2. 2
Overview
This report summarizes a process that was used to create a quantitative selection
process that focuses on characteristics of current business practices and
can be used to maximize our TV exposure for funded 1st mortgages by:
* profiling data and comparing it to national profiles
* providing profiles on specific outcomes of our marketing efforts
* describing how to integrate Nielsen TV ratings to these key demographics
* creating key metrics to rate each Channel by daypart
* combining these metrics into a single composite score
* using these scores to pick BEST channels by daypart
* compare this outcome with current AD purchasing
3. 3
Background, rational and setup
We used a data base profiling approach that looked at different outcomes of all 2006
business and included
83,996 fundings (26,437 1st Mortgages and 55,229 2nd Mortgages),
422,398 cancellations (237,108 1st Mortgages and 162,567 2nd Mortgages), and
100,000 dead leads (single time callers).
The first step was to append a commercial segmentation scheme called PRIZM to each
record. This segmentation classifies each record into 1 of 66 different clusters based upon
age, income, marital status, home owner status, household composition, ethnicity, and
geographic location. We calculated an index against the National profile and identified
13 clusters where we over-penetrate (index 135+) for 1st Mortgages
19 clusters where we over-penetrate (index 135+) for 2nd Mortgages
13 clusters where we over-penetrate (index 135+) for 1st Mortgages Cancellation
18 clusters where we over-penetrate (index 135+) for 2nd Mortgages Cancellation
4. 4
The clusters where is dominant for 1st Mortgages
Label COUNT PERCENT HH USPercent index Urbanicity HH Income
White Picket Fences 652 2.47 1,403,531 1.25 1.97 Second City Midscale
Kids and Cul-de-Sacs 832 3.15 1,828,699 1.63 1.93 Suburban Upper-Mid
Upward Bound 785 2.97 1,793,920 1.6 1.86 Second City Upscale
American Dreams 1,004 3.8 2,447,099 2.18 1.74 Urban Midscale
New Homesteaders 919 3.48 2,254,616 2.01 1.73 Town Upper-Mid
Beltway Boomers 416 1.57 1,079,269 0.96 1.64 Suburban Upper-Mid
Fast-Track Families 713 2.7 1,950,575 1.74 1.55 Town/Rural Upscale
Blue-Chip Blues 489 1.85 1,400,592 1.25 1.48 Suburban Midscale
Winner's Circle 412 1.56 1,239,200 1.1 1.42 Suburban Wealthy
Suburban Sprawl 490 1.85 1,473,003 1.31 1.41 Suburban Midscale
Kid Country, USA 489 1.85 1,500,755 1.34 1.38 Town Lower-Mid
Home Sweet Home 669 2.53 2,062,147 1.84 1.38 Suburban Upper-Mid
The Cosmopolitans 422 1.6 1,317,884 1.17 1.36 Urban Midscale
Label HH Age Range HH Comp HH Tenure HH Education HH Employment HH Race & Ethnicity HH IPA
White Picket Fences Age 25-44 HH w/ Kids Mix, Owners Some College BC, Srv, Mix W, B, A, H, Mix Moderate
Kids and Cul-de-Sacs Age 25-44 HH w/ Kids Mostly Owners College Grad WC, Mix W, A, H, Mix Above Avg.
Upward Bound Age 35-54 HH w/ Kids Mostly Owners College Grad WC, Mix W, A, H, Mix Above Avg.
American Dreams Age 35-54 Mostly w/ Kids Homeowners Some College WC, Srv, Mix W, B, A, H, Mix Above Avg.
New Homesteaders Age 25-44 HH w/ Kids Mostly Owners College Grad BC, Srv, Mix W Above Avg.
Beltway Boomers Age 45-64 HH w/ Kids Mostly Owners College Grad WC, Mix W, B, A, Mix Above Avg.
Fast-Track Families Age 35-54 HH w/ Kids Mostly Owners College Grad Management W Above Avg.
