View
120
Download
1
Category
Tags:
Preview:
DESCRIPTION
Voluntas 4th Annual Customer Conference. 15th November 2012. Customer Insight Analysis by Paul Ryall-Friend, Head of Customer Experience, Curo-Group.
Citation preview
What do we know about our customers?Customer Insight Analysis
Voluntas Customer Conference 15th November 2012
Paul Ryall-FriendHead of Customer Experience
v 1.0
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
Who are we?
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
‘Outside-In’ processes &
Right First Time
Effective Customer Contact
Management
Maximum Customer Loyalty
& Minimum Customer Effort
Customer Experience Strategy - Maximise Customer Loyalty / Minimise Customer Effort
Customer Feedback
Customer Insight
Business Improvement
Activity
• Do what we say we will• Do it when we say we will• ‘I’ can do it
• Respond to individual customer needs and preferences• Multi-channel access and customer choice• Consistent
• NPS• Effort
Sources of Satisfaction- What we do well- Drivers of satisfaction- Do more of / continue doing / do less of- Compliments
Sources of Dissatisfaction
Complaint root cause analysis -Reduce process
error, risk waste -
Prioritise and agree action -
Customer Profile
Inputs Insight Outputs
Feedback UnderstandingPriorities for
change
Survey data
SurveySurvey
Com
plai
nts
Dat
a
Refusals data
Neighbou
rhoo
d
com
men
ts
Compliments
data in one place
Survey mechanism
• Survey construction• Survey channel maintenance• Data sample governance• MI & Reporting
• Automate data sample generation and feed• Relationship with survey provider(s)• Owner of customer feedback data
• Performance – Effort/NPS• Drivers – correlation / regression / verbatim• Importance to customer• Root Cause Analysis (RCA)• Mystery shopping
share & inform
Insight
• do more of / continue doing / do less of• Sources of satisfaction & dissatisfaction• Market research & benchmarking• Customer Profiling
Business Improvement
Activity
• Share insight, knowledge and understanding• Reduce process errors, risk and waste• Reduce complaints• Lever and increase drivers of satisfaction and advocacy• Measure and monitor benefits
Curo Customer Insight – ‘to be’ process
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.
Customer Insight – Net Promoter Score (NPS)
How likely would you be to recommend Curo Housing to family or friends?
Customer Insight – Net Promoter Score (NPS)
Net Promoter EconomicsPromoters 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 allocated to Promoters. Between 80 and 90% of positive referrals come from Promoters. Detractors, meanwhile, are responsible for 80 to 90% of the negative word-of-mouth, and the cost of this drag on growth should be allocated to them.
How to Improve Our ScoreA 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 CustomersGrouping 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.
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 USAA (Homeowners Ins)
71 Simply Health 29
*
* 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
Voluntas Customer Satisfaction – Rated By Residents Survey
Re-Lets Responsive Repairs
Planned Works
Gas Servicing
600 pa (50 pm)
900 pa (75 pm)
900 pa (75 pm)
840 pa (70 pm)
Monthly data
sample
Fortnightly data
sample
Monthly data
sample
18 Qs 20 Qs 20 Qs 25 Qs
Fortnightly data
sample
Customer Satisfaction Service Area Target Aug July June3 months
to Aug3 months
to July3 months to June
How satisfied or dissatisfied are you with the service provided by Curo Housing Group – LETTINGS
Curo Group 95% 100% 96% 100% 97.53% 95% 94%
How likely would you be to recommend Curo Housing to family or friends - LETTINGS (Net Promoter Score)
Curo Group TBD 40.74% 48% n/a 45.43% n/a n/a
How satisfied or dissatisfied are you with the service provided by Curo Housing Group – REPAIRS
Curo Group 95% 96% 94.74% 96% 95.57% 95.12% 95.4%
How likely would you be to recommend Curo Housing to family or friends – REPAIRS (NPS)
Curo Group TBD 46.67% 47.36% n/a 47.40% n/a n/a
How satisfied or dissatisfied are you with the service provided by Curo Housing Group – GAS SERVICING
