Customer Insight Analysis


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

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Customer Insight Analysis

  1. 1. What do we know about our customers?Customer Insight AnalysisVoluntas Customer Conference15th November 2012Paul Ryall-FriendHead of Customer Experience v 1.0
  2. 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 peoples services• We provide homes and support services to general social housing residents, youngpeople and teenage parents, older people in sheltered housing, homeless people, sharedowners 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 1,473homes by 2016• We have a foyer where, in addition to accommodation, we provide training for youngpeopleOur prioritiesWe 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. 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, customercomplaints, compliments and documented reasons as to why customers refuse plannedmaintenance 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 ofcustomer feedback that could be used to explore broad trends or shifts in customer opinion, viewsand requirements• The current feedback data capture process was neither rigorous nor consistent and data analysishad been extremely limited• Information held was of varying quality across the different teams and it was not clear how thisinformation was analysed, interpreted or shared• We had stopped surveying customers once they have been through the complaint handlingprocess – we don’t know how customers perceive our ability to manage complaints• Voluntas have been contracted to deliver our customer satisfaction feedback survey through to31st December 2012
  4. 4. Customer Experience Strategy - MaximiseCustomer 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. 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. 6. Customer Insight –Net Promoter Score (NPS)How to Calculate our Net Promoter ScoreNPS is based on the fundamental perspective that every companys customers can be divided into threecategories: Promoters, Passives, and Detractors. By asking one simple question — How likely is it that youwould recommend Curo to a friend or colleague? — you can track these groups and get a clear measure ofCuro’s performance through its customers eyes. Customers respond on a 0-to-10 point rating scale and arecategorized 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 throughnegative word-of-mouth.To calculate Curo Net Promoter Score (NPS), we take the percentage of customers who are Promoters andsubtract the percentage who are Detractors. How likely would you be to recommend Curo Housing to family or friends?
  7. 7. Customer Insight –Net Promoter Score (NPS)How to Improve Our ScoreA companys Net Promoter Score (NPS) helps corporate leaders define their companies real mission and holdtheir 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, its a scheme that can be actedupon. Frontline managers can grasp the idea of increasing the number of Promoters and reducing the number ofDetractors a lot more readily than the idea of raising the customer satisfaction index by one standard deviation.Net Promoter EconomicsPromoters and Detractors exhibit dramatically different behaviours and produce dramatically different economicresults. Several factors distinguish Detractors from Promoters — explaining why it is so compelling for companies toincrease 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 shorterand less profitable relationships with a company.Margins: Promoters are usually less price-sensitive than other customers because they believe they are gettinggood value overall from the company. The opposite is true for Detractors: theyre more price-sensitive.Annual Spend: Promoters increase their purchases more rapidly than Detractors. They tend to consolidate more oftheir category purchases with their favourite supplier. Promoters interest in new product offerings and brandextensions exceeds that of Detractors or Passives.Cost Efficiencies: Detractors complain more frequently, thereby consuming customer-service resources. Somecompanies also find that credit losses are higher for Detractors. (Perhaps that is how the Detractors extractrevenge.) By contrast, Promoters help bring down your customer-acquisition costs by staying longer and helping togenerate new referrals.Word-of-Mouth: Quantify the proportion of new customers who selected your firm because of reputation orreferral. The lifetime value of these new customers, including any savings in sales or marketing expense, should be
  8. 8. Customer Insight – Net Promoter Score (NPS) † † NPS Leaders – US 2012 NPS Leaders – UK 2012 USAA* Banking 83 Apple I-phone 69 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. 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 monthsCustomer Satisfaction Service Area Target Aug July June to Aug to July to JuneHow satisfied or dissatisfied are you with the service provided Curo Group 95% 100% 96% 100% 97.53% 95% 94%by Curo Housing Group – LETTINGSHow likely would you be to recommend Curo Housing to Curo Group TBD 40.74% 48% n/a 45.43% n/a n/afamily 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 – REPAIRSHow likely would you be to recommend Curo Housing to Curo Group TBD 46.67% 47.36% n/a 47.40% n/a n/afamily 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 SERVICINGHow likely would you be to recommend Curo Housing to Curo Group TBD 50.01% 26.67% n/a 35.51% n/a n/afamily 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 WORKSHow likely would you be to recommend Curo Housing to Curo Group TBD 56.25% 63.16% n/a 59.06% n/a n/afamily 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 combinedHow likely would you be to recommend Curo Housingto family or friends – ALL (NPS) combined Curo Group 0 47.56% 44.08% n/a 45.34% n/a n/a
  10. 10. Voluntas Customer Satisfaction– What do we know? Distribution curve… Repairs 350 300 250 OSQCustomers 200 Advocacy Quality 150 Neighhood 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. 11. Voluntas Customer Satisfaction– What do we know? Distribution curve… Lettings 140 120 100 OSQCustomers 80 Advocacy Qua Home 60 Neighhood 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. 12. Voluntas Customer Satisfaction – What do we know? Regression AnalysisThe quest to determine real customer insight…• June 2012 – Voluntas were asked to undertake regression analysis across 1241 surveyresponses gathered in 2012• Data was placed in a stepwise regression model which builds the ‘best’ predictive model ofoverall 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 theythen 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 andunderstanding should be focused as these are statistically predicted to have the most beneficialeffect on overall satisfaction with Curo services H A C Performance B D L L Predictive Ability HWhy do this?• Maybe this analysis should be carried out annually? - Trends shift slowly and over time – identify
  13. 13. Voluntas Customer Satisfaction– What do we know? Regression Analysis Ability of wider variables to predict tenants reponse to Q6: Overall Satisfaction, compared to current reported levels of satisfactionRe-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 homeCurrent 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. 14. Voluntas Customer Satisfaction– What do we know? Regression Analysis Ability of wider variables to predict tenants reponse to Q11: Overall Satisfaction, compared to current reported levels ofResponsive 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 reportCurrent 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. 15. Voluntas Customer Satisfaction – What do we know? Regression Analysis Ability of wider variables to predict tenants reponse to Q12: Overall Gas Satisfaction, compared to current reported levels of satisfactionServicing Quadrant A: Low Quadrant C: High 100 Predictive Ability/ Predictive Ability/ High Satisfaction High Satisfaction Q9: Satisfaction with gas servicing 99 arrangements 98Current 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. 16. Voluntas Customer Satisfaction – What do we know? Regression Analysis Ability of wider variables to predict tenants 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 94.5Current 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. 17. Voluntas Customer Satisfaction – Regression Analysis summaryBased on this regression analysis, the following questions offer the best opportunity to improve ormaintain 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. 18. Voluntas Verbatim – what are customers tellingus? 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. 19. Voluntas Verbatim – what are customerstelling 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. 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. 21. Customer Complaint – Top 10 Root CauseAnalysis 2011/12– what do we know?1. Quality of work (both Repairs and Estate Services in-house repairs/contractors)2. Internal/External lack of communication3. Quality of service4. Residents having to chase staff for a response to query – resulting in a complaint5. Repair – length of time to schedule6. External contractors who work on our behalf don’t adopt the use of our values or service standards7. Rude staff/contractors8. Confidence in our service9. Multiple visits10. Request for work we do not normally/cannot carry out10. Missed appointments
  22. 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?Importanceof 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 2Determine • What we need to do more of/less of/the same to preserve/ increase satisfactionemotionalelements 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