“Half th e money asted.
t ising is w
on adver on’t know
I spend
Trou ble is, I d f.”
w hich hal
r
John W anamake tising
de rn Adver
Father of Mo

How do you
KNOW the QUALITY
tioners:
of your ONLINE LEADS?
A tten et Mark
Perfor mance
u’s eS cores
With eBurea
You Know
Now

eBureau’s eScore Predicts Which
Online Leads Will Convert
100%
90%
80%
Percentage of Customers
70% eScore Model
60%
50%
40%
96% of customers
30%
coming from 50% of
20%
10% Random
your leads
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
eScore Model Random
Percentage of Leads

How do you
KN OW the
QUAL ITY of
your
ADS?
ONL INE LE

eBureau KNOWS
the SCORE on
ONLINE LEADS.
The Quality Score
For Online Leads

110%
Cumulative % of Applicants
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110%
Cumulative % of Leads
Lead Source Lead Source
Lead Source Lead Source
Lead Source Lead Source
Lead Source Lead Source

110%
Cumulative % of Applicants
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 110%
Cumulative % of Leads
ies Best
eScor e Identif Lead Source Lead Source
r st Leads
and Wo
Lead Source Lead Source
Lead Source Lead Source
Within Lead Source Lead Source
Every Source

eScore Pred
icts
Which
MORTGAG
E
LEADS
Will Resul
t in
Booked Loa
ns

ts Which
eScor e Predic
LEADS
EDU CATION
, Become
Wi ll Apply
a nd Stay
St udents,
Enrolled
Will this Lead Enroll?
Will this lead continue with
courses beyond Q2?
Does this lead have a higher
potential of graduating ?

eScore Predicts
Payment Rates
for
Mu sic Clubs BILL-ME
Online Orders
Book Clubs

r to
Makin g the Right Offe
r
th e Right Custome
Consumer
Marketer

r to
Makin g the Right Offe
r
th e Right Custome 1
Consumer
John Smith enters his
name and address into
online registration form
Safe to Offer Bill-Me
Payment Terms
Marketer 4
With a clearer picture of John
Smith, the marketer safely offers a
“bill me later” option, helping:
•Increase conversion rates
Verified & Low •Increase transaction amount
Payment Risk
2
Gender: Male
Married: Yes
3
Age: 39 eScore: 850
Household Income: $45,321 Low Payment
Presence of Children: Y Risk
r sends
Markete ureau Political Affiliation: Liberal
B
data to e Homeowner: Yes
Length of Residence: 7 years
Home Value: $180,000 eBureau verifies contact data,
Education Level: 16 highlights demographic profile and
Occupation: Salesman assesses payment risk with fraud score

Buying the Right
Leads at the Right
Price
Lead Scoring In Real-Time

Buying the Right
Leads at the Right
Price
1 CPL Advertiser
(Lead Buyer)
Lead Seller Offers Leads for Sale
4 CPL Advertiser
Rejects Low
Scoring Leads,
2
Rejected Pay $20
Pays the Right
Pay $50 Price for
Remaining Leads
Advertiser
Sends Leads
Reject
to eBureau
Score: 47
Zero Pay $20
Lead Scoring In Real-Time Chance of Score: 244
Converting Pay $50 Low
Score: Chance to
1 Buy/no-buy decision 578
Likely to
Convert
Convert
2 Lead buyers send leads to eBureau for
verification and scoring 3
3 eBureau returns the scored leads back
to buyer
eBureau verifies and
scores the lead -
Score: 578 measuring
4 Lead buyer rejects some leads, offers
right price for others
Likely to
Convert
probability of sales
conversion

e
zing th
Optimi -Sales
Lead-to sion
Conver
Marketer

e
zing th
Optimi -Sales
Sales Conversion Center
Lead-to sion
C:
A: Most B: High Medium D: Low E: eMail
Urgent Priority Priority Priority Only
Conver
Marketer
Best Sales Call Center
Incubate
Rep Score:
Score: <100
Marketer’s Score: 300+ 100-300
Little Chance
Online Likely to Low Chance of
of Converting
Registration Convert Converting
Form Completed
3
1 Marketer intelligently
routes leads to
appropriate channel
Marketer sends
leads to
eBureau
Score: 578
Likely to
convert
2 2
eBureau verifies contact data, corrects
5555 any mis-keys and calculates conversion
probability for each lead

Direct Marketing
Purchase &
Payment
Multiple History
Identity
Databases
Additional
Sources
Aggregated
Demographic Credit Data
&
Asset Data

How Does
eBureau Do it?
Vast Consumer Data
Direct Marketing
Purchase & • Identity Verification Data
Payment
Multiple
Identity
Databases
History
• Demographic and
Property Data
Additional
Aggregated
Sources
• Purchase and Payment
Credit Data
Demographic
& History Data
Asset Data
200 Terabytes of Data
300 Billion Consumer Records

How it Works

How it Works
1
Marketers’ historical
Predictive Model segments 3
good leads from bad
leads performance
Advertiser data
Lead
Performance
Data
2 eScore model put into
4
eBureau’s 300 Billion Records
production as real-time
of Consumer Overlay Data
web service
eBureau
Data

eBureau’s Data Center

e Scoring
SPEE D. Real-tim
u tstanding
INTELL IGENCE. O
Predi ctive Power
Maximize
R ESULTS.
S UPERIOR R educe Costs
Convers ion Rates, I
rketing RO
Improve Ma
Patented
Over 50,000
Expert
predictive Modeling
Purchasing Behavior
a nalytics
Gender
Best Phone
Best Address
Variables
Phone numbers
Addresses
Bankruptcy information
Deceased identification
Marital Status Assets
Educational Level Relatives
Occupation Associates
Household Income Neighbors
Length of Residence
eBureau’s Data Center

ead Q uality
L
Dash board
• Monitor your eScores and
other data in real-time
• Forecast your lead
conversion rates
• Compare quality across
multiple sources
• Detect fraudulent
lead patterns

“The Price of Light is less
than the Cost of Darkness.”
- Arthur C. Nielsen

www.ebureau.com
320-534-5000