Blue-Chip Blues Age 25-44 HH w/ Kids Mix, Owners Some College BC, Srv, Mix W, B, A, H, Mix Below Avg.
Winner's Circle Age 25-44 HH w/ Kids Mostly Owners Grad Plus Management W, A, Mix High
Suburban Sprawl Age 35-54 HH w/o Kids Homeowners College Grad Professional W, B, A, Mix Moderate
Kid Country, USA Age 25-44 HH w/ Kids Mix, Owners H.S. Grad BC, Srv, Mix W, B, H, Mix Below Avg.
Home Sweet Home Age <55 HH w/o Kids Mostly Owners College Grad Professional W, B, A, Mix Above Avg.
The Cosmopolitans Age 55+ Mostly w/o Kids Homeowners Some College WC, Mix W, B, A, H, Mix High
5. 5
The clusters where is dominant for 2nd Mortgages
Label COUNT PERCENT HH USPercent index Urbanicity HH Income
Kids and Cul-de-Sacs 2,076 3.76 1,828,699 1.63 2.31 Suburban Upper-Mid
Upward Bound 1,934 3.50 1,793,920 1.60 2.19 Second City Upscale
New Homesteaders 2,207 4.00 2,254,616 2.01 1.99 Town Upper-Mid
Winner's Circle 1,163 2.11 1,239,200 1.10 1.91 Suburban Wealthy
Fast-Track Families 1,724 3.12 1,950,575 1.74 1.79 Town/Rural Upscale
White Picket Fences 1,236 2.24 1,403,531 1.25 1.79 Second City Midscale
Country Squires 1,866 3.38 2,152,742 1.92 1.76 Town/Rural Upscale
Beltway Boomers 900 1.63 1,079,269 0.96 1.70 Suburban Upper-Mid
Greenbelt Sports 1,294 2.34 1,612,141 1.44 1.63 Town/Rural Upper-Mid
God's Country 1,347 2.44 1,735,899 1.55 1.57 Town/Rural Upscale
Brite Lites, Li'l City 1,282 2.32 1,684,994 1.50 1.55 Second City Upscale
Home Sweet Home 1,552 2.81 2,062,147 1.84 1.53 Suburban Upper-Mid
Movers and Shakers 1,343 2.43 1,807,572 1.61 1.51 Suburban Wealthy
American Dreams 1,795 3.25 2,447,099 2.18 1.49 Urban Midscale
Big Sky Families 1,472 2.67 2,014,484 1.79 1.49 Rural Upper-Mid
Country Casuals 1,274 2.31 1,807,787 1.61 1.43 Town/Rural Upscale
Blue-Chip Blues 970 1.76 1,400,592 1.25 1.41 Suburban Midscale
Pools and Patios 1,009 1.83 1,470,884 1.31 1.39 Suburban Upper-Mid
Blue Blood Estates 731 1.32 1,094,703 0.98 1.35 Suburban Wealthy
Label HH Age Range HH Comp HH Tenure HH Education HH Employment HH Race & Ethnicity HH IPA
Kids and Cul-de-Sacs Age 25-44 HH w/ Kids Mostly Owners College Grad WC, Mix W, A, H, Mix Above Avg.
Upward Bound Age 35-54 HH w/ Kids Mostly Owners College Grad WC, Mix W, A, H, Mix Above Avg.
New Homesteaders Age 25-44 HH w/ Kids Mostly Owners College Grad BC, Srv, Mix W Above Avg.
Winner's Circle Age 25-44 HH w/ Kids Mostly Owners Grad Plus Management W, A, Mix High
Fast-Track Families Age 35-54 HH w/ Kids Mostly Owners College Grad Management W Above Avg.
