market optimization phase vi

1

Market Optimization

Phase VI

May 2012

ji ,

i = 1, …, M customers j = 1, …, N offers

A = (aij) is a matrix where aij = 1, if offer j is to be targeted to prospect i and 0 otherwise

Maximize

Profit(A)= Aij*(profit,cost,score)ij

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Optimization & Market Expansion

Optimize current screen criteria to local market conditionsInclude all zip codes in current marketsIntegrated Data Warehouse “Buckets” across all sources - ITA, PA, Trigger, Vertical, TBD

Optimizing target selections via individual screens– Relevant Messages– Model Scores– Performance Scores by Branch, Channel, Branch,

Proprietary Targeting Cells

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FICO 41%

Income 4%

Home Equity 24%

Home Value0.12%

Age 25%

Credit Utilization

5%

Loan Date 1%

Single Family Dwellings Age Income Home Equity Home Value

FICO Trades Credit Utilization 2 Yrs History Loan Date

Unpaid Public Record Accounts Opened Judgement or Lien

Extreme Situation: Too tight of Screens and 91.% of Homeowners were screened out of Target base

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INCOME

0 7.59 <= 2500 1.96

11000 7.59 <=4999 3.08

15000 9.3 <=14999 15.84

25000 35.66 <=29999 35.43

30000 49.96 <=42499 50.34

50000 70.13 <=67499 70.85

60000 80.17 <=84999 80.59

70000 86.57 <=99999 86.16

621000 100 >=100000 100.00

TOTAL 56,363,152 109,297,000

TU US CENSUS DATA

Aggregated Incom per Household

Cumulative Percent

INCOME Range

Low-HighCumulative

Percent

Even though we aggregated income per household, TU

income is lower than Census Info.

TU has only individual level of Income, this is aggregated income per

household

Prospects with less than TU Defined $30K individual Income, eliminated over 15% of the market opportunity

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HOME EQUITY

STATE

ZIPCODE

TU Acxiom

Individual Count(2001) 19,360 19,567

HouseHold Count(2001) 8,658 7,592

Home Value(2001) 146,009 195,567

Home Equity(2001) 74,247 117,411

Individual Count(2002) 20,280 22,119

HouseHold Count(2002) 11,381 8,280

Home Value(2002) 229,320 293,328

Home Equity(2002) 122,550 139,853

Changes in Individual(%) 4.8% 13.0%

Changes in Household(%) 31.5% 9.1%

Changes in HomeEquity (%) 65.1% 19.1%

Changes in Home Value (%) 57.1% 50.0%

Changes in Individual(#) 920 2,552

Changes in Household(#) 2,723 688

Changes in Equity ($) 48,303 22,442

Changes in Home Value (%) 83,311 97,761

NY

11803

STATE ZIPCODE

Average Home Value

New Intellidyn

Home Value

FL 32501 133,840 135,860

STATE ZIPCODE

Average Home Value

New Intellidyn

Home NY 11803 229,320 234,800

New Intellidyn Home Value – Maximum of TU home value, Mortgage 1 balance or Mortgage 2 balance

About 5% homeowners have a loan which has higher mortgage balance than home value

01 - 02

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Scored individual records from multiple master filesSelects based on deciles from multiple bucketsAll phone numbers Verified/corrected for Telemarketing

Pre-determined mix of ITA/PA/Vertical

SameSelect based on model score from one aggregate bucketSolicitations by telemarketing and/or email based on model score and optimizationNo pre-determined mix of sources. Optimization chooses records most likely to respond and convert

Optimization Vs. Current Production

Current Production Intelli-Optimization

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Mathematical Function for Optimization The multidimensional optimization technique requires the exact mathematical

solution to the maximization or minimization of multi variable functions (i.e. business goal of a campaign such as profit, budget or sales).

ji ,

i = 1, …, M customers j = 1, …, N offers

A = (aij) is a matrix where aij = 1, if offer j is to be targeted to prospect i and 0 otherwise

Maximize

Profit(A)= Aij*(profit,cost,score)ij

The final objective of the analysis is to exactly determine which specific offers (aij) to target to each individual prospect simultaneously optimizing business goals while satisfying business rules/constraints and local market conditions.

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Required Input for Market Optimization

-Business Goals (Maximum Profit, Minimum Budget, Maximum Number of Sales)

-Dimensions (Offers by channels, etc. ): Campaign typically consists of several offers for targeting to a large set of prospects*. Each channel is associated with the following attributes necessary to calculate the business goal to be optimized:

Response Model score for calculating probability of a prospect to respond to the offer

Conversion Model score for calculating probability of a prospect to be funded.

Profitability model score for calculating the profitability of a prospect for the offer

Eligibility condition for determining eligible prospects for the offer

Economics such as the delivery costs and selling costs of the offer

-Constraints: Campaigns have several associated constraints such as budget limitations, minimum profit requirements and maximum number of offers that can be targeted to a prospect.

