Recent engagements:

• Developed numerous dashboards using Oracle’s Hyperion Interactive Reporting (IR/OBIEE) for a top automotive finance company. Users understood lending activity better and were able to effectively take action to save losses and increase revenue.
• Created predictive models for a top Wireless Communications company to reduce churn and cross-sell products. The benefit was better insight of their customers leading to improved direct marketing efforts.
• Analyzed a database of marketing campaign data for a pharmaceutical company. The objective was to document and propose changes to improve the quality, access, and intuition of the data. The findings enabled more efficient and effective campaign analysis and campaign operations which improved profits.
• Enhancing a Microsoft Access database used by an engineering company to track their projects. The improvement will enable them to clearly understand each project’s profitability and improve operations.
  

Consulting offerings and examples:

• Reporting: Which customer segments exceeded your budgeted charge-off rate?
• Dashboards: Which campaigns had the best take rates and ROI?  Let’s dive into the data to understand why.
• Analysis: What is a sound analytical approach to understand which customers are delinquent and what the impact is?  Let’s set an analytical process that is effective, improves over time, and can easily be passed to new hires.
• Data Mining: Which customers are likely to leave us (churn)?  Let’s use this to spend our marketing dollars wisely.
• Campaign Analysis: How did the treatment group compare to the control group?  Which segments were the best?
• Campaign Testing: Properly set an experiment to test different aspects of a marketing campaign.  Which attributes have the biggest impact?
• Learning Loop: Set a process to improve by learning from your efforts.  This includes documenting the campaign, setting success criteria, measuring results, and communicating insight to improve results.
• Targeting: Which customers want product X or even better which products are best for our “high potential” customer segment?
• Data Quality:  Can we trust the data under our reports and analyses?   Do we spend too much time questioning results and cleaning data?
• Data access and gathering: Let’s develop and automate a process utilizing SAS, SQL, and excel to merge data from different sources for analysis.