HPMC12: Aviva - Existing Customer Management: Poor data,no analytics,no chance?
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Transcript of HPMC12: Aviva - Existing Customer Management: Poor data,no analytics,no chance?
Copyright © 2010 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.
High Performance Marketing Conference 2012
Aviva UK Life & Pensions
?
Copyright © 2010 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.
Aviva UK Life & Pensions
Existing Customer Management: Poor data, no analytics, no chance?
3
Contents
• Background & Context
• Existing Customer Management
• From Tactical to Strategic
• Benefits & Reflections
5
About Aviva
© 2011 Accenture. All rights reserved.
A significant global business:
- 6th largest insurer globally
- 44m customers
- 12 focus markets
- 36,000 employees
- 300bn+ funds under mgt
A clear strategy:
- Geographic focus
- Exploit composite advantage
- Build on core capabilities
6© 2011 Accenture. All rights reserved.
• Life, pensions, savings and retirement• 12% market share• 6.5m customers mostly through IFA and
Bancassurance
• House, car, pet, business insurance • Direct and broker distribution• 6m customers
• Private medical insurance and group risk
• Direct and intermediated business• #3 in market
Aviva UK – a 300 year pedigree
UK Life
UK GI
UK Health
7
Aviva UK Life – the problem and the opportunity
© 2011 Accenture. All rights reserved.
ProblemMore customers leaving than arriving
£XXXm commission bill
Creaking legacy infrastructure
Overwhelmingly intermediated
Opportunity
6.5m customers
Regulatory dynamics
Compelling customer brand
Existing Customer Management:
“We’ll invest in analytics-led customer marketing
to drive more value from our existing base”
Starting from a low base
Old approach constrained by: Existing Customer Management team created
• Increase up/ cross sell, retention, rollover campaigns with analytics
• Identify additional opportunities
• Build Aviva internal sustainability
• Define the roadmap and investment case
• Pilot value from customer analytics
• Poor view of customer data
• No analytics tools
• No analytical skills
• Campaign selections by intuition / experience
• Little visibility of new opportunities
• DM campaigns with long lead times
• Belief DQ = bad
New brand position focused on understanding the
individual customer and being relevant
ItalyIndia
UK
10
Feb’10 March 2012Aug.’10
Analytics managed serviceValue exploitationProof of value / pilot
Feb’ 11
Key project phases and evolution of capability
•Data Check •Exploratory Analytics•Test Campaigns •Process Changes•Build Awareness
Jul’ 11
© 2011 Accenture. All rights reserved.
•Increase Campaign Volumes
•Test Multiple Channels / Mechanics
•Broaden Use•Inform Customer Retention Strategy
•Inform IFA Analytics Proposition
•Campaign Prioritisation•Pure Off-shore Model •Transition To In-House (plan)
Did we have poor data?
Life Insurance Data Model Structure
Accenture’s Life Insurance Customer Analytic Record (CAR): • Facilitates rapid descriptive and
propensity model development• Identified overall data quality and
identify key data gaps.
DWH
Policy
Campaigns
Complaints
PaymentsClaims
Value Segment
Demographics
© 2011 Accenture. All rights reserved. 11
We developed a new “single-view-of-the- customer”
DWH
Descriptive Modelling: shining a light to identify value
© 2011 Accenture. All rights reserved. 13
X.Xm active Life1 Customers
31% are joint policies
XXm no longer active Life1 Customers
X.Xm customers with at least one Protection policy
X.Xmil customers with at least one IPP policy, 0.5m for GPP, 0.6m for
Annuities, 0.1m for Equity Release
X.Xm customers with at least one
Investment policy
X.Xm Active Life1 Policies
X.Xm Active Joint Life1 Policies
• X.X million Life policy holder records in our CIC database
• X.X million are currently active
• Average Life product holding is 1.06 products.
• Average Life policy holding is 1.2 policies.
