2. Proximo Consulting Services is an information
technology services firm
We specialize in helping our clients do more
with less
Our products and services are used by
companies in many verticals to maintain
information that is necessary to run their
organizations
Be it gathering, accessing, communicating, or
supporting your information and the systems
it runs on, we can help
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3. Founded in 1997
Our firm has a staff of about 20 people
◦ 90% of staff involved in implementation
Historical Growth Rate of 10%
Headquarters: New York City
Offices in Atlanta, DC, and LA
Certified LGBT-owned business through
National Gay & Lesbian Chamber of
Commerce
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4. Consumer Focused Health
L’Oreal Catalyst Health Solutions
Volvo Center for Disease Control
New Balance American College of
Cardiology
Financial Times
Finance Non-Profit / Association
Moody’s Investor Services Food Bank for New York City
First Data International Copper
Citibank Association
Deutsche Bank Personal Care Products Council
Travel & Hospitality Other
Ernst & Young
Wyndham Worldwide
Oracle
Intercontinental Hotel Group
Stora Enso
Shell Vacations Club
Ingersoll Rand
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5. Our offering is both service and software
We use data mining and modeling techniques
and tools from regression to decisions trees
and neural networks
Proprietary data mining and modeling
software called GMAXTM, which is powered by
a machine learning technique called “genetic
programming”
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6. Automatically mixing, matching and testing
new combinations of data variables
GMAXTM automates this laborious and often
manual and subjective tasks of data testing,
variable selection, creating new variable
combinations and model algorithms
Exponential "lift" (predictability) and insight
can be gained from the best competitive
advantage you have - your own data
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9. We enable you to see the strength of individual
variables as well as powerful new combinations
that help you better understand drivers. “Lift” is a
measure of predictability.
Variable Lift
Commissions earned 375
High face amounts on policies 352
Mix of business sold 240
Sales to first time customers 205
Ratio of policies issued to price quotes 200
Rate of underwriting approval 190
Weeks since last activity 188
Multiple product sales to same client 170
High retention rate for policies 167
Policies denied in underwriting process 153
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10. Mem ID Donor PRB PERCENTILE INDEX Mem (Member) ID
1684171001 0.329025 0.489929 143 Internal ID from source database.
1665858008 0.306879 13.549265 134
478465002 0.294251 22.906914 128 Donor PRB(ability)
30611002 0.283676 31.268373 124 This is the “Score” that comes out of our model.
1676612008 0.27209 43.249863 119 There is one score for each member ID. The score
1659517008 6
0.259039 51.453457 113 will be between zero and one. The higher the value
1664659001 0.253872 55.367447 111 of this number, the better the lead is in terms of
1659797008 0.246489 58.524769 107 likelihood to donate.
70549001 0.244217 60.087101 106
151719002 0.238843 61.937943 104
Percentile
This is the percentile ranking within the supplied
1660616001 0.22861 67.283615 100
file (lead list) for that particular member ID
1674806001 0.212031 74.676102 92
number. The percentile starts at zero (best) and
1669600001 0.210859 75.87915 92 goes to one hundred (worst). E.g. The records that
1571245001 0.206774 78.034843 90 have values between zero (0.00) and ten (10.0)
1601946001 0.205258 79.559067 89 comprise the top ten percent of the records in the
1679754001 0.204013 81.1595 89 file that was scored, in terms of their likelihood to
914045001 0.198669 85.557976 87 attend the conference.
1682935008 0.194388 93.075668 85
146766003 0.194388 93.075668 85 Index
1679487003 0.194037 93.837784 85 This value compares the Donor PRB value of the
1683303001 0.193554 94.485573 84 member ID against the average Donor PRB value for
1663675001 0.193312 94.681549 84
the file. An “average” member from the file has an
index value of 100. (E.g. A record or member with
an index value of 143 would be considered to be
43% more likely to donate than the average record
Proximo Consulting Services, Inc. on the file).
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11. Split Validation Modeling
◦ Model on half the database
◦ Test the model on the other half
◦ Compare second half to actual history
◦ If those with high scores in the second half actually
donated, then the model is validated
Field Testing
◦ Focus outreach on top one to three deciles
◦ Compare to some outreach on lower-deciles
◦ Compare to history, including cost of outreach per
dollar received
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12. For more information contact:
David Ricciardi
Proximo Consulting Services, Inc.
dricciardi@proximo.com
800-236-9250 ext 318
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