Voluntas 4th Annual Customer Conference. 15th November 2012. Customer Insight Analysis by Paul Ryall-Friend, Head of Customer Experience, Curo-Group.

1. What do we know about our customers?
Customer Insight Analysis
Voluntas Customer Conference
15th November 2012
Paul Ryall-Friend
Head of Customer Experience
v 1.0

2. Who are we?
At a glance
• We are the largest social landlord in the Bath area providing 12,000 homes
• We are a major local provider of older people's services
• We provide homes and support services to general social housing residents, young
people and teenage parents, older people in sheltered housing, homeless people, shared
owners and leaseholders
• We provide services to other housing associations
• We let private market-rented properties
• We have developed more than 1,700 homes since 2002 and are due to complete 1,473
homes by 2016
• We have a foyer where, in addition to accommodation, we provide training for young
people
Our priorities
We have set ourselves six priorities:
• Creating a renowned customer service culture
• Owning great properties and places
• Setting up an ethical care and support business
• Working for happy, safe, popular neighbourhoods
• Helping people who need work
• Lobbying for positive social change

3. Customer Insight? - What we used to do…..
Method and approach
• The customer feedback process provided us with a snapshot view about how customers felt
• Feedback mechanisms included an event triggered customer satisfaction survey, customer
complaints, compliments and documented reasons as to why customers refuse planned
maintenance work
• Other feedback came direct from the resident involvement framework
• This data was not held centrally within our business and therefore we lacked a repository of
customer feedback that could be used to explore broad trends or shifts in customer opinion, views
and requirements
• The current feedback data capture process was neither rigorous nor consistent and data analysis
had been extremely limited
• Information held was of varying quality across the different teams and it was not clear how this
information was analysed, interpreted or shared
• We had stopped surveying customers once they have been through the complaint handling
process – we don’t know how customers perceive our ability to manage complaints
• Voluntas have been contracted to deliver our customer satisfaction feedback survey through to
31st December 2012

4. Customer Experience Strategy - Maximise
Customer Loyalty / Minimise Customer Effort
Effective Maximum
‘Outside-In’
Customer Customer Loyalty
processes &
Contact & Minimum
Right First Time
Management Customer Effort
• Do what we say we • Respond to individual • NPS
will customer needs and • Effort
• Do it when we say preferences
we will • Multi-channel access
• ‘I’ can do it and customer choice
• Consistent
Sources of Sources of
Business Satisfaction Dissatisfaction Customer
Improvement - What we do well Complaint root Feedback
Activity - Drivers of cause analysis -
satisfaction Reduce process
- Do more of / Customer error, risk
continue doing Insight waste -
/ do less of Prioritise and
- Compliments agree action -
Customer
Profile

5. Curo Customer Insight – ‘to be’ process
Inputs Insight Outputs
Compliments Survey
mechanism
ts Business
in
ta la
Survey Improvement
Da mp
• Survey construction • Automate data sample
Activity
Co
Survey • Survey channel generation and feed
maintenance • Relationship with survey
Survey • Data sample governance provider(s)
data • MI & Reporting • Owner of customer
Refusals
feedback data
data
• Share insight, knowledge
d and understanding
oo Insight
rh ts • Reduce process errors,
ou en • Performance – • do more of / continue
hb m risk and waste
g Effort/NPS
ei om doing / do less of • Reduce complaints
N c
• Drivers – correlation / • Sources of satisfaction • Lever and increase drivers
regression / verbatim & dissatisfaction of satisfaction and advocacy
• Importance to customer • Market research & • Measure and monitor
• Root Cause Analysis (RCA) benchmarking benefits
• Mystery shopping • Customer Profiling
Feedback data in share & Priorities for
Understanding inform
one place change

6. Customer Insight –
Net Promoter Score (NPS)
How to Calculate our Net Promoter Score
NPS is based on the fundamental perspective that every company's customers can be divided into three
categories: Promoters, Passives, and Detractors. By asking one simple question — How likely is it that you
would recommend Curo to a friend or colleague? — you can track these groups and get a clear measure of
Curo’s performance through its customers' eyes. Customers respond on a 0-to-10 point rating scale and are
categorized as follows:
•Promoters (score 9-10) are loyal enthusiasts who will keep buying and refer others, fuelling growth.
•Passives (score 7-8) are satisfied but unenthusiastic customers who are vulnerable to competitive offerings.
•Detractors (score 0-6) are unhappy customers who can damage your brand and impede growth through
negative word-of-mouth.
To calculate Curo Net Promoter Score (NPS), we take the percentage of customers who are Promoters and
subtract the percentage who are Detractors.
How likely would you be to recommend Curo
Housing to family or friends?

