10. Key Risks/Mitigation/ CSFs
10.1. There are a number of critical success factors to deliver the desired end state
for MI. These are expressed below as risks with the mitigations representing the
success factors
MI and Data not seen as a priority amongst e Upfront agreement by GE /
the other Transformation initiatives required Transformation Committee that MI and
to deliver the Post Office strategic outcomes Data is critical to Post Office
and therefore is de-prioritised over time Transformation
e GE sponsor champions MI and Data at
Transformation Committee
e SLT level sponsor (Head of BI)
champions MI and Data at
Transformation Management Group and
Design Authority
Interdependencies with other elements of Programme is clearly defined and
Transformation are not well understood interdependencies clearly described
leading to fragmented solutions and / or e Programme is fully integrated into the
delays overall Transformation Programme plan
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11. Conclusion
11.1. High quality Data, MI, Analysis and Insight is critical to quality decision making
11.2. The current maturity of MI provision in Post Office is low on all dimensions
11.3. To transform from the current state to the proposed end state will be a multi-
year £multi-million journey.
11.4. There are steps which can be taken in the next 6 months which will start to
deliver immediate improvements and start the journey towards the desired end
state
11.5. A high level of commitment and sponsorship will be required to achieve the
required transformation
11.6. Savings of £0.5m will need to be identified to fund the BAU cost of the
proposed BI team to drive the required improvement in MI.
12. Recommendations
The Group Executive is asked to:
1
2.
3.
4.
5.
6.
Ts
8.
9.
10.
11,
12.
12.1. Note the results of the review set out in this paper;
12.2. Agree the recommendations set out in section 6 of the paper, or if necessary
agree further work required to revise the recommendations
12.3. If the recommendations are agreed, initiate the implementation approach
described in section 8 above
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Peter Goodman
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Appendix 1 — Terms of Reference
Management Information review ~ Terms of Reference
1. Background
An opportunity has been identified to streamline Management Information (MI) across Post
Office. This has the potential to:
* Improve the quality of MI in terms of its relevance and clarity — only produce the MI that
is used and can drive decision making and business performance management
¢ Improve the quality of MI in terms of information integrity and consistency — one version
of the truth and high level of confidence in the accuracy of the information
¢ Provide greater clarity across the business on where to go for MI
* Reduce the cost of MI provision by rationalising the number of reports and simplifying
the organisation for the production of MI
2. Purpose / Objectives
The purpose of this review is to:
Analyse the ‘as-is’ MI landscape in terms of:
Report ‘catalogue’
Organisation / roles and responsibilities
© Systems
e¢ Data
@ Governance
e Capabilities
¢ Process
Analyse the gap between the ‘as-is’ and optimal end state for MI against the above
dimensions
@ = Make high level recommendations on how to improve MI against each of the
dimensions, splitting the recommendations into:
Quick wins (0-6 months)
* Medium term (6-18 months)
© Longer term (18 months+)
© Give specific focus to the organisational dimension and include a recommendation on
whether a centralised MI function should be created and, if so, the design of the
organisation
@ Make high level recommendations on implementation approach
@ Make high level recommendations on interdependencies / requirements for MI
considerations in major programmes e.g. Front Office
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3. Approach
The review approach will be:
Carry out a number of structured interviews to collect information and views from
stakeholders (see below)
Analyse inputs and prepare preliminary recommendations
Discuss preliminary recommendations with Sponsor and ET Stakeholders
Refine recommendations
Finalise and present recommendations
4. Scope
The following areas are in scope for this review
Regular Management Information (daily , weekly, monthly, quarterly, annual)
Sales volume, income, P&L
ET level business wide reporting e.g. queue times, easy to do business with, etc.
ET level customer MI i.e. summarised info on customers e.g. products per customer,
customer satisfaction, retention
The following areas are out of scope for this review
Data Analytics
Ad hoc analysis
Individual customer level information (e.g. what information is captured and held for
individual customers)
Operational reporting i.e. non-financial / sales volume MI below ET level e.g. supply
chain operational (routing etc.), network operational (absence reporting etc.)
