The document describes the marketing intelligence services of Eleventy Group, including their large consumer databases containing demographic, behavioral and lifestyle information. They offer profiling, predictive modeling, and data enhancement services to help clients better target, segment, and communicate with audiences. Case studies show how these services helped clients improve targeting, test different audience characteristics, and enhance fundraising appeals.

1. MARKETING
INTELLIGENCE

2.  Have you ever wanted to reach…

3.  The eleventy marketing group’s innovative
intelligence enables you to…

4.  So that when your target audience is…

5.  You don’t reach…

6.  We have a database of…
 205 million records
 132 million households
 105 million phone numbers
 This allows eleventy to offer unmatched insights
into the...
 Attitudes
 Behaviors
 Lifestyles
 Demographics
 Of your donors/customers…

7. Demographic
Who am I?
Transactional
What have I done?
Psychographic
What do I do?

8. Demographic
Who am I?
Transactional
What have I done?
Psychographic
What do I do?

9.  We can offer those insights by…
 Profiling
 Predictive modeling
 Data enhancement/List creation
 Donor/Customer mapping
 This allows you to...
 Target
 Segment
 Enhance creative design
 Select the right channels
 Email – Direct mail – Telephone - Online
 To your donors/customers…

10.  Did you know…
 A major client thought their prospects were
predominantly male…
0%
10%
20%
30%
40%
50%
60%
70%
80%
DATAMETRIC
DATA ATTRIBUTE
Gender
Multi Percent Single Percent Universe Percent

11. 7%
8%
9%
10%
11%
12%
13%
14%
15%
16%
16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76+
ResponseRate
Age Group
Program Comparative Response Rates
PROGRAM A PROGRAM B AVERAGE
 Did you know…
 We were able to show this client they needed two
types of TM programs to reach a wider audience…
PROGRAM A
21 - 55
PROGRAM B
56 – 76+

13. -set individual column scores
UPDATE MODEL_ELEVENTY_02_24_2012_1.00a1_MOR
SET REGION_ID_11T = CASE WHEN REGION = 'AB'THEN 31.46
WHEN REGION = 'AT'THEN 25.93
WHEN REGION = 'BC'THEN 25.35
WHEN REGION = 'GT'THEN 23.28
WHEN REGION = 'MW'THEN 28.03
WHEN REGION = 'NE'THEN 25.61
WHEN REGION = 'PQ'THEN 23.05
WHEN REGION = 'SW'THEN 25.58
ELSE NULL END,
alter table MODEL_ELEVENTY_02_24_2012_1.00a1_MOR add sum_score_2 float,
mode_name_2 varchar(100),
model_score_2 tinyint
UPDATE MODEL_ELEVENTY_02_24_2012_1.00a1_MOR
SET sum_score_2 = ((( REGION_ID_11T - 25.640) / 2.620) * 0.050 ) +
((( PRICE_PLAN_SERIES_ID_11T - 25.640) / 0.540) * 0.013 ) +
((( NIN1_ID_11T - 25.650) / 0.920) * 0.027 ) +
((( COMMON_IND_ID_11T - 25.630) / 1.500) * 0.017 ) +
((( PAY_METHOD_ID_11T - 25.640) / 1.320) * 0.023 ) +
((( KEYCODE_ID_11T - 25.640) / 2.400) * 0.044 ) +
((( data_no_data_ID_11T - 25.680) / 1.820) * 0.020 ) +
((( MULTIPLE_ACTIVATIONS_ID_11T - 25.690) / 0.490) * 0.009 ) +
((( EMAIL_YN_ID_11T - 25.680) / 0.800) * 0.018 )

14.  We take the mystery out of modeling and deliver
actionable results…
Decile
Total Call
Universe
% of Total
Called
Cumulative %
Called
Total
Responders
% of Total
Responders
Cumulative %
Responders
Decile
Response Rate
Cumulative
Rate Marginal Lift Cumulative Lift
Most Likely 1 10,000 10.0% 10.0% 1,100 16.1% 16.1% 11.00% 11.00% 161 161
2 10,000 10.0% 20.0% 989 14.5% 30.6% 9.89% 10.45% 145 153
3 10,000 10.0% 30.0% 900 13.2% 43.9% 9.00% 9.96% 132 146
4 10,000 10.0% 40.0% 876 12.9% 56.7% 8.76% 9.66% 129 142
5 10,000 10.0% 50.0% 749 11.0% 67.7% 7.49% 9.23% 110 135
6 10,000 10.0% 60.0% 604 8.9% 76.6% 6.04% 8.70% 89 128
7 10,000 10.0% 70.0% 523 7.7% 84.2% 5.23% 8.20% 77 120
8 10,000 10.0% 80.0% 468 6.9% 91.1% 4.68% 7.76% 69 114
9 10,000 10.0% 90.0% 369 5.4% 96.5% 3.69% 7.31% 54 107
Least Likely 10 10,000 10.0% 100.0% 238 3.5% 100.0% 2.38% 6.82% 35 100
100,000 100.0% 6,816 100.0% 6.82%
Gains Table Based on Development Sample
Overall Results

15.  So you can stand on the shoulders of history to
look into the future…
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
%OfResponders
% Of Universe
Model Performance Summary Chart
Random Selection Model Selection
57% OF THE RESULTS
FROM ONLY 40% OF
THE CALLS

17.  Did you know…
 This client only used traditional RFM strategies, we
showed them a way to better target…
Estimated Household Income
$1 - $39,999
$40,000 - $74,999
$75,000 - 124,999
$125,000 - $249,999
$250,000+

18.  Did you know…
 By enhancing their data with estimated income to
vary appeals the results were conclusive…
Control GRC
$/CC CC RR% Avg. Gift
$3.08 3,073 8.14% $37.47
Dynamic GRC
$/CC CC RR% Avg. Gift
$3.92 2,549 9.42% $41.66

19.  Did you know…
 A major client believed all of their donors from
multiple channels were focused in the South East…
IFCJ TV SUPPORTERS
DMA Code Mapping
Less than Universe Population
Greater than Universe Population
TV Supporters

20.  Did you know…
 We showed them that TM reaches new markets…
IFCJ TELEPHONE SUPPORTERS
DMA Code Mapping
Less than Universe Population
Greater than Universe Population
TM Supporters

21.  Combined with our consulting services we can
tie it all together…
ROI
APPLICATION
ANALYTICS
INFORMATION

22. MARKETING
INTELLIGENCE