White Picket Fences Age 25-44 HH w/ Kids Mix, Owners Some College BC, Srv, Mix W, B, A, H, Mix Moderate
Country Squires Age 35-54 HH w/ Kids Mostly Owners Grad Plus Management W High
Beltway Boomers Age 45-64 HH w/ Kids Mostly Owners College Grad WC, Mix W, B, A, Mix Above Avg.
Greenbelt Sports Age 35-54 HH w/o Kids Mostly Owners College Grad WC, Mix W Above Avg.
God's Country Age 35-54 HH w/o Kids Mostly Owners College Grad Management W High
Brite Lites, Li'l City Age 35-54 HH w/o Kids Mostly Owners College Grad Professional W, A, Mix Above Avg.
Home Sweet Home Age <55 HH w/o Kids Mostly Owners College Grad Professional W, B, A, Mix Above Avg.
Movers and Shakers Age 35-54 HH w/o Kids Mostly Owners Grad Plus Management W, A, Mix High
American Dreams Age 35-54 Mostly w/ Kids Homeowners Some College WC, Srv, Mix W, B, A, H, Mix Above Avg.
Big Sky Families Age 25-44 HH w/ Kids Mostly Owners Some College BC, Srv, Mix W Moderate
Country Casuals Age 35-54 HH w/o Kids Mostly Owners College Grad Management W High
Blue-Chip Blues Age 25-44 HH w/ Kids Mix, Owners Some College BC, Srv, Mix W, B, A, H, Mix Below Avg.
Pools and Patios Age 45-64 HH w/o Kids Mostly Owners College Grad Professional W, A, Mix High
Blue Blood Estates Age 45-64 HH w/ Kids Mostly Owners Grad Plus Management W, A, Mix High
6. 6
Where are cancels and single time callers coming from ?
The same process was repeated for cancellations, but we are reserving identifying these
clusters until we can differentiate applications that were denied versus true cancels.
Single time callers that never pursued an application were labeled dead leads (see below)
Label COUNT PERCENT HH USPercent index Urbanicity HH Income
Beltway Boomers 611.00 1.35 1,079,269.00 0.96 1.40 Suburban Upper-Mid
Blue Highways 899.00 1.98 1,644,447.00 1.46 1.36 Rural Lower-Mid
Upward Bound 985.00 2.17 1,793,920.00 1.6 1.36 Second City Upscale
Fast-Track Families 1,071.00 2.36 1,950,575.00 1.74 1.36 Town/Rural Upscale
New Homesteaders 1,236.00 2.73 2,254,616.00 2.01 1.36 Town Upper-Mid
Kids and Cul-de-Sacs 999.00 2.20 1,828,699.00 1.63 1.35 Suburban Upper-Mid
Label HH Age Range HH Comp HH Tenure HH Education HH Employment HH Race & Ethnicity HH IPA
Beltway Boomers Age 45-64 HH w/ Kids Mostly Owners College Grad WC, Mix W, B, A, Mix Above Avg.
Blue Highways Age 35-54 HH w/o Kids Homeowners H.S. Grad BC, Srv, Mix W Moderate
Upward Bound Age 35-54 HH w/ Kids Mostly Owners College Grad WC, Mix W, A, H, Mix Above Avg.
Fast-Track Families Age 35-54 HH w/ Kids Mostly Owners College Grad Management W Above Avg.
New Homesteaders Age 25-44 HH w/ Kids Mostly Owners College Grad BC, Srv, Mix W Above Avg.
Kids and Cul-de-Sacs Age 25-44 HH w/ Kids Mostly Owners College Grad WC, Mix W, A, H, Mix Above Avg.