Programme status reporting (programme financials in scope)
Budgeting / target setting
5. Assumptions
The following assumptions will be made whilst carrying out this review:
MDM, Credence, SAP BI will co-exist at least until the Back Office tower provider has
taken over back office systems and has made a recommendation on approach
There will be no material change in the structure of call centres in the timelines under
consideration in this review i.e. product related call centres will continue to be managed
by clients
Funding for investment in MI technology will be limited in the 15/16 — 16/17 timeframe
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6. Risks
The key risk to delivery of this review within the timescales specified and to the quality required
is the availability of stakeholders to engage in the process given other priorities e.g. year end,
other programmes.
7. Governance / Reporting
This review will be governed as follows:
® Sponsor: Alisdair Cameron
¢ Review Lead: Peter Goodman
e Approach: Weekly meetings between Lead and Sponsor to review direction and progress
supported by a simple weekly progress report
8. Stakeholders
Input will be sought from the following in conducting this review:
e ET
e Kevin Gilliland
* Martin George
e Nick Kennett
e Neil Hayward
e David Ryan
¢ Paula Vennells
e Lesley Sewell
e Pete Markey
e SLT
© Dave Hulbert
¢ Neil Wilkinson
e Nick Sambridge
Sarah Hall
¢ = Colin Stuart
¢ Sharon Bull
© Rod Ismay
e Angela van den Bogerd
e Harry Clark
Roger Gale
¢ Michael Larkin
¢ Henk van Hulle
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e Julie Thomas
e Giles Dunning
° Jeremy Law
¢ Other
e Nina Trueman
¢ Kevin Lenihan
9. Deliverables
The deliverables from this review will be:
discussion at ET meeting
10. Timescale
© Agree TOR
Set up interviews / meetings
¢ Interviews / Analysis
¢ Collate findings and prepare draft report
¢ Socialise draft findings with ET stakeholders
® Send report to ET
e ETpresentation
Peter Goodman. 1 April 2015
« A Report to ET covering the areas described in the ‘Purpose / Objectives’ section above
e A PowerPoint presentation summarising key points from the report to facilitate a
The review will be carried out to the approximate timescales below:
2 April
2 April
13-24 April
27 April—1 May
5 May -8 May
13 May
15 May
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Appendix 2 — MI content detailed findings
The following provides more detail on the feedback form interviews on MI content
Too much focus on sales income and insufficient on retention / back book income
Too much focus on volume and income, not enough on margin / profit / contribution
Get good product MI (but not customer MI) from Bofl but not integrated with ours
Need pipeline and conversions reporting
Need more and better MI on sales drivers e.g. sales leads -> appointments -> applications -> sales or
digital page hits -> product page -> application start-> application completion -> sale (e.g. amazon
focus on page visits and conversions)
No single view of branch e.g. Types of customers, demographics, nearby businesses
Very lacking in customer info
MI not aligned to accountability e.g. need branch MI to reflect what branches can influence
Lack of structured hierarchy of MI from GE level KPIs down to branch etc. (consistent info)
Level of detail not tailored to level in org — ExCo should get high level dashboard — branches get
detail
Lack of hierarchy of performance drivers (Business scorecard is outcomes / historic)
Don’t have any way of measuring cross sales although a lot of new business cases are based on it
Difficult getting HR data (for Finance) e.g. headcount, absence, joiners and leavers, cost per
employee
Lack of ability to analyse impact of promo activity
Need more driver info e.g. telephony churn, sales conversions
Clients also have requirements for MI e.g. Flightpath (RMG)
Lack of trend analysis and rolling trajectories
Need more reference / measurement vs. external benchmarks
MI requirements for digital:
© System availability
Page response times
© Traffic levels
Page conversion
¢ Customer satisfaction
Need improved agent compliance information
Back book / retentions of increasing importance as want to drive business to continuing income
products
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Too much info missing at Trading Board
Need branch P&Ls
Programme reporting is unwieldy, cumbersome, duplicated, manual
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Appendix 3 — Current MI reports, Teams and FTEs
FS Performance and Planning Fs, crown Network [credence /Accoss /Excol