7. Customer Insight –
Net Promoter Score (NPS)
How to Improve Our Score
A company's Net Promoter Score (NPS) helps corporate leaders define their companies' real mission and hold
their people accountable for building great customer relationships — the only path to prosperity and true growth.
"Act Upon" the Three Groups of Customers
Grouping customers into these three clusters — Promoters, Passives, and Detractors — provides a simple,
intuitive scheme that accurately predicts customer behaviour. Most important, it's a scheme that can be acted
upon. Frontline managers can grasp the idea of increasing the number of Promoters and reducing the number of
Detractors a lot more readily than the idea of raising the customer satisfaction index by one standard deviation.
Net Promoter Economics
Promoters and Detractors exhibit dramatically different behaviours and produce dramatically different economic
results. Several factors distinguish Detractors from Promoters — explaining why it is so compelling for companies to
increase the number of Promoters and decrease the number of Detractors in their business.
Retention Rate: Detractors generally defect at higher rates than Promoters, which means that they have shorter
and less profitable relationships with a company.
Margins: Promoters are usually less price-sensitive than other customers because they believe they are getting
good value overall from the company. The opposite is true for Detractors: they're more price-sensitive.
Annual Spend: Promoters increase their purchases more rapidly than Detractors. They tend to consolidate more of
their category purchases with their favourite supplier. Promoters' interest in new product offerings and brand
extensions exceeds that of Detractors or Passives.
Cost Efficiencies: Detractors complain more frequently, thereby consuming customer-service resources. Some
companies also find that credit losses are higher for Detractors. (Perhaps that is how the Detractors extract
revenge.) By contrast, Promoters help bring down your customer-acquisition costs by staying longer and helping to
generate new referrals.
Word-of-Mouth: Quantify the proportion of new customers who selected your firm because of reputation or
referral. The lifetime value of these new customers, including any savings in sales or marketing expense, should be

8. Customer Insight –
Net Promoter Score (NPS)
† †
NPS Leaders – US 2012 NPS Leaders – UK 2012
USAA* Banking 83 Apple I-phone 69
Amazon.com 76 First Direct – Banking 62
USSA* – Auto Ins. 74 Apple hardware 59
Trader Joe’s - Grocery 73 Tesco Mobile 47
Costco / Apple 71 Simply Health 29
*
USAA (Homeowners Ins)
* United Services Automobile Association
†
2011 UK Net Promoter Industry benchmarks
Industry Avg. Best Worst
Banking 0 61 -34
Car Insurance -6 14 -
Home Insurance -20 -8 -38
Utilities -35 -19 -55
† Satmetrix 2012 US Net Promoter Benchmark / Satmetrix 2012 European Net Promoter Benchmark

9. Voluntas Customer Satisfaction
– Rated By Residents Survey
Re-Lets Responsive Gas Planned
Repairs Servicing Works
18 Qs 20 Qs 20 Qs 25 Qs
600 pa 900 pa 900 pa 840 pa
(50 pm) (75 pm) (75 pm) (70 pm)
Monthly Fortnightly Fortnightly Monthly
data data data data
sample sample sample sample
3 months 3 months 3 months
Customer Satisfaction Service Area Target Aug July June
to Aug to July to June
How satisfied or dissatisfied are you with the service provided
Curo Group 95% 100% 96% 100% 97.53% 95% 94%
by Curo Housing Group – LETTINGS
How likely would you be to recommend Curo Housing to
Curo Group TBD 40.74% 48% n/a 45.43% n/a n/a
family or friends - LETTINGS (Net Promoter Score)
How satisfied or dissatisfied are you with the service provided
Curo Group 95% 96% 94.74% 96% 95.57% 95.12% 95.4%
by Curo Housing Group – REPAIRS
How likely would you be to recommend Curo Housing to
Curo Group TBD 46.67% 47.36% n/a 47.40% n/a n/a
family or friends – REPAIRS (NPS)
How satisfied or dissatisfied are you with the service provided
Curo Group 95% 89.13% 96% 96% 94.38% 96.11% 95.10%
by Curo Housing Group – GAS SERVICING
How likely would you be to recommend Curo Housing to
Curo Group TBD 50.01% 26.67% n/a 35.51% n/a n/a
family or friends – GAS SERVCING (NPS)
How satisfied or dissatisfied are you with the services
Curo Group 95% 100% 94.74% 100% 98.36% 95.99% 96.2%
provided by Curo Housing Group – PLANNED WORKS
How likely would you be to recommend Curo Housing to
Curo Group TBD 56.25% 63.16% n/a 59.06% n/a n/a
family or friends – PLANNED WORKS (NPS)
How satisfied or dissatisfied are you with the service
Curo Group 95% 95.12% 95.45% 96.66% 95.74% 95.58% 95.2%
provided by Curo Housing Group – ALL combined
How likely would you be to recommend Curo Housing
to family or friends – ALL (NPS) combined Curo Group 0 47.56% 44.08% n/a 45.34% n/a n/a