Curo Group 95% 89.13% 96% 96% 94.38% 96.11% 95.10%
How likely would you be to recommend Curo Housing to family or friends – GAS SERVCING (NPS)
Curo Group TBD 50.01% 26.67% n/a 35.51% n/a n/a
How satisfied or dissatisfied are you with the services provided by Curo Housing Group – PLANNED WORKS
Curo Group 95% 100% 94.74% 100% 98.36% 95.99% 96.2%
How likely would you be to recommend Curo Housing to family or friends – PLANNED WORKS (NPS)
Curo Group TBD 56.25% 63.16% n/a 59.06% n/a n/a
How satisfied or dissatisfied are you with the service provided by Curo Housing Group – ALL combined
Curo Group 95% 95.12% 95.45% 96.66% 95.74% 95.58% 95.2%
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
Repairs
0
50
100
150
200
250
300
350
1 2 3 4 5
Satisfaction / Likelihood
Cu
sto
mer
s
OSQ
Advocacy
Quality
Neigh'hood
VFM
Voluntas Customer Satisfaction – What do we know? Distribution curve…
Very dissatisfied Fairly dissatisfied Neither Fairly satisfied Very satisfied
Very unlikely Fairly unlikely Neither Fairly likely Very likely
Jan-May 2012
Lettings
0
20
40
60
80
100
120
140
1 2 3 4 5
Satisfaction/Likelihood
Cu
sto
mer
s
OSQ
Advocacy
Qua Home
Neigh'hood
Rent VFM
Voluntas Customer Satisfaction – What do we know? Distribution curve…
Very dissatisfied Fairly dissatisfied Neither Fairly satisfied Very satisfied
Very unlikely Fairly unlikely Neither Fairly likely Very likely
Jan-May 2012
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
Why do this?
• Maybe this analysis should be carried out annually? - Trends shift slowly and over time – identify drivers, determine what we need to do more of / continue doing / do less of, implement changes and then track/monitor feedback over the next period…
Predictive Ability
Perf
orm
ance
H
HL
L
A
B
C
D
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
Q12: Would recommend to family and friends
Q1: Given enough time to look at property
Q13: Member of staff did what they said they would do
Q7: Overall quality of home
87
88
89
90
91
92
93
94
95
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)
Curr
ent
repo
rted
leve
l of s
atisf
acti
on (%
)
Quadrant C: High Predictive Ability/ High Satisfaction
Quadrant A: Low Predictive Ability/ High Satisfaction
Quadrant B: Low Predictive Ability/ Lower Satisfaction
Quadrant D: High Predictive Ability/ Lower Satisfaction
Re-Lets
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
satisfaction
Q1: Repairs easy to report
Q5: Property left clean and tidy
Q8: Satisfaction with repairs and maintenance dept.
Q12: Overall quality of home
Q13: Neighbourhood as a place to live
Q14: Rent provides value for money
Q15: Listens to your views and acts upon them
Q16: Would recommend to family and friends
82
84
86
88
90
92
94
96
98
100
0 0.1 0.2 0.3 0.4 0.5 0.6
R-squared relationship to Q11: Overall Satisfaction (Predictive ability)
Curr
ent
repo
rted
leve
l of s
atisf
acti
on (%
)
Quadrant C: High Predictive
Ability/ High Satisfaction
Quadrant A: Low Predictive
Ability/ High Satisfaction
Quadrant B: Low Predictive
Ability/ Lower Satisfaction
Quadrant D: High Predictive
Ability/ Lower Satisfaction
Responsive Repairs
Voluntas Customer Satisfaction – What do we know? Regression Analysis
Ability of wider variables to 'predict' tenant's reponse to Q12: Overall Satisfaction, compared to current reported levels of satisfaction
Q17: Would recommend to family and friends
Q11: Member of staff did what they said they would
Q13: Overall quality of home
Q10: Person spoke to helpful
Q9: Satisfaction with gas servicing arrangements
90
91
92
93
94
95
96
97
98
99
100
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)
Curr
ent
repo
rted
leve
l of s
atisf
acti
on (%
)
Quadrant C: High Predictive Ability/ High Satisfaction
Quadrant A: Low Predictive Ability/ High Satisfaction
Quadrant B: Low Predictive Ability/ Lower Satisfaction
Quadrant D: High Predictive Ability/ Lower Satisfaction
Gas Servicing
Voluntas Customer Satisfaction – What do we know? Regression Analysis
Ability of wider variables to 'predict' tenant's reponse to Q13: Overall Satisfaction, compared to current reported levels of satisfaction