{Dave Burford) SAP HR/ ofl data
[Network FS Sales{ John O'Neil Stead, appt ete is salesforce 7
INetwork Branch standards (Dearne) (andy [Branch Conformance ranch Standards advisors, ICredence, dient data 7 BI
kingham) kevin Gilitand, Martin
Network IAgency tears (KarlOliver) [Ad hoc analysis [agency tT [credence limited
promotion impact
FSC- Other Debt, Transaction corrections, INetwork, Security, POLSAP, SAP CFS, Excel
Losses
[Finance Financial Contra IMonthiy Performance Report [GE and Board [Mattipte sources into 7 7
(inescorecard) Excel
Flash performance report
Finance (6PM Product Pais [corimercial teams /ProductISAP CFS, SAP BI, Excel 5 oa] 015) 025
IMS / FS sales analysis Managers, GE
Finance [NTP (Tim Branch) [converted branch performance NTP tear Icredence, Agent Pay, 025) 025)
Excel
[commeraat ata (Giles Dunning team) credence
sit catalyst adobe)
[commensal Eh Howard in Nik Beas ear
[commerdal —Markatng Caster Anal IPost Gmpaian Analyses, Produc Teans (Corser ORANDs SAS Toy 3d 7 yaa x
(ames de Sout) Product Marketing Pl Sales, fad 5), Marketing Planing [party xcl
(crossHolngcontactabiltyIfanion
lete)
[commerdal [Eustomer Experience insight Voice of customer, Voie of istemat 7
(andy Vig) isto (web)
[conrmerdal simon Philip (EWE) [ub porate Knbapaceontine
[cormmersal Wind hare external) edi spendand AO aera!
[uppiy Chin Multiple Maite Operational mates [Suply Chainintemal[POLSAP,Trastrak,
leg-ordercustomerserice, [ce Jashan, Galan, Ms
rack vale, tums cients Dynamics, Aces, xl
Marketing lwes, Mera, New Voice
sank oftraana Mea, Remedy, NIPS
[aiene
Bank oftrland learns
other
lther nts
te) other
frorar £7 EE
ERIE *o iomigte dare those withhigh numbers of repos produced and/or lrgst numberof people
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Appendix 4 — Possible future MI reporting suite
To define the future MI reporting suite is a significant project in its own right and cannot be covered
within the scope of this review. However the diagram below shows key elements of the future
design:
Income
source
Channel/
branch
Customer
\
Financial
Measures
Non-financial
st measures /
Product drivers Employee
Each of the boxes around the edge represents both a dimension for reporting / analysis purposes
and a master data store which must be complete, consistent, of high quality and easily accessible to
the organisation (with relevant access rights / security). This represents the main dimensions but is
not exhaustive — others would include projects, marketing campaigns, etc.
The ‘income source’ dimension is particularly important as it does not currently exist in any of our MI
systems. This would include sales, back-book, retentions, fixed agreements and would allow the
drivers to be collated and income to be calculated and reported correctly against each of these
income drivers. This would allow different but consistent views of income / profit depending on
accountability e.g. branches could see their performance based solely on sales income whilst a
product manager could see their performance based on all income streams
Some of the dimensions will require a full hierarchy to be established and maintained (some of which
already exist) e.g.
Note that the following areas have not been specifically included in this review and will need to be
factored into future work:
© budgeting / forecasting / target setting
¢ requirements of the Security team who carry out significant analytics work
e InfoSec aspects of Data / MI (Data Protection Act, Data privacy, etc.)
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The following table shows examples, but not an exhaustive list, of potential financial and non-
financial KPIs / measures:
Financial measures / KPIs Non-financial measure / KPIs
Income Sales volumes
Cost of goods sold Customer metrics
Direct product costs Pipeline / conversion metrics
Direct product contribution Penetration rates
Indirect product costs Market and market share
Indirect product contribution Pricing vs competition
Overheads Promotional effectiveness
EBITDAS Customer Satisfaction
EBIT Easy to do business with
Cash Flow Net promoter score
Staff cost / income ratio Queue time
Direct Product Contribution / income Branch compliance
Losses People indices (Attendance, diversity,
engagement, H&S, ER, Leadership, Talent,
reward
Branch transformation metrics
ROI on branch transformations
Stock
Top level KPIs are being defined as part of the strategy / 3 year plan work currently in progress.
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