Strategic Data Sourcing 09221011 - Merkle Inc.€¦ · OPTIMIZATION Usinggy analytics to drive data...

STRATEGIC DATASTRATEGIC DATA SOURCING AND OPTIMIZATION

Using analytics to drive data decisions for the g yInsurance & Wealth Management Market

Agenda

Setting The Solution

The Data Landscape

Data Evaluation & OptimizationData Evaluation & Optimization

The Value of Data

Data Optimizationp

Data Acquisition

Case Study: AARPCase Study: AARP

© 2011 Merkle Inc. All Rights Reserved. Confidential 2

Value of this Presentation

If data is the fuel that analytics uses to create insight that drives strategy, how do you identify, evaluate, optimize and acquire the most valuable data?

Prospect Universe

Shoppers

Inquirers

Non-Converter/UW Reject

Leverage data and analytics to optimize

Customers

analytics to optimize the migration of high-value customer segments throughout th t lif l

High-Value CustomerMid-Value CustomerL V l C t

Unknown

Segment 3Segment 1 Segment 2Product AProduct B

the customer lifecycleLow-Value Customer


Multi-Product

The Data Landscape


The Data Landscape

COMPANY TYPES

COMPILERS LIST MANAGEMENT SPECIALTY COMPILERS

CREDIT DATA

SEGMENTATION TOOL PROVIDERS

SYNDICATED RESEARCH

DIGITAL DATA

Panel data AggregatorsDemographics & Firmographics Response Data

Lifestyle/Behavioral, Realty, Transactional, Life Events

Credit Scores, Credit Attributes

Generic Clusters - utilizing attitudinal, demographics, or credit information

Panel data representing consumer attitudes & behaviors

Aggregators, Owners, Audience, Analytics


Cutting Through The ‘Noise’

Determining the most valuable mix of information, from across the hundreds of data providers covering the U.S. Consumer and Business populations is the goalpopulations is the goal

Optimal Mix

==© 2011 Merkle Inc. All Rights Reserved. Confidential 6

Data Evaluation & Optimization


Dual Path to Data Optimization

Top Down Approach “List Optimization”

Optimize the existing data seeking ways to cut costs while maintaining or improving performance. Eliminate compiled or response

Continuous Optimization &

p psources based on true ROI

Evaluate each source of current and new data for value keeping the optimal mix. Analytic

A t f

Bottoms Up Approach “Content Lab”

Credit Data, Trigger Data, Life Events – New Movers Affinity Partners

Assessment of New Data Sources


Key Data Value

Data

What Do We know About Them?

Marketable Names & Addresses

Accuracy Coverage

Hygiene Reach Overlap

Targeting

Predicting P f Profiling Insightyg p Performance g g


The Content Lab

The key value drivers of a list are analyzed to arrive at a composite score for quantitative evaluation and comparative assessment of each list.

Demographic

Content Lab


Identification of New Sources Using Dry Testing


Vertical List Dry Testing Recommendation

Rank order list sample files using a weightedDevelop a model on customers

Clone or Response Model

2 Rank order list sample files using a weighted combination of lift, universe size and cost.

1 Develop a model on customers or responders.

Ranked List

15 00%

20.00%

25.00%

30.00%

35.00%

0.00%

5.00%

10.00%

15.00%

1 2 3 4 5 6 7 8 9 10

Sample Clone Rate (Unweighted) Cum Clone Rate (Unweighted)


List Optimization Dynamics

The purpose of the list optimization process is to balance cost and value.

Maximize List Value

Increase PerformanceExpand Universe

Minimize List Cost

Reduce List CostsReduce List CostsReduce Run ChargesReduce Duplication


Overlap Is Expensive

List Li t

Existing Universe Lists Future Universe Lists

List List

List List

List List

List

List

List

List List

List List

List

List List List

ListList

List

List List

List

List List List

List List

List List

List

List

List

List

“N” listsInform the source / list pool and universe optimization process

ROI

# of ListsN lists

universe optimization process with analytics to define the right mix and number of lists that maximize ROI


N lists

Unbiased List Attribution

Unbiased response attribution enables accurate list level performance analysis by analyzing list sources prior to merge-purge

Response is randomly assigned to a single list, typically the list that got paid. R i i li t d t t th dit

TRADITIONAL RESPONSE ATTRIBUTION 1

2Random Assignment

Remaining lists do not get the credit hence resulting in incomplete attribution

3

4

3

Response is assigned to each of the

UN-BIASED (APPEARED-ON) RESPONSE ATTRIBUTION

1

2

1

2Un-Biased

espo se s ass g ed o eac o elists on which the individual exists 3

4

3

4


List Optimization Process

List Universe

Historical Lists

1List

Universe (Unique ID Assigned)

Fresh Testing ListsReuse Lists

List Universe Creation

1

List Universe (Complete

Updated List Size and Cost

BI ReportsCredited-on RRData

Append2

(Complete Data) Campaign Files

Appeared-on RR, OverlapPost Merge Report

Mail Qty, %Net

O ti i ti dOptimization

3

Mail Plan

Optimization and Mail Plan Creation

Engine3


Optimization Engine


Optimized Source Mix Illustration

The ratio of the Base File names increases in the optimized source mix scenario where the base file is a lower cost national file with net-net pricing.p g

Recognize Expensive Overlap

Alternative Appeared On Performance

Optimization Engine


Example: Bureau & Non-Bureau Data

Utilize less expensive and less restrictive non-bureau data a safe distance from non-standard prospects and bureau data to be more precise as you get closer to

t d d tnon-standard prospects.