Q18: Would recommend to family and friends
Q10: Satisfied with planned maintenance service
Q4: Contractor wearing ID
Q16: Rent provides value for money
Q9: Satisfaction with contractor
Q7: Work completed within timescale
Q2: Views and preferences taken into account
91
91.5
92
92.5
93
93.5
94
94.5
95
95.5
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
R-squared relationship to Q13: Overall Satisfaction (Predictive ability)
Curr
ent
repo
rted
leve
l of s
atisf
acti
on (%
)
Quadrant C: High Predictive Ability/ High Satisfaction
Quadrant A: Low Predictive Ability/ High Satisfaction
Quadrant B: Low Predictive Ability/ Lower Satisfaction
Quadrant D: High Predictive Ability/ Lower Satisfaction
Planned Works
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):
QuestionOpportunity to improve
furtherRelatively High
Satisfaction already
Recommend to family and friends
Responsive repairs; Gas Servicing
Re-Lets; Planned Works
Overall quality of home Responsive repairs; Gas Servicing
Re-Lets
Listens to views and acts on them
Responsive repairs
Satisfaction with service area (e.g. repairs)
Responsive Repairs; Planned Works
Helpful person Gas Servicing
Member of staff did what they said they would
Gas Servicing
Satisfaction with contractor Planned Works
Rent provides VFM Planned Works
Work completed within timescales
Planned Works
Voluntas Verbatim – what are customers telling us? Responsive Repairs advocacy comments…
“The price is good for the
service I receive”
“I think they are brilliant – they are always there if you
need anything”
“Prompt service”
“Always happy with the way Somer treats
me”
“The lady I dealt with when I was
getting the flat was amazing”
“I think they should be
stricter with some residents”
“They are too slow to deliver
the service with regards to repairs”
“Poor services – they don’t do what they said they will, they don’t consider
personal circumstances and communication is
lacking”
Voluntas Verbatim – what are customers telling us? Gas Servicing advocacy comments…
“If you have a problem they are
very prompt – such as repair work. It’s
good they have checks every 10
months rather than yearly”
“Always very clean and tidy”
“Everybody is very helpful”
“They always listen”“Because Somer
have always treated us well”
“Overall I am happy but there are a few niggly bits which
have not been resolved”
“No-one seems to care – service
has gone downhill”
“Electrical safety check is still
outstanding and anti-social
behaviour still not sorted out”
Voluntas Customer Satisfaction verbatim – likely drivers of satisfaction/dissatisfaction?
Friendly and
helpful Had no problems
in the past
Relative performance –
better than other RPs
Keep your promises Long
standing resident
Impact of ASB
Not calling back
Time to wait for repair
Staff
attitude
Still waiting for
multiple
fixes
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
Customer Insight – next steps: priorities and action based on what we know
Importance of
Advocacy
1• Develop true NPS advocacy measures across all surveys
• Need to understand important drivers of advocacy – what, when and why?
• Target and drive action to increase promoters to NPS
• Align and interpret with colleague NPS measure and drivers
Determine emotional elements
2
Quality of Home
Satisfaction with repair
Satisfaction planned wk.
• Need to determine emotional elements around key drivers of satisfaction
• What we need to do more of/less of/the same to preserve/ increase satisfaction
• State of decoration?
• Neighbourhood?
• Quality of Fixture & Fittings?
• Clean & Tidy?
• Right First Time?
• Durability?
• Repair Vs. replace?
• Speed of response?
• Value for money – customers appreciating planned works?
• Setting expectations around timescales?
Needs driven event
3 • Our agenda rather than customer agenda – e.g. Gas Servicing
• Customer isn’t asking anything of us…….but we recognise the importance of colleague attitude/friendliness/helpfulness and did what we said we would
e.g.
4Survey
structure• Survey requirements; tender process; sample governance & representation
Recommended