Gregory D. Email & Phone Number

San Antonio, Texas, United States

Credit card remediation analytics with SAS.

San Antonio, Texas Metropolitan Area

Credit card remediation analytics

San Antonio, Texas Area

Credit card remediation analytics

San Antonio, Texas

Applied statistical/quantitative analytical techniques using both SAS and SQL to draw conclusions and make recommendations tailored to answer business questions focused on credit product losses and portfolio risks. Used discrete hazard logistic regression to estimate probability of default and exposure at default (PD /EAD) for individual credit segments within product groups; used Maturation-Exogenous-Vintage factors as inputs. Used SAS SQL to create single-use, multi-million customer panel data sets covering periods between 5 and 15 years to use for modeling. Data sources were widely spread throughout the organization which involved post processing using multiple SQL joins and SAS data-step merges. Post processing included creating new variables to reduce dimensionality and predictor transformations such as logs, lags, binning and weights of evidence categorizations.Analyzed early program development of a high rewards credit card product. Difficulties were presented given we had no other similar product from which to extrapolate results. The terms of offer required specific customer requirements that must be maintained to continue to receive benefits. Whether these requirements were reasonable and sufficient was an integral part of P&L of the product.Created decision tree/CHAID with SAS Enterprise Miner to identify critical variables to be included in modeling 84-month auto loan credit losses. Forecast losses for this credit product versus 60 and 72-month terms. Made actionable recommendations regarding product pricing and risk mitigation. Validated stress testing assumptions, testing historical results, appending variances and updating interpretation of results. Regularly translated analytical results into meaningful solutions to enhance decision making. Produced analytical materials for discussions with cross-functional teams to understand complex business objectives and influence solution strategies.

Round Rock, Texas

Created RAD forecast models for every segment of business in Canada; forecasts were aligned with financial targets and used to create sales quotas. Made presentations to senior leadership regarding most likely financial outcomes each quarter. Canada segments consistently met forecast targets and lead Dell North American performance versus all other segments including the U.S.Manipulated large customer data sets using SQL and BI tools to identify new opportunities including market sizing, market share, share of wallet, customer profiling/segmentation, cross-sell and to measure business progress versus forecast. Integrated BI analytics with IDC forecasts.Ranked customers viewed as priority targets by campaign and in-line with leadership RAD penetration goals. Used Salesforce.com data to highlight which customers had open opportunities on key product lines and which promising customers were lacking for attention. (E.g. who bought hardware but not storage or software? Which customers had not been contacted recently but had bought key product lines in the past? What was their cycle for repurchase?)

Round Rock, Texas

Sole responsibility for the Integrated Demand (IDM) Statistical (Autoregressive) forecasting models for SMB online and offline segments in Canada that included a complete financial forecast (RUM); built presentation views that included quarterly financial risk versus given financial targets. Presented outlook to senior leadership quarterly. Developed detailed forecasts of Online Demand Generation traffic and conversion goals for email, banners, affiliates, product listings and search. Created demand plans that broke quarterly totals into weekly and daily demand gen targets; created daily and weekly online dashboards to monitor actual online traffic and RUM production versus goal. Forecast call center volumes and conversion (close-rate) that aligned with financial forecast.Created indirect attribution “black box” statistical model to assign unattributed traffic to most likely source. Translated Media Mix Model results into concrete recommendations the online team could use in their planning to maximize marcom resources. Completed ad hoc “deep dive” analysis of demand gen vehicle issues and trends that needed highlighting and attention. Used multiple data sources such as Omniture & Site Catalyst to investigate impressions, open-rate, clicks, click-through-rate, cost-per-click, impression share, revenue per click, and average position. Aligned digital analytics with traditional data sources for unique insights.

Pulled RAW data from Enterprise Data Warehouse with SQL then analyzed with SAS Enterprise Miner to produce insights for sales and marketing that isolated developing trends in customer activity by customer groupings and product types.Analyzed customer behavior for insights into which items customers bought together and which items they would most likely buy next and at what interval and by which channel they should be targeted (Market Basket Analysis). Used SAS, Excel and SQL to parse and analyze data. Used base SAS to produce the monthly customer scorecard showing customer counts by stage of CRM lifecycle development and churn rate. Used Logistic Regression to score customer’s anticipated value. Completed adhoc cluster analysis of customer data to align sales resources with targets. Analyzed customer response data by campaign type (Direct Mail, Catalog, and TV) using Epsilon data pulled using SQL and isolated performance using unique media attributes such as mailer style, catalog cover and price points.

Elgin, Il

Pulled data from and Enterprise Data Warehouse using Oracle SQL, Analyzed data on customer responses to (DM, TV and E-mail) and purchases of life insurance to inform both sales and marketing on which campaigns were most successful and which targets were most likely to buy.Profiled existing customers by demographic and lifestyle to inform the purchase of customer names from data merchants. Modeled response using SAS logistic regression to determine key attributes of most attractive customer targets. Analyzed call center data to determine the best policy of lead distribution and development. Developed score cards to emphasize sales and marketing performance versus goal.

Master Of Business Administration - Mba, Marketing, Statistics & Finance

Bachelor'S Of Arts In Extension Studies, Cum Laude, Economics

Activities and Societies: Special Student, Harvard College