St d d P d tStandard ProductData Mix

Bureau Non-Bureau


Data Acquisition


List Universe Creation (for optimization)

The list acquisition team plays a critical role in the creation of the list universe used for optimization.

ID creation for each unique list + select combination

Identification of strong performing lists

Available reuses that should be considered

Examination of prior test results

Exclusion of lists with high overlap Exclusion of lists with high overlap

List Universe

Historical Lists

1List

Universe (Unique ID Assigned)

Fresh Testing ListsReuse Lists

List Universe Creation

1

24

Data Append (for optimization)

Next the list acquisition team provides needed updates.

Update universe sizes Update universe sizes

Update negotiated lists costs

Update merge retentionsp g

Update results

List Universe (Complete

C

Updated List Size and Cost

BI ReportsCredited-on RRData

Append2

Data) Campaign FilesAppeared-on RR, Overlap

Post Merge ReportMail Qty, %Net

25

Mail Plan Creation (following optimization)

Pull it all together using the optimization output and verify

Optimal quantity for each segment makes sense Optimal quantity for each segment makes sense

Achieve or exceed expected # of responses for each

No list should make up more than 15% of total mail volume for each psegment.

Licensed data % consistent with prior campaigns

Multis consistent with prior campaigns Multis consistent with prior campaigns

Optimization andOptimization

Engine3

Mail Plan

Optimization and Mail Plan Creation

Engine

26

Mail Plan Source Summary (simulated example)

The Mail plan is a working environment where all columns are live formulas. Any changes that are made will automatically be reflected in the running totals.

Provides analysis for source mix distribution comparing against prior mailings to ensure continuity.

Campaign list mix totals are provided for a series of comparison by segment: source breakdowns, list type, and touches.

Variance columns are included so the plan is a continued vision into the campaign even after orders are placed, merge is run and results are received.


Case Study: AARP


Background

Founded in 1958, AARP is a nonprofit, nonpartisan membership i ti th t h l l 50 d i th lit forganization that helps people 50 and over improve the quality of

their lives.

AARP’s mission is to enhance the quality of life for all as we age, q y g ,leading positive social change, and delivering value to members through information, advocacy, and service.

M b hi d $16 f t Membership dues are $16 for a one year term.


Direct Mail @ AARP

>50% of new members

200MM pieces of mail/year

Monthly mailings


Direct Mail Performance

Response rates are decreasing

Thus, cost per order (CPO) is increasing

** Only way to keep CPO constant is to reduce costs**


CPO Drivers

Cost

Package/printing

Response

Package/treatment/offerPackage/printing

Postage

Package/treatment/offer

Brand relevance

Data Audience


Data Sourcing and Optimization Strategy

Engaged with Merkle to develop a Data Sourcing & Optimization Strategy

Began with a detailed analysis of the historical performance data and performed a comprehensive analysis in the Content Lab environmentenvironment

New analytical methodologies were applied to ensure we were buying the optimal combination of lists that would yield the highest y g p y gresponse and lowest costs

Attributed value to data for selectivity, not for the name/address alonealone


Source Mix – Typical Mail Month

6060

MM

)

3035 31

40

ames

(in

M

0.2

3020

110.3

2014

0

20

H C il d PDB R M ltib

Inpu

t Na

House Compiled PDB Response Multibuyer2010 2011

Even though Compiled names doubled, our cost was reducedEven though Compiled names doubled, our cost was reduced significantly by licensing a secondary file (primary file is used for PDB)

Response list costs have gone down even though input quantity has stayed constant = buying smarter


Impact

Annual Savings forecasted at +50%

No impact to performance

Improvement in nets of 5-8%

Improved List Deals Negotiated have also contributed significantly to total savings

Life Event Triggers expanded universe of 2 successful Life Event Triggers – expanded universe of 2 successful markets:

– New Movers: 3,000,000– Milestone Birthdays: 2,400,000

Vertical Dry List Testing – Test to Continuation: 85% Win Rate


Thank You

Gary RobinsonVP Enterprise Solutions

Merkle

Mary Ann BuoncristianoVP Data Solutions

Merkle

Sonia AlvarezDirector Analysis &

Marketing ExecutionMerkleMerkleAARP


Strategic Data Sourcing 09221011 - Merkle Inc.€¦ · OPTIMIZATION Usinggy analytics to drive data...

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Transcript of Strategic Data Sourcing 09221011 - Merkle Inc.€¦ · OPTIMIZATION Usinggy analytics to drive data...