10. Voluntas Customer Satisfaction
– What do we know? Distribution curve…
Repairs
350
300
250
OSQ
Customers
200 Advocacy
Quality
150 Neigh'hood
VFM
100
50
0
Very dissatisfied Fairly dissatisfied Neither Fairly satisfied Very satisfied
1 2 3 4 5
Very unlikely Fairly unlikely Neither Fairly likely Very likely
Satisfaction / Likelihood Jan-May 2012

11. Voluntas Customer Satisfaction
– What do we know? Distribution curve…
Lettings
140
120
100
OSQ
Customers
80 Advocacy
Qua Home
60 Neigh'hood
Rent VFM
40
20
0
Very dissatisfied Fairly dissatisfied Neither Fairly satisfied Very satisfied
1 2 3 4 5
Very unlikely Fairly unlikely Neither Fairly likely Very likely
Satisfaction/Likelihood Jan-May 2012

12. Voluntas Customer Satisfaction
– What do we know? Regression Analysis
The quest to determine real customer insight…
• June 2012 – Voluntas were asked to undertake regression analysis across 1241 survey
responses gathered in 2012
• Data was placed in a stepwise regression model which builds the ‘best’ predictive model of
overall satisfaction for Curo services
• The model starts with whichever variable covers the most unique variance in overall satisfaction
(e.g. most extreme responses) and then adds more in order of how much unique variance they
then explain, until its built the best possible model and stops adding variables
• In the following charts, Quadrant C and D (most potential quadrants) are those where effort and
understanding should be focused as these are statistically predicted to have the most beneficial
effect on overall satisfaction with Curo services
H
A C
Performance
B D
L
L Predictive Ability H
Why do this?
• Maybe this analysis should be carried out annually? - Trends shift slowly and over time – identify

13. Voluntas Customer Satisfaction
– What do we know? Regression Analysis
Ability of wider variables to 'predict' tenant's reponse to Q6: Overall
Satisfaction, compared to current reported levels of satisfaction
Re-Lets Quadrant A: Low Quadrant C: High
95 Predictive Ability/ Predictive Ability/
Q1: Given enough time to look
High Satisfaction High Satisfaction
at property
94
Q7: Overall quality of home
Current reported level of satisfaction (%)
93 Q12: Would recommend to
family and friends
92
91
Quadrant B: Low Quadrant D: High
Predictive Ability/ Predictive Ability/
90 Lower Satisfaction Lower Satisfaction
89
88 Q13: Member of staff did what
they said they would do
87
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
R-squared relationship to Q6: Overall Satisfaction (Predictive ability)

14. Voluntas Customer Satisfaction
– What do we know? Regression Analysis
Ability of wider variables to 'predict' tenant's reponse to Q11:
Overall Satisfaction, compared to current reported levels of
Responsive satisfaction
Repairs Quadrant A: Low Quadrant C: High
Predictive Predictive
100 Q5: Property left clean and Ability/ High Ability/ High
tidy
Satisfaction Satisfaction
98
Q1: Repairs easy to report
Current reported level of satisfaction (%)
96 Q8: Satisfaction with repairs
and maintenance dept.
94
92 Q14: Rent provides value for
money
90 Q13: Neighbourhood as a
Quadrant B: Low Q16: Would recommend to Quadrant D: High
place to live Predictive family and friends Predictive
88 Ability/ Lower Q12: Overall quality of Ability/ Lower
Satisfaction home Satisfaction
86
Q15: Listens to your views
84 and acts upon them
82
0 0.1 0.2 0.3 0.4 0.5 0.6
R-squared relationship to Q11: Overall Satisfaction (Predictive ability)

15. Voluntas Customer Satisfaction
– What do we know? Regression Analysis
Ability of wider variables to 'predict' tenant's reponse to Q12: Overall
Gas Satisfaction, compared to current reported levels of satisfaction
Servicing
Quadrant A: Low Quadrant C: High
100 Predictive Ability/ Predictive Ability/
High Satisfaction High Satisfaction
Q9: Satisfaction with gas servicing
99
arrangements
98
Current reported level of satisfaction (%)
97
Q10: Person spoke to helpful
96
95
Quadrant B: Low Quadrant D: High
Predictive Ability/ Predictive Ability/
94
Lower Satisfaction Lower Satisfaction
Q17: Would recommend to family
93 and friends
Q13: Overall quality of home
92
Q11: Member of staff did what they
said they would
91
90
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
R-squared relationship to Q12: Overall Satisfaction (Predictive ability)

