Wayne Wilkins

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•  Setting the Stage

•  Challenges

•  Strategy Recommendation

•  Execution

•  Summary

Roadmap

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•  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

Setting the Stage

Proposed migration from mass mail shop to a disciplined database marketing organization

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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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•  Identified 4 critical dimensions for improvement

THEN STATE INTERIM STATE FUTURE STATE

Strategy Recommendation

• Mail complete list • Largest affinities 1st • No profiling

• Response & revenue models • Profile for creative • Remail based on value • Model on outside lists

• Monthly print and imaging

• Staggered drops based on volume

• Test 3 month print runs

• Imaging by drop

• Semi-annual print and imaging by drop

• Staggered drops based on value

• Printed Self Mailers & Oversized PCs

• Client approval needed every time

• Low variability

• Test and Learn creative platform

• Variable copy by affinity type

• Control v. Challenger pipeline

• Templates • Variable copy by

affinity & buyer type

• Volume • CPP • Unknown

performance

• Gross & Net Response

• Revenue Optimization

• CPA • Return per Marketing $ • NPV Key: Goal Alignment

• Datamart build • OLAP tool • Response model

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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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Execution – Measurement (cont’d.)

•  Optimization model example - inputs

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Execution – Measurement (cont’d.)

•  Optimization model example - results

Acquisition Retention

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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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•  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%

Execution – Data / Analytics (cont’d.)

 Gains Curve

 0%  10%  20%  30%  40%  50%  60%  70%  80%  90%  100%

 1  2  3  6  7  8  9  10

 Pct of Population  Pct of Response  Response Gain

 4  5  Decile

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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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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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•  How did we do?

THEN STATE INTERIM STATE FUTURE STATE

Scorecard

• Mail complete list • Largest affinities 1st • No profiling

 Response & revenue models  Profile for creative  Remail based on value o  Model on outside lists

• Monthly print and imaging

• Staggered drops based on volume

 Test 3 month print runs

 Imaging by drop

 Semi-annual print and imaging by drop

 Staggered drops based on value

• Printed Self Mailers & Oversized PCs

• Client approval needed every time

• Low variability

 Test and Learn creative platform

 Variable copy by affinity type

 Control v. Challenger pipeline

 Templates  Variable copy by

affinity & buyer type

• Volume • CPP • Unknown

performance

 Gross & Net Response

 Revenue Optimization

 CPA  Return per Marketing $ o  NPV

 Datamart build  OLAP tool  Response model

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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