Proximo Analytics Presentation April 2011

by Proximo Consulting Services

April, 2010

http://www.proximo.com/index.jsp

Proximo Consulting Services, Inc.ConfidentialCopyright 2011

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


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


L’Oreal Volvo New Balance Financial Times

Food Bank for New York City International Copper

Association Personal Care Products Council

Consumer Focused

Non-Profit / Association

Health

Other

Finance

Travel & Hospitality

Wyndham Worldwide Intercontinental Hotel Group Shell Vacations Club

Moody’s Investor Services First Data Citibank Deutsche Bank

Ernst & Young Oracle Stora Enso Ingersoll Rand

Catalyst Health Solutions Center for Disease Control American College of

Cardiology


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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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 insightcan be gained from the best competitive advantage you have - your own data

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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.

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Variable LiftCommissions earned 375High face amounts on policies 352

Mix of business sold 240Sales to first time customers 205Ratio of policies issued to price quotes 200

Rate of underwriting approval 190Weeks since last activity 188Multiple product sales to same client 170

High retention rate for policies 167Policies denied in underwriting process 153


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Mem (Member) IDInternal ID from source database.

Donor PRB(ability) This is the “Score” that comes out of our model. There is one score for each member ID. The score will be between zero and one. The higher the value of this number, the better the lead is in terms of likelihood to donate.

PercentileThis is the percentile ranking within the supplied file (lead list) for that particular member ID number. The percentile starts at zero (best) and goes to one hundred (worst). E.g. The records that have values between zero (0.00) and ten (10.0) comprise the top ten percent of the records in the file that was scored, in terms of their likelihood to attend the conference.

IndexThis value compares the Donor PRB value of the member ID against the average Donor PRB value for 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 on the file).

Mem ID Donor PRB PERCENTILE INDEX

1684171001 0.329025 0.489929 143

1665858008 0.306879 13.549265 134

478465002 0.294251 22.906914 128

30611002 0.283676 31.268373 124

1676612008 0.272096

43.249863 119

1659517008 0.259039 51.453457 113

1664659001 0.253872 55.367447 111

1659797008 0.246489 58.524769 107

70549001 0.244217 60.087101 106

151719002 0.238843 61.937943 104

1660616001 0.22861 67.283615 100

1674806001 0.212031 74.676102 92

1669600001 0.210859 75.87915 92

1571245001 0.206774 78.034843 90

1601946001 0.205258 79.559067 89

1679754001 0.204013 81.1595 89

914045001 0.198669 85.557976 87

1682935008 0.194388 93.075668 85

146766003 0.194388 93.075668 85

1679487003 0.194037 93.837784 85

1683303001 0.193554 94.485573 84

1663675001 0.193312 94.681549 84


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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For more information contact:

David Ricciardi

Proximo Consulting Services, Inc.

[email protected]

800-236-9250 ext 318

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mailto:[email protected]

Technology

Proximo Analytics Presentation April 2011