Case study highlighting DBM and strategy skill set.

1. Wayne Wilkins

2. Roadmap
• Setting the Stage
• Challenges
• Strategy Recommendation
• Execution
• Summary
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3. Setting the Stage
• A large property and casualty company (XYZ Inc.) employed affinity, list-based mail
as a way to drive volume to the call center
– Marketing was driven by operations
• Fill inbound telemarketing capacity
• Satisfy other stakeholders such as list sources (universities, associations, credit unions, non profits)
– Must mail every affinity partner record 1X per year, minimum
• Only 2 FTE within the company dedicated to P&C direct marketing, neither of whom came from
insurance backgrounds
– Mailed about 2-3MM pieces per year using Agency, who had acquired the account almost by
accident
• Almost all decisions centered around smoothing call volume, not generating accounts
or premium $$$
– Response rates hovered around 50 bps
– Hindered by the lack of an MCIF and the inability to make a case for extra budget without
promising results
• XYZ saw promise but felt anxiety
– Thought direct marketing could take them from < 5% of their unit sales to a much greater
percent, but didn't know how to get there
Proposed migration from mass mail shop to a disciplined database marketing organization
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4. Challenges
• Biggest XYZ concerns
– Lack of internal expertise and insurance direct marketing knowledge
– Confined to affinity list sources (non-negotiable)
– Horribly inefficient ITM unit – reps had no individual sales goals, yet marketing still needed
to fill the “leads pipeline”
– Tons of data, but… mostly irrelevant to marketing
• No access to campaign information
• No demographic, purchase, cross sell information
– Constrained by underwriting, incentive laws and pricing
– No proven USP
– Could not use credit data or auto data
• Addressed goals by asking, can we change the rules of the affinity marketing game?
– What are the major drivers of value for direct insurance?
– What are XYZ’s objectives and how do they measure success?
– Is XYZ focusing first on doing the right things, then on doing things right?
– How can we XYZ marketing more efficient?
– What are the marketing levers we can pull? Operational levers?
– What is XYZ’s biggest unsolved problem?
– What can’t we change about XYZ?
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5. Strategy Recommendation
• Identified 4 critical dimensions for improvement
THEN STATE INTERIM STATE FUTURE STATE
• Volume • Gross & Net • CPA
• CPP Response • Return per Marketing $
• Unknown • Revenue • NPV
performance Optimization Key: Goal Alignment
• Mail complete list • Datamart build • Response & revenue models
• Largest affinities 1st • OLAP tool • Profile for creative
• No profiling • Response model • Remail based on value
• Model on outside lists
• Monthly print and • Test 3 month print • Semi-annual print
imaging runs and imaging by drop
• Staggered drops • Imaging by drop • Staggered drops
based on volume based on value
• Printed Self Mailers & • Test and Learn • Control v. Challenger
Oversized PCs creative platform pipeline
• Client approval • Variable copy by • Templates
needed every time affinity type • Variable copy by
• Low variability affinity & buyer type
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6. Execution – Measurement
CHALLENGE:
• Moving XYZ from focusing on CPP to demand-based success measures
– CPP was a holdover from operational, cost center focus
– Sense that we were possibly overcharging them
– P&L ownership resided with product managers with an underwriting focus, not marketing
managers with a sales focus
SOLUTION:
• Brought in a direct insurance consulting practice at no charge to client
– Great expertise in 2 partners
– Built confidence in our solutions and gave them insurance knowledge
• Bridged our bank experience to insurance and demonstrate how going from CPP to
Net Response to CPA and ultimately NPV was more aligned with XYZ’s objectives
• Also built an acquisition-retention model that showed why optimizing customer value
was better than maxing response or minimizing cost, by using
– Current Acquisition Cost, Hurdle Rate and Contribution Margin per Customer
– Current Conversion and Retention rates
– Estimated Ceiling Conversion and Retention rates
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7. Execution – Measurement (cont’d.)
• Optimization model example - inputs
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8. Execution – Measurement (cont’d.)