7. 7
How to identify Total US Mortgage Market through
a definitional set of criteria within PRIZM
Segment Nickname Urbanicity HH Income HH Age Range HH Comp HH Tenure HH Education HH Employment HH Race & Ethnicity
Upper Crust Suburban Wealthy Age 45-64 HH w/o Kids Mostly Owners Grad Plus Professional W, A, Mix
Blue Blood Estates Suburban Wealthy Age 45-64 HH w/ Kids Mostly Owners Grad Plus Management W, A, Mix
Movers & Shakers Suburban Wealthy Age 35-54 HH w/o Kids Mostly Owners Grad Plus Management W, A, Mix
Young Digerati Urban Upscale Age 25-44 Family Mix Mix, Owners Grad Plus Professional W, A, H, Mix
Country Squires Town/Rural Upscale Age 35-54 HH w/ Kids Mostly Owners Grad Plus Management W
Winner's Circle Suburban Wealthy Age 25-44 HH w/ Kids Mostly Owners Grad Plus Management W, A, Mix
Money & Brains Urban Upscale Age 45-64 Family Mix Mostly Owners Grad Plus Professional W, A, H, Mix
Executive Suites Suburban Upper-Mid Age 35-54 HH w/o Kids Mostly Owners College Grad Professional W, A, Mix
Big Fish, Small Pond Town/Rural Upscale Age 45-64 HH w/o Kids Mostly Owners Grad Plus Management W
Second City Elite Second City Upscale Age 45-64 HH w/o Kids Mostly Owners Grad Plus WC, Mix W
God's Country Town/Rural Upscale Age 35-54 HH w/o Kids Mostly Owners College Grad Management W
Brite Lites, Li'l City Second City Upscale Age 35-54 HH w/o Kids Mostly Owners College Grad Professional W, A, Mix
Upward Bound Second City Upscale Age 35-54 HH w/ Kids Mostly Owners College Grad WC, Mix W, A, H, Mix
New Empty Nests Suburban Upper-Mid Age 65+ HH w/o Kids Mostly Owners College Grad Retired W
Pools & Patios Suburban Upper-Mid Age 45-64 HH w/o Kids Mostly Owners College Grad Professional W, A, Mix
Beltway Boomers Suburban Upper-Mid Age 45-64 HH w/ Kids Mostly Owners College Grad WC, Mix W, B, A, Mix
Kids & Cul-de-sacs Suburban Upper-Mid Age 25-44 HH w/ Kids Mostly Owners College Grad WC, Mix W, A, H, Mix
Home Sweet Home Suburban Upper-Mid Age <55 HH w/o Kids Mostly Owners College Grad Professional W, B, A, Mix
Fast-Track Families Town/Rural Upscale Age 35-54 HH w/ Kids Mostly Owners College Grad Management W
Gray Power Suburban Midscale Age 65+ Mostly w/o Kids Mostly Owners College Grad Retired W
Greenbelt Sports Town/Rural Upper-Mid Age 35-54 HH w/o Kids Mostly Owners College Grad WC, Mix W
Country Casuals Town/Rural Upscale Age 35-54 HH w/o Kids Mostly Owners College Grad Management W
The Cosmopolitans Urban Midscale Age 55+ Mostly w/o Kids Homeowners Some College WC, Mix W, B, A, H, Mix
Middleburg Managers Second City Midscale Age 45-64 HH w/o Kids Mostly Owners Some College WC, Mix W
Traditional Times Town/Rural Upper-Mid Age 55+ HH w/o Kids Mostly Owners Some College WC, Mix W
American Dreams Urban Midscale Age 35-54 Mostly w/ Kids Homeowners Some College WC, Srv, Mix W, B, A, H, Mix
Suburban Sprawl Suburban Midscale Age 35-54 HH w/o Kids Homeowners College Grad Professional W, B, A, Mix
New Homesteaders Town Upper-Mid Age 25-44 HH w/ Kids Mostly Owners College Grad BC, Srv, Mix W
Big Sky Families Rural Upper-Mid Age 25-44 HH w/ Kids Mostly Owners Some College BC, Srv, Mix W
White Picket Fences Second City Midscale Age 25-44 HH w/ Kids Mix, Owners Some College BC, Srv, Mix W, B, A, H, Mix
Blue-Chip Blues Suburban Midscale Age 25-44 HH w/ Kids Mix, Owners Some College BC, Srv, Mix W, B, A, H, Mix
Mayberry-ville Town/Rural Upper-Mid Age 35-54 HH w/o Kids Mostly Owners H.S. Grad BC, Srv, Mix W
Domestic Duos Suburban Midscale Age 55+ Mostly w/o Kids Mostly Owners H.S. Grad WC, Mix W, Mix
8. 8
How to get from clusters to TV Nielsen Ratings and Channel dominance
While earlier slides identify where the brand is dominant given their current
advertising, the previous slide used PRIZM to define the total presence of home mortgage
by using the same characteristics that described that current business to estimate a total
Mortgage Market. Results came from a key informant that concluded 33 clusters were needed.