Product Holdings
PH with Single Policy (#)
PH with Single Policy (%)
PH with multiple policies within same product holding
PH with multiple policies within same product holding (%)
Total Policy Holders (PH) (#)
Protection
Investments
IPP
GPP
Annuities
Equity Release
X.X.m Policyholders are between 35 and 49 and represent future
growth opportunity
1ST POLICY AnnuitiesGroup
Pension InvestmentsIndividual Pension
Equity Release Protection
Unknow Life Grand Total
Annuities 59% 0% 31% 2% 3% 4% 0% 88,528 GPP 8% 49% 3% 21% 0% 19% 0% 48,815 Investments 5% 1% 66% 9% 0% 18% 0% 442,480 IPP 18% 7% 11% 40% 0% 24% 0% 331,664 Equity Release 3% 0% 19% 0% 64% 13% 0% 9,382 Protection 2% 3% 8% 9% 1% 77% 0% 407,887 Uknown Life 8% 2% 50% 9% 1% 21% 10% 4,953 Grand Total 11% 5% 29% 17% 1% 36% 0% 1,333,709
2ND POLICY
90 Campaign Ideas Generated
Multiple Layers Of Analysis ….
Propensity Model Top 2 Deciles of Customers –
203K
Active policy holders
– 144K
Age Profiling Cut
(30 -60 yrs)– 108K
Has Phone– 74 K
After Supp. 28K
Campaign targeting through analytics
We enhanced
Customer T
argetin
g by
overlaying Resp
onder
Profiling on th
e
Propensity M
odel
Results
Increased targeting by applying filters based on insights from profiling of responders
14
15
Campaign results - significant uplift over BAU selection
CAMPAIGNS LIFT OVER RANDOMANNUITY CROSS_SELL 2.59PROTECT CROSS_SELL 1.57EQUITY RELEASE CROSS_SELL 1.36MOTOR TO LIFE 1.5HOME TO LIFE 3.8IPP RESTART 0.9 (negative performance)Annuity Pensions base 7.1Annuity Non Pensions base 3.6
Strategic Analysis: IFA (Financial advisor Base)
Data driven insights
delivered to IFA’s
Execute agreed actions
PROJECT APPROACHTrack IFA
Performance
1
24
3Plan and prioritise
actions on IFA Base
PARTNER WITH IFA TO CROSS- SELL HIGH VALUE PRODUCTS
PARTNER WITH IFA TO CROSS- SELL STRATEGIC PRODUCT LINES IFA IS NOT OPERATING IN
RE-TIERING
BRING IN-HOUSE DM OPERATION / RESPONSIBILITY FOR NON-STRATEGIC IFA’s
© 2011 Accenture. All rights reserved. 17
Strategic Analysis - churn segmentation
Joint holders : 10%Active products and joint policy holder group
Golden geese: 19%Older customer, high value and high ticket policy holder with bond surrender
Protection seekers : 19% Young customers, protection focused group
Loyal customers : 17%Middle age group, matured policies, high tenure group, pension and annuity base, mostly IFA customers
Movers & Changers: 35%Older customer, voluntary inactive group, low value, joint policyholders, pension customers
• Cluster analysis of inactive accounts to identify the types of customer attitudes and behaviour toward churn.
• Overlaid sample of bond surrender customer feedback to validate
© 2011 Accenture. All rights reserved. 18
Benefits delivered
• Uplift of 2.84 in sales conversion achieved against BAU customer selections.
• £XXmillion incremental value delivered through ECM campaigns 2011
•200k customers added to multiple Life policy holders in 18 months: 23% vs 20.5% (adding in GI policies 34% vs 26%)
• Proved tactical and strategic value of analytics• Insight generation and dissemination has led to snowball of new insight requests
•
1. Incremental sales conversion & revenues
3. Analytics & Strategy
• 25+ insight driven campaign waves executed• First cross BU campaign by customer management team• Campaign timelines reduced by 50%• 100% increase in marketable base by challenging existing suppressions and data management routines
2. Campaign Management & Process
© 2011 Accenture. All rights reserved. 20
21
Reflections from Aviva
• We developed analytics too early – the marketing infrastructure wasn’t ready
• The business wasn’t bought in enough – limiting the effectiveness of some campaigns
• We can use analytics as a tradeable currency – why aren’t we doing more?
• Is there more we can use CIC for... Predictive underwriting...?
• We still haven’t developed processes to deploy the analytics in anger – but we are near
• The value added from Accenture was outstanding – far more that their remit……..
22
Aviva’s Accenture Legacy
• We have proven the case for analytics in UK Life
• We have reduced campaign speed to market substantially
• We have a usable single customer view… after 6 years of trying
• We have sold the benefits of analytics to the wider UK region
But essentially……
• We have a platform for significant value growth over three years