• Optimization model example - results
Acquisition Retention
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9. Execution – Data / Analytics
CHALLENGE:
• Operational mentality and contract arrangements limited audience selection options
– Biggest affinities held greatest sway and cross-affinity suppressions were impossible
SOLUTION:
• Datamart and OLAP to capture prospect and customer insight for client
– Great margin on something that helped Agency
– Housed at Agency, with XYZ access
• Nested Response and Revenue models identified the highest value prospects
– We recommended cutting at decile 5 based on expected value per piece mailed
• Picked records from among all affinities based on score
– Client chose to cut at decile 8 – were still captive to operational constraints
– Remailed through decile 2
• Results were fairly strong, though not optimized due to XYZ-dictated decile cuts
– Based on mailed population, achieved about 20% lift in expected value – but would have
been closer to 40%+ if not constrained by list source requirements of mailing households 1x
per year
– Datamart gave much better insight into prospects and eventual customers, by affinity type
and demo  greater client confidence in Agency by extension
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10. Execution – Data / Analytics (cont’d.)
• Gains curve shows the max k-s of the two cumulative response populations
– At decile 5, we saw ~25% separation from average
– Decile 8 was only ~10% lift, but revenue model added another 10%
 Gains Curve
 100%
 90%
 80%
 70%
 60%
 50%  Pct of Population
 Pct of Response
 40%  Response Gain
 30%
 20%
 10%
 0%
 1  2  3  4  5  6  7  8  9  10
 Decile
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11. Execution – Production
CHALLENGE:
• Cost per piece was hindering client efficiency, even though it was helping Agency
margins
• Client was not changing creative concepts frequently, but was tweaking copy non-
stop
SOLUTION:
• Proposed “locking down” the creative into a few Challenger templates, with some
portion of flexible imaged copy for affinity tailoring
– Allowed us to print for 3 months at a time initially
• Once winner packages were established, allowed us to move to 6 month print cycles
• Datamart and modeling eventually allowed us to go move from Drops to Waves
– Combined data processing lowered cost
– Could now suppress across affinities and send higher scoring affinity’s creative
– Still had to track records to ensure at least one mailing per household
• Reduced CPP by substantial amount
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12. Execution – Creative / Messaging
CHALLENGE:
• No test and learn culture – mailed same pieces over and over without testing format
or message
• USP was not well defined and appealed primarily to low price
SOLUTION:
• Set up testing of control creative vs. challengers
• Profiling shaped messaging to prospects
– Customized packages based on affinity’s value
• Challenger USP (value due to membership) against Control USP (low price)
• Package, Contact frequency, List Source and Remail lift tested
– Challenger #10 template beat and Challenger OPC matched Control SM performance
– Remail actually out performed initial mail by more than 10%
– OPC could be used instead of SM for remail (more economic)
– Certain affinities outperformed others, sometimes substantially
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13. Scorecard
• How did we do?
THEN STATE INTERIM STATE FUTURE STATE
• Volume  Gross & Net  CPA
• CPP Response  Return per Marketing $
• Unknown  Revenue o NPV
performance Optimization
• Mail complete list  Datamart build  Response & revenue models
• Largest affinities 1st  OLAP tool  Profile for creative
• No profiling  Response model  Remail based on value
o Model on outside lists
• Monthly print and  Test 3 month print  Semi-annual print
imaging runs and imaging by drop
• Staggered drops  Imaging by drop  Staggered drops
based on volume based on value
• Printed Self Mailers &  Test and Learn  Control v.
Oversized PCs creative platform Challenger pipeline
• Client approval  Variable copy by  Templates
needed every time affinity type  Variable copy by
• Low variability affinity & buyer type
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14. Summary
• Great internal press for client – went from less than 5% to 12% of XYZ unit sales
in two years and expanded department by 2 FTE
• Lowered direct marketing CPA – decreased by a cumulative 45% in < 2 years
• Huge volume increase for Agency – increased mail quantity from
2-3MM to 19MM pieces per year
• Agency and XYZ negotiated tiered pricing – lowered CPP based on the number
of pieces mailed annually
– While margin decreased, profit increased tremendously
• Became largest Agency client – more than $9MM per year (non-pass through
revenue)
Client grew into a true DBM function, gained added credibility within XYZ, and
Agency expanded the relationship dramatically over two years
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