This resulted in 6 profiles that can be described from a segmentation perspective using
dominant clusters (Funded 1st and 2nd, Cancelled 1st and 2nd , Dead Leads, and Target 33 )
These profiles were then used to provide TV viewing behavior across 80 cable channels by
the four major dayparts (early morning, day time, early fringe, and prime time) *. For each
channel Nielsen provides a key audience metric that counts the total eyeball watching at the
midpoint of every 15 minute interval within that daypart. These numbers were summed from
the first quarter 2007.
Eight unique performance indexes were created to rate each channel and then quantitatively
pick out those whose audience is best suited for our 1st mortgage product. Creatively using
these indicator maximizes our pull and dominance while minimizing cancels and dead leads
* ON and LN are currently available for Target 33 and dead lead only
9. 9
Performance Index Definitions by Daypart
Index33 = (Channel Minute 33 / Total Minute33) /
(Total Minute Daypart / Grand Total Minute Daypart)
This used a definitional set of clusters to calculate Total Homeowner Market Potential
FF_Index =(Channel Funded 1st Minutes / Total Funded First Minutes) /
(Total Minute Daypart / Grand Total Minute Daypart)
This used PRIZM matches against 26,437 Funded Firsts in 2006 that index 135+
FS_Index =(Channel Funded 2nd Minutes / Total Funded 2nd Minutes) /
(Total Minute Daypart / Grand Total Minute Daypart)
This used PRIZM matches against 55,229 Funded 2nd in 2006 that index 135+
CF_Index =(Channel Cancel First Minutes / Total Cancel First Minutes) /
(Total Minute Daypart / Grand Total Minute Daypart)
This used PRIZM matches against 237,000 cancelled Firsts in 2006 that index 135+
CS_Index =(Channel Cancel 2nd Minutes / Total Cancel 2nd Minutes) /
(Total Minute Daypart / Grand Total Minute Daypart)
This used PRIZM matches against 162,000 cancelled 2nd’s in 2006 that index 135+
10. 10
Performance Index Definitions by Daypart
Deadlead _Index =(Channel Deadlead Minutes / Total Deadlead Minutes) /
(Total Minute Daypart / Grand Total Minute Daypart)
Using a sample of 100,000 phone numbers of one time callers that never called
back, a reverse address append resulted in 45,000 PRIZM coded records
Pull_Index = (Channel Minute 33 / Channel Total Minute) / Mean Pull
This index measures the percent of total eyeball minutes seen by Target 33’s indexed to
the grand average percent for that daypart. The higher the index then more minutes of
that channel are being seen by our target 33 profile
pull=target_33_minutes/total_minutes;
pull_index=pull / mean pull for that daypart
Dom_index = (Channel Target33 minutes / Grand Total Target33 Minutes) / Mean Dominance
This index measures the dominance in total minutes of each channel indexed to the
average dominance for that daypart
dominance=total target33_minutes by channel / grand total target33 minute
dom_index=dominance / mean dominance for that daypart
11. 11
Comparison to March 2007 Early Morning Spot Picks
Top 10 Quantitative Selections in BLUE
