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Mayank Aggarwal
Company: Gilead Sciences
Mayank Aggarwal used pivot tables and data visualization tools to examine the relationship between increases in out-of-pocket cost to patients and subsequent abandonment of one of the company’s top branded drug. Mayank went on to quantify the value lost due to the increased abandonment rate and identify the Medicare payers responsible for the most lost business in order to take appropriate corrective action.
Mayank helped senior sales management address important concerns around rising cost to patients leading to drug abandonment. Drug abandonment was not only a source of lost revenue but also a cost to the entire healthcare system whenever abandonment led to adverse events resulting in more ER costs. Mayank studied patient claims data (more than 500,000 rows in data sheet) to investigate the hypothesis that increased patient’s out of pocket cost was linked to drug abandonment. He was further able to identify increased abandonment of the drug among specific payer segments using pivot tables. With a capability to summarize business lost by payer segment, Mayank was able to establish the business case for negotiating new contracts. The analysis was very helpful to the company and will be used to adjust the payer contracts and lower the patient’s cost burden, hence reducing drug abandonment.
Rathi Bala
Company: M&T Bank
During her internship, Rathi Bala completed a first-ever analysis of the performance of the bank’s financial investigations unit. After some arduous data cleansing and report generation using pivot tables, she uncovered enough valuable insights that she ended up presenting them directly to the bank’s CRO (Chief Risk Officer). Her recommendations are projected to reduce cycle time by 36% and eliminate the existing backlog of work within two months.
This project was part of a bank-wide risk mitigation effort. Rathi used pivots in order to develop metrics to assess the current state of the department in terms of productivity. In particular, she uncovered data that proved there was significant variability in terms of performance across individuals. The top performers in the department were completing double the amount of work as the bottom performers while still maintaining quality. This was a very surprising insight and illustrated the amount of opportunity available for improvement.
As mentioned, recommendations included solutions that reduced current cycle time by 36%, removing the current state of backlog within 2 months and ultimately eliminating the need for additional FTE hires.
Greg Bicksler
Company: AT Kearney
Greg Bicksler built a 22,000 decision-variable linear-programing model to recommend how a client could best re-assign 10,000+ stores to 20+ distribution centers located across the US given the client’s recent acquisition of a competitor. The re-assignment substantially reduces shipment miles resulting in significant (but confidential) dollar savings.
Morgan Breck
Company: American Express
Morgan Breck used her DAO skills to match millions of transaction records to Corporate Platinum Card Members in order to examine the relationship between card benefit usage and retention. The statistically significant difference she found in benefit usage between retained and downgraded members quantitatively justified (for the first time) her group’s continuing investment in benefits.
Zhiyuan (Jerry) Wang
Company: Compare.Com
Jerry Wang applied pivot tables to a big database in which each record represented the purchase of an auto insurance policy using compare.com (the Expedia of auto insurance). His purpose was to help the firm understand how and why revenue-per-sale varied across states, suppliers, and the ages of the purchasers. His presentation at summer’s end to the CEO and heads of departments will help evaluate the value of different types of customers and shape company strategy.