DM STAT-1 Consulting - STATISTICAL MODELING & ANALYSIS for DATABASE MARKETING: EFFECTIVE DATA MINING TECHNIQUES

DM STAT-1 Consulting's Founder and President Bruce Ratner, Ph.D. has made the company the ensample for Statistical Modeling & Analysis, and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in the full range of standard statistical techniques, and methods using hybrid machine learning-statistics algorithms, such as its patented GenIQ Model, to achieve its Clients' Goals - across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecom., Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, and Risk Management, and Nonprofit Fundraising. Bruce’s par excellence consulting expertise is apparent from his a best-selling book Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data. Bruce assures his clients' marketing decision problems are solved with the optimal problem-solution methodology; rapid start-up and timely delivery of projects results; and, the client’s projects are executed with the highest level of statistical practice. He is an often-invited speaker at public industry events and for private seminars as per a company's formal request. Bruce is the predominate co-author of the popular textbook The New Direct Marketing. Also, he is on the editorial board of The Journal of Database Marketing. Bruce is active in the data mining/direct marketing community as the instructor of the advanced statistics course Modern Methods of Data Analysis and Response Modeling, sponsored by the Direct Marketing Association, and as a frequent speaker at industry conferences. Moreover, Bruce has a micro-website GenIQ.net for his machine-learning data mining software. He has written over a hundred peer-reviewed articles on his content domain. 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 Demographic- based 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; Optimize Customer Loyalty; CRM for Cross-Sell and Up-Sell to Improve Response Rates and Increase Revenue; CRM Segmentation for Targeted Marketing; Workforce Optimization; 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; Subprime Lender Short Term Loan Models for Credit Default and Exposure; Retail Revenue Optimization: Accounting for Profit-eating Markdowns; Nonprofit Modeling: Remaining Competitive and Successful; Detecting Fraudulent Insurance Claims; Demand Forecasting for Retail; Cross-Sell and Up-Sell to Improve Response Rates and Increase Revenue; Credit Scoring for Controlling Risk; 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, Machine-Learning Conjoint Analysis, and all other models in the data analyst's tool kit for problem-solution approaches. Model Types: Acquisition/Prospect Models, Retention Models, Attrition Models, LifetimeValue Models, Credit Risk Models, Response-Approval Models, Contact-Conversion Models, Contact-Profit 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, Trigger Marketing Model, Fraud Detection: Beyond the Rules-Based Approach, Workforce Optimization Model, 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; Procedures for Data that Are Too Large to be Handled in the Memory of Your Computer; Detecting 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; A Method for Moderating Outliers, Instead of Discarding Them; Extracting Nonlinear Dependencies: An Easy, Automatic Method; The GenIQ Model: A Method that Lets the Data Specify the Model; Data Mining Using Genetic Programming; Quantile Regression: Model-free Approach; Missing Value Analysis: A Machine-learning Approach; Gain of a Predictive Information Advantage: Data Mining via Evolution; and many more analytical strategy-related analytical tactics.		(click block) Webcast for GenIQ Model (Click book image.) (NEW BOOK!) (click block)

DM STAT-1 Consulting is the leading firm for analysis and modeling in the DM Space (direct/database marketing (DDBM)), customer relationship management (CRM), and knowledge discovery/data mining (KDD). DM STAT-1 specializes in the full range of standard statistical techniques, statistics and machine learning hybrid methods, and cutting-edge data mining tools to successfully achieve their clients' DM goals. DM STAT-1's par excellence data mining expertise is highlighted by its GenIQ Model, a 3-in-1 tool for variable selection, data mining and model building. DM STAT-1 assures: the client's marketing decision problems will be solved with the optimal problem-solution data mining methodology; rapid start-up and timely delivery of projects results; the client's projects will be executed with the highest level of statistical data mining practice. Typical projects worked on include Data Mining & Knowledge Discovery; Data Mining with GenIQ Model; Data Mining for Direct Marketing; Data Mining for Database Marketing; Data Mining for B-to-B; Data Mining for CRM; Data Mining Regression; Data Mining Classification; Data Mining Tree Model; Data Mining Predictions; Data Mining Segmentation; Text Data Mining; Data Mining Analysis; Data Mining Optimization; Data Mining Modeling; Data Mining Business Rules; and Data Mining Strategy.