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DM STAT-1 CONSULTING
574 Flanders Drive North Woodmere, NY 11581
516.791.3544 fax 516.791.5075 1 800 DM STAT -1
BRUCE RATNER, PhD
[email protected]
www.dmstat1.com
Bruce Ratner, Ph.D.: Curriculum Vitae
Bruce Ratner, Ph.D., Founder and President of DM STAT-1 Consulting, has made the company the ensample for
Statistical Modeling & Analysis and Data Mining in Direct & Database Marketing, Customer Relationship
Management, Business Intelligence, and Information Technology. DM STAT-1 specializes in the full range of
standard statistical techniques, and methods using hybrid statistics-machine learning algorithms, such as its patented
GenIQ Data Mining, Modeling & Analysis Software, to achieve its Clients' Goals - across industries of Banking,
Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass &
Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, and Risk
Management. Bruce’s par excellence consulting expertise is clearly apparent as he wrote the best-selling book on
Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big
Data. (based on Amazon Sales Rank, and assures: the client’s marketing decision problems will be solved with the
optimal problem-solution methodology; rapid start-up and timely delivery of projects results; and, the client’s
projects will be executed with the highest level of statistical practice. He is often-invited speaker at public and
private industry events.
Clients' Goals include:

Results-Oriented: Increase Response Rates; Drive Costs Down and Revenue Up; Increase Customer
Retention; Stem Attrition; Check Churn; Increase Customer Affinity - Match Products with
Customer Needs; Enhance Collections & Recovery Efforts; Improve Risk Management; Strengthen Fraud
Detection Systems; Increase Number of Loans without Increasing Risk; Work Up Demographicbased Market Segmentation for Effective Product Positioning; Perform Retail Customer Segmentation for
New Marketing Strategies; Construct New Business Acquisition Segmentation to Increase Customer Base;
Identify Best Customers: Descriptive, Predictive and Look-Alike Profiling to Harvest Customer
Database; Increase Value
of
Customer
Retention; Generate
Business-to-Business Leads
for
Increase Profitability; Target Sales Efforts to Improve Loyalty Among the Most Profitable Customers;
Improve Customer Service by Giving Marketing and Sales Better Information; Build CRM Models for
Identifying High-value Responders; Build CRM Models to Run Effective Marketing Campaigns; Improve
Human Resource Management - Retain the Best Employees; Optimize Price and Package Offerings; Right
Offer at the Right Time with the Right Channel; Maintain Product Profitability and Support Effective
Product Management; Increase the Yield of Nonprofit Fundraising Campaigns; Personalize
Recommendations for Information, Products or Services; Credit Scoring to Control Risk; Retain Best
Customers and Maximize Their Profits; Nonprofit Modeling: Remaining Competitive and Successful;; and
so on.

Analytical Strategy: Build, Score and Validate Logistic Regression Models, Ordinary Regression Models,
Variant Regression-based Models, Decision-Tree Models, Machine-Learning Models, Quasi-Experimental
Design Models, Marketing Mix Optimization Models; Latent Class Models, Survival/Proportional Hazards
Models, and Structural Equation Models; and all other models in the data analyst's tool kit for problemsolution approaches.
o
Model Types: Acquisition/Prospect Models, Retention Models, Attrition Models, Lifetime Value
Models, Credit Risk Models, Response-Approval Models, Contact-Conversion Models, ContactProfit Models, Customer-Value Based Segmentation Models; Credit Scoring Models, Web-traffic
Models, Balanced Scorecard Models, Cross-sell/Up-sell Models, Zipcode-based Models,
Blockgroup-based Models, Decision-Tree Inventory Forecast Models, Models for Maximizing
Profits from Solicitations, Mortgage and Credit Card Default Models, Collaborative Filtering
Systems, and an assortment of results-related analytical strategies.

Analytical Tactics: Procedure When Statistical Model Performance is Poor; Procedures for Data that are
Too Large to be Handled in the Memory of Your Computer; Detect Whether the Training and Hold-out
Subsamples Represent the Same Universe to Insure that the Validation of a Model is Unbiased Data
Preparation for Determining Sample Size; Data Preparation for Big Data; The Revised 80/20 Rule for Data
Preparation; Implement Data Cleaning Methods; Guide Proper Use of the Correlation
Coefficient; Understand Importance of the Regression Coefficient; Effect Handling of Missing Data,
and Data Transformations; High Performance Computing for Discovering Interesting and Previously
Unknown Information in - credit bureau, demographic, census, public record, and behavioral
databases; Deliverance of Incomplete and Discarded Cases; Make Use of Otherwise Discarded Data;
Determine Important Predictors; Determine How Large a Sample is Required; Automatic Coding of
Dummy Variables; Invoke Sample Balancing; Establish Visualization Displays; Uncover and
Include Linear Trends and Seasonality Components in Predictive Models; Modeling a Distribution with a
Mass at Zero; Upgrading Heritable Information; "Smart" Decile Analysis for Identifying Extreme Response
Segments; and many different kinds of analytical strategy-related analytical tactics.
Dr. Ratner's latest book Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining
Big Data is an Amazon’s Sales Rank best seller within its content domain since its publication (June 2003). Bruce is
also the co-author of The New Direct Marketing, a text widely studied by database marketing practitioners, and the
author of the DM STAT-1 Newsletter, the only online information exchange about quantitative methods in the DM
Space. He has a doctorate in statistics, and a self-interest in developing hybrid statistics-machine learning
methodologies. He is active in the direct marketing industry, both as a frequent lecturer at Direct Marketing
Association (DMA) conferences and as an instructor of the Advanced Statistics course sponsored by the DMA. He is
an often invited speaker at private organizations to present his cutting-edge solutions to common business problems
for superior results. Bruce is on the editorial boards of The Journal of Database Marketing and Decision Sciences
Journal.
Bruce’s Clients include:
Citibank, N.A.; American Express TRS/Corporate Services; Book of the Month Club; Ameritech; General Foods;
Wall Street Journal; Chase Manhattan; T. Rowe Price; BMG Music Clubs; AT&T; Convergys; Newsweek, Inc.;
Chemical Bank; Scudder Stevens & Clark; Columbia House; Bell Atlantic; Memorial Sloan Kettering; Editions
Atlas/Newfield Publication; First Union; H&R Block; Collier Encyclopedia; Comcast Communications; Mobile;
Prentice Hall; GE Capital; ABIG; Cutco Cutlery; LA Cellular; Polaroid; Xerox Education Publication; MBNA
America; Allstate Life Insurance; Double Day Book Clubs; MCI; P&G; American Lawyer Magazine; Monogram
Bank; CUNA Insurance; Grolier Book Club; NYNEX; Reader's Digest; Wells Fargo Bank; State Farm Insurance;
JCPenney Catalog; NJ Bell; Saks; Providian Bank; JCPenney Life Insurance; Service Merchandise Co.; SNET; VW;
First USA; Discover Financial Services; Macy's by Mail; AT&T Wireless; and Campbell.
Patent
GenIQ patent in 1999 for unique application in solving the two-group classification problem with genetic
programming.
Publications
1. Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, Chapman
and Hall/CRC Press, May 2003.
2. The New Direct Marketing: How to Implement A Profit-Driven Database Marketing Strategy, McGraw Hill,
1998.
3-101 plus. Bruce has written over s-five peer-reviewed articles, which are on his website www.dmstat1.com.
Bruce Ratner, Ph.D.