16. Voluntas Customer Satisfaction
– What do we know? Regression Analysis
Ability of wider variables to 'predict' tenant's reponse to Q13: Overall
Planned Satisfaction, compared to current reported levels of satisfaction
Quadrant C: High
Works Quadrant A: Low
Predictive Ability/ Predictive Ability/
95.5 High Satisfaction High Satisfaction
95
Q10: Satisfied with planned
maintenance service
94.5
Current reported level of satisfaction (%)
Q9: Satisfaction with contractor
Q2: Views and preferences taken
94 Q18: Would recommend to
into account
family and friends
93.5
93 Quadrant B: Low Quadrant D: High
Predictive Ability/ Predictive Ability/
Lower Satisfaction Lower Satisfaction
92.5
Q4: Contractor wearing ID
92
Q16: Rent provides value for
91.5 money
Q7: Work completed within
timescale
91
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
R-squared relationship to Q13: Overall Satisfaction (Predictive ability)

17. Voluntas Customer Satisfaction
– Regression Analysis summary
Based on this regression analysis, the following questions offer the best opportunity to improve or
maintain overall satisfaction with Curo services, by service area (in no particular order):
Opportunity to improve Relatively High
Question
further Satisfaction already
Recommend to family and Responsive repairs; Gas
Re-Lets; Planned Works
friends Servicing
Responsive repairs; Gas
Overall quality of home Re-Lets
Servicing
Listens to views and acts on
Responsive repairs
them
Satisfaction with service Responsive Repairs;
area (e.g. repairs) Planned Works
Helpful person Gas Servicing
Member of staff did what
Gas Servicing
they said they would
Satisfaction with contractor Planned Works
Rent provides VFM Planned Works
Work completed within
Planned Works
timescales

18. Voluntas Verbatim – what are customers telling
us? Responsive Repairs advocacy comments…
“Always happy
with the way
“The price is “I think they are Somer treats
good for the brilliant – they are me”
service I receive” always there if you
need anything” “The lady I dealt
with when I was
getting the flat was
amazing”
“Prompt
service” “Poor services –
“They are too
slow to deliver they don’t do what
the service with they said they will,
regards to they don’t consider
repairs” personal
“I think they circumstances and
should be communication is
stricter with lacking”
some residents”

19. Voluntas Verbatim – what are customers
telling us? Gas Servicing advocacy comments…
“If you have a
problem they are “Always very
very prompt – such clean and tidy”
“Everybody is as repair work. It’s
very helpful” good they have
checks every 10
months rather than
yearly” “They always
“Because Somer listen”
have always
treated us well”
“Overall I am happy “Electrical safety
but there are a few check is still
“No-one seems outstanding and
to care – service niggly bits which
have not been anti-social
has gone behaviour still not
downhill” resolved”
sorted out”

20. Voluntas Customer Satisfaction verbatim –
likely drivers of satisfaction/dissatisfaction?
Friendly
and
helpful Had no
problems
in the
past
w Sti
ai ll
t
Keep your m fo ing
promises u r
Long fix ltip
standing es l e
resident
Staff T im
de wa e t o
Relative attitu it
performance – rep for
air
better than
other RPs
Not
calling
Impact of back
ASB

21. Customer Complaint – Top 10 Root Cause
Analysis 2011/12– what do we know?
1. Quality of work (both Repairs and Estate Services in-house
repairs/contractors)
2. Internal/External lack of communication
3. Quality of service
4. Residents having to chase staff for a response to query – resulting
in a complaint
5. Repair – length of time to schedule
6. External contractors who work on our behalf don’t adopt the use of
our values or service standards
7. Rude staff/contractors
8. Confidence in our service
9. Multiple visits
10. Request for work we do not normally/cannot carry out
10. Missed appointments

22. Customer Insight – next steps:
priorities and action based on what we know
• Develop true NPS advocacy measures across all surveys
1 • Need to understand important drivers of advocacy – what, when and why?
Importance
of Advocacy • Target and drive action to increase promoters to NPS
• Align and interpret with colleague NPS measure and drivers
• Need to determine emotional elements around key drivers of satisfaction
2
Determine • What we need to do more of/less of/the same to preserve/ increase satisfaction
emotional
elements
Quality of Satisfaction Satisfaction
Home with repair planned wk.
e.g. • State of • Right First Time? • Value for money
decoration? – customers
• Durability?
appreciating
• Neighbourhood?
• Repair Vs. planned works?
• Quality of Fixture replace?
• Setting
& Fittings?
• Speed of expectations
• Clean & Tidy? response? around timescales?
3 • Our agenda rather than customer agenda – e.g. Gas Servicing
Needs
driven • Customer isn’t asking anything of us…….but we recognise the importance
event of colleague attitude/friendliness/helpfulness and did what we said we would
4
Survey • Survey requirements; tender process; sample governance & representation
structure