Channel COUNT PERCENT Channel COUNT PERCENT
FIT 171 5.6195 HI 36 1.1830
FCS 162 5.3237 GAMC 34 1.1173
CW+ 134 4.4035 FXSC 33 1.0845
MSNB 131 4.3050 BBCA 32 1.0516
FX 116 3.8120 SLEU 32 1.0516
A&E 102 3.3520 ESPN2 30 0.9859
TOC 98 3.2205 SOAP 28 0.9201
STYL 97 3.1876 DIY 27 0.8873
HGTV 94 3.0891 BETJ 25 0.8216
TMC 93 3.0562 DISM 23 0.7558
CNN 91 2.9905 WGNS 23 0.7558
OXY 91 2.9905 ESPC 20 0.6572
HLN 86 2.8262 GAME 20 0.6572
HIST 85 2.7933 FOOD 19 0.6244
DSCH 82 2.6947 BIO 17 0.5587
FNEW 82 2.6947 TNT 16 0.5258
HOM 79 2.5961 TWC 16 0.5258
SCFI 78 2.5633 FXRE 12 0.3943
TBS 73 2.3989 ANPL 11 0.3615
HELV 72 2.3661 DSKD 8 0.2629
TTC 69 2.2675 TRAV 6 0.1972
CRNT 67 2.2018 HALL 5 0.1643
OUTD 61 2.0046 USA 5 0.1643
TVL 51 1.6760 DSCN 4 0.1314
CNBC 48 1.5774 DISC 2 0.0657
CMT 41 1.3474 DIST 2 0.0657
CSTV 41 1.3474 FINE 2 0.0657
AMC 40 1.3145 FSN 2 0.0657
BRAV 39 1.2816 G4 2 0.0657
NBA 38 1.2488 SPKE 2 0.0657
ESNW 36 1.1830 BFC 1 0.0329
This page shows what the
Past purchase plan was for
March 2007. The top 10
Quantitaive selections are
highlighted in BLUE. Comments
are that this appears to be
Sub-optimal and fragmented.
Note the top select for this
Daypart BBCA is number 35
and was purchased at only 1%.
Additional dayparts were also
done but not included.
12. Using Jan-March 2007, Grand Total Spots per month rounds to 20,000
March07 19,422 spots were distributed over various day parts as follows:
EM= 15.6% DT=37% EF=32.45% PR=7.96% LN=3.9% ON=2.9%
EM= 3,043 DT=7,188 EF=6,303 PR=1,546 LN=773 ON=569
Using the PRIZM coded Nielsen Reports distributions, suggests this pattern
EM= 9.5% DT=30.5% EF=25.7% PR=34.0%
EM= 1,845 DT=5,923 EF=4,991 PR=6,603
The following pages detail each channel’s specific profile using
8 unique performance indexes that will weight us heavier into PRIME Daypart
AND increase our funded 1st portfolio by focusing our AD buy in targeted spots
Why we should shift our spending to PRIME Daypart
12
13. 13
How to create a pick list from the Master file
• There are many ways to pick from this master list (see attached Updated Master
.xls) and some of the indexes are to be included (target33, FF, FS) while others are
to be excluded (deadlead, CF and CS). In addition two other indexes profile both the
pull or reach of a station to our target 33 profile as well as the dominance of that
channel to draw total eyeball minutes within that daypart.
• Cost information is only available for channels that were previously purchased. A
linear program was executed by the team to assign an optimal number of spots per
daypart (see attached LP solution). One issue with this approach is that fact that NO
major shift to Prime Time is delivered
• Using a factoring approach that assigned a statistical distance between channels was
calculated. Two composite scores were created – one to maximize our pull of
mortgage applicants, then second weighted to maximize FUNDED applicants.The
idea is choose channels with HIGH Target 33 AND FF profiles, but LOW cancel first
and LOW deadlead profile while pulling HIGH and being dominant with that daypart