* Prospects for this example were a 10% sample of three buckets, ITA, PA, Vertical

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How Market Optimization Works

Calculates Net Present Value (NPV), expected profit, number of sales, etc. across multiple campaign parameters

Applies Constraints to determine eligibility of each prospect to each campaign (i.e.need a phone number for telemarketing)

Applies Business Rules (I.e. 1 offer per Household, Budget

constraints, etc.) to determine the optimal offer for each prospect

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Performance*, Score & Cost Metrics

ITA PA VerticalCost ($) 0.266 0.309 0.323

Ratio 1 1.1616541 1.2142857DM DM+TM DM+EM

Cost ($) 0.197 0.917 0.322Ratio 1 4.6548223 1.6345178

DM DM+TM DM+EMResp. Rate 0.0036758 0.014934 0.0048426

Ratio 1 4.0627971 1.3174355

Historical Performance

COST

Branch Conv. Rate Ratio

AS401 10.45 1.44AT411 5.83 0.80DL602 12.36 1.70DO403 6.07 0.84DT322 6.68 0.92FM414 7.31 1.00GR324 5.36 0.74HC404 8.26 1.14IN332 7.13 0.98JA405 6.57 0.90NV413 4.20 0.58OC407 7.34 1.01PB412 10.46 1.44PH312 11.34 1.56RE622 25.93 3.57SP408 7.75 1.07TP410 5.98 0.82VB341 4.80 0.66Total 7.27

Branch Performance

*All performance metrics are based on MIS report as of 4/4/2012

Resp. Score Conv. ScoreITA ITA Intellidyn Resp. Model ITA Intellidyn Conv. Model

PA PA OSM Resp. Model PA Intellidyn Conv. Model

Model Scores

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Score Function

Business Goal: Find most appropriate objective function

Probability to respond =( Response model score + Historical Performance by channel )/2

Probability to Convert =( Conversion model score + Historical Performance by channel )/2

Cost = Source cost + Cost by channel

Budget vs. Branch Budget Exercise: Optimize Budget allocation

Probability to respond = Response model score

Probability to Convert =( Conversion model score + Historical Performance by branch )/2

Cost = Source cost + average Cost for all channel

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Overall Scenario Results

Name Business Goal MIN Resp Max Resp MIN Loans MAX LoansMAX

Cost/CustMAX

Budget Exact BudgetMIN

Budget MIN OffersMAX

Offers

Scenario 1 MAX Loans 490,351 Scenario 2 MAX Loans 8,000 490,351 Scenario 3 MIN Budget 600 Scenario 4 MIN CPL 400,000 Scenario 5 MIN CPL 500,000 Scenario 6 MAX Responders 490,351 Scenario 7 MIN Budget 500

CONSTRAINTS

Name Responders Funded Loans CPL Budget OffersResponse Rate (bps)

Fund Rate (bps) Conv. Rate

Scenario 1 5,924 420 1,144 480,548 763,810 77.56 5.5 7.09Scenario 2 5,913 419 1,143 479,205 762,300 77.56 5.5 7.09Scenario 3 8,422 601 1,284 771,854 1,150,430 73.21 5.23 7.14Scenario 4 4,099 317 1,267 401,068 336,020 121.98 9.42 7.73Scenario 5 4,982 380 1,320 501,062 420,230 118.56 9.04 7.62Scenario 6 7,001 360 1,351 485,870 998,190 70.14 3.6 5.14Scenario 7 7,023 501 1,210 605,888 931,350 75.41 5.38 7.13

RESULTS

Red Highlighted numbers represent max/min for each column

Scenario 2 has lowest CPL but no other higher

results

Scenario 3 has most responders but Resp. Rate and

Fund Rates are low

Scenario 4 has highest fund rate but also a

higher CPL

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Business Goal : Max Loans

Name MIN Resp Max Resp MIN Loans MAX Loans MAX Cost/CustMAX BudgetExact BudgetMIN BudgetMIN Offers MAX Offers

Scenario 1 490,351 Scenario 2 8,000 490,351

Name RespondersFunded Loans CPL Budget Offers Elgbl Cust Response Rate (bps)Fund Rate (bps)Conv. Rate

Scenario 1 5,924 420 1,144 480,548 763,810 N/A 77.56 5.5 7.09 DM 4,472 274 1,099 300,913 613,600 9,531,110 72.89 4.46 6.12 TM 1,451 146 1,229 179,623 150,190 3,280,760 96.63 9.73 10.07 EM - - 784 13 20 172,530 47.35 8.05 16.99


BUSINESS GOAL: MAX LOANS

CONSTRAINTS

RESULTS

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Scenario 1 400,000 Scenario 2 500,000


Scenario 1 4,099 317 1,267 401,068 336,020 N/A 121.98 9.42 7.73 DM 63 4 555 2,487 4,950 9,531,110 126.78 9.05 7.14 TM 4,035 312 1,277 398,525 330,980 3,280,760 121.91 9.43 7.74 EM 1 - 667 56 90 172,530 137.01 9.38 6.85

Scenario 2 4,982 380 1,320 501,062 420,230 N/A 118.56 9.04 7.62 DM 80 6 579 3,261 6,490 9,531,110 122.52 8.68 7.08 TM 4,901 374 1,331 497,720 413,610 3,280,760 118.5 9.04 7.63 EM 2 - 715 82 130 172,530 125.87 8.78 6.97

BUSINESS GOAL: MIN CPL

CONSTRAINTS

RESULTS

Business Goal : Minimum Cost Per Loan

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Scenario 1 500 Scenario 2 600



Scenario 2 8,422 601 1,284 771,854 1,150,430 N/A 73.21 5.23 7.14 DM 5,610 339 1,231 417,767 853,800 9,531,110 65.7 3.98 6.05 TM 2,812 262 1,352 354,006 296,500 3,280,760 94.84 8.83 9.31 EM 1 - 1,157 81 130 172,530 40.77 5.37 13.17

CONSTRAINTS

BUSINESS GOAL: MIN Budget

Business Goal : Minimum Budget

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Business Goal : Minimum Budget


Track 1 490,351


Track 1 7,001 360 1,351 485,870 998,190 N/A 70.14 3.6 5.14 DM 7,001 360 1,351 485,870 998,190 9,531,110 70.14 3.6 5.14 TM - - N/A - - 3,280,760 N/A N/A N/A EM - - N/A - - 172,530 N/A N/A N/A

RESULTS

BUSINESS GOAL:MAX Responders

CONSTRAINTS

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Budget: Overall Vs. Branch

Name

Elgbl Cust

Responders

Budget by

BranchOverall Budget

Budget by


Budget by


Budget by


Budget by


Budget by


Budget by


Budget by


Overall 9,531,113 6,637 6,924 413 529 1,157 927 477,414 489,804 616,143 633,532 107.72 109.30 6.70 8.34 6.22 7.63 RENO 130,930 54 220 5 20 494 920 2,625 18,477 3,417 24,073 157.92 91.48 15.54 8.34 9.84 9.12 PLANO 1,183,186 149 1,635 16 159 307 889 4,896 141,407 6,242 182,233 238.95 89.75 25.56 8.73 10.70 9.73 PHNX 1,171,978 926 1,444 89 136 671 883 59,953 120,658 78,180 157,405 118.38 91.72 11.43 8.68 9.65 9.46 WPB 418,834 489 244 40 21 1,468 951 58,531 19,845 75,738 25,812 64.52 94.24 5.26 8.08 8.16 8.58 ALS 228,520 180 150 15 13 1,045 926 15,707 11,648 20,264 14,997 88.64 99.47 7.41 8.38 8.36 8.42 HRC 184,404 139 159 10 11 935 997 9,004 10,891 11,630 14,083 119.86 113.16 8.28 7.76 6.91 6.86 STP 206,829 169 89 10 5 1,457 1,003 15,283 5,655 19,882 7,318 85.03 121.47 5.28 7.71 6.21 6.35 OCL 88,161 43 37 2 2 1,093 1,000 2,616 2,032 3,357 2,603 127.81 140.12 7.13 7.81 5.58 5.57 FTM 34,346 69 14 4 1 2,488 985 9,077 780 11,539 1,005 59.40 131.02 3.16 7.88 5.32 6.01 INDY 2,196,924 1,327 1,379 79 82 947 967 75,217 79,692 97,308 103,108 136.38 133.75 8.16 7.99 5.98 5.97 CANTON 579,436 585 398 31 21 1,232 986 38,352 21,248 49,282 27,249 118.73 145.94 6.32 7.91 5.32 5.42 JAX 236,048 275 97 14 5 1,974 977 27,077 5,089 34,848 6,503 78.96 149.68 3.94 8.02 4.98 5.35 DTO 273,550 214 82 11 5 1,704 1,066 18,281 4,557 23,701 5,880 90.36 139.76 4.53 7.28 5.01 5.20 TPA 199,431 79 61 4 3 1,179 1,038 4,400 2,986 5,629 3,820 140.11 158.49 6.63 7.53 4.73 4.75 ATLANTA 528,264 258 142 12 7 1,419 1,075 17,410 7,370 22,123 9,358 116.83 152.17 5.55 7.33 4.75 4.82 GRD RAPID 654,811 539 379 24 17 1,228 1,031 28,989 17,276 37,271 22,194 144.63 170.82 6.33 7.55 4.38 4.42 VAB 428,986 286 81 11 3 2,060 1,069 22,651 3,523 28,868 4,473 99.09 180.94 3.81 7.37 3.84 4.07 NASHVILLE 479,916 576 109 18 3 2,566 1,091 47,421 4,112 61,163 5,237 94.20 207.96 3.02 7.20 3.21 3.46 MISSOURI 306,559 280 206 17 13 1,168 985 19,924 12,556 25,702 16,183 108.88 127.34 6.63 7.87 6.09 6.18

Budget by Branch (scenario 2 ) -vs- Overall Budget (scearion1)

CPLFunded Loans Budget OffersResponse Rate

(bps) Fund Rate (bps) Conv. Rate

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Market Optimization Next Steps

Use to Determine or test market Screens

Development of channel specific models will increase performance of Optimization Software

market optimization phase vi

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