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Elder Research Capabilities

In late December, was acquired by Elder Research, Inc. Many of you have asked for more detail, so here’s an introduction to the folks at Elder Research and some stories of what they do.  There are 100+ employees at Elder Research, led by John Elder, the founder, and Gerhard Pilcher, the CEO. I met John through his interest in resampling, which I shared.  John founded Elder Research in 1995. With offices in Charlottesville VA, Baltimore MD, Raleigh NC, Washington DC, and London UK, they’ve solved hundreds of challenges for commercial and government clients by extracting actionable knowledge from all types of data. Dr. Elder has co-authored three books — on practical data mining, ensembles, and text mining — two of which won “book of the year” awards. He earned engineering degrees from Rice and University of Virginia, where he’s an Adjunct Professor, and was named by President Bush to serve 5 years on a panel to guide technology for national security. 

Gerhard’s work experience spans both private and government sectors, domestic and international.  He is a coauthor of “Mining Your Own Business,” popular among executives and senior managers who want to shift their organizations towards better data driven decisions. The book is both a basic introduction to the subject of data science and a useful reference for specific, daily guidance.  Gerhard currently serves on the Institute for Advanced Analytics Advisory Board and George Washington University’s Advisory Board for the Business Analytics graduate program. He was named to the NC State Computer Science Department’s “Hall of Fame” in its inaugural year. Most of the employees at Elder Research are involved in predictive analytics, data science and data engineering.  Their watchword is “return on investment,” generated by smart analytics and smart deployment.

ROI on Data Science Projects

With the right business problem, data science methods can deliver exceptional return on investment (ROI) by assessing and managing risk, detecting and preventing fraud, optimizing workflow and business processes, and prioritizing resources.  They can help determine which customers to contact for marketing, which tax returns to audit, which debtors to approve for increased credit limits, which patients to clinically screen, which customers are likely to leave, which persons of interest to investigate, and which equipment to inspect for impending failure.

Interestingly, there is often a mismatch between what companies think they should do with analytics versus what actually provides the most value. There is no “easy button” for achieving value through analytics and it is difficult to determine specific ROI at the onset of an analytics project. However, it is often possible to realize returns on analytics investment in the range of 10x to 100x or higher. 

Here are just a few of the projects the Elder Research teams have been involved in:

1.) Were Those MRI’s For Real?

Issue: It is a costly and time-consuming process to investigate medical billing fraud, since there are relatively few bad actors. Elder Research’s client estimated they had a global rate of provider fraud of 5 percent, and wanted to focus investigations on the providers most likely to be fraudulent or non-compliant. Elder developed a predictive model that scored and ranked cases by risk to generate leads with the biggest potential for payoff.

Results:  The model increased the fraud detection rate from 5% to 48% for the top 50 riskiest providers identified by the model, dramatically increasing the efficiency of their investigative resources.

2.) Improving Credit Risk Scoring in Retail Banking With Ensembles

Issue:  The client’s “tried and true” logistic regression model had been developed and deployed by dozens of expert statisticians over many years. The client wanted to know whether any new insights would be produced by using modern data mining techniques (they were doubtful). Elder Research applied expertise in machine learning, statistics, data mining, model testing strategies, and model ensembles to produce a more effective model. 

Results: The new model ensemble reduced the number of credit card accounts that defaulted on the client’s evaluation dataset by more than 10% when compared to their world-class baseline model.

3.) Prioritizing Building Lease Renewals for a Government Agency

Issue:  The Postal Service owned and leased more than 33,000 facilities with over 285 million interior square feet, but over recent years there had been a significant decline in workload. Were all 33,000 facilities still necessary for operations? Could the facilities inventory be optimized?  Elder Research built a predictive model to predict future interior needs, compare market values, and identify fraudulent lessors.

Results: The model focused auditors on leases with highest risk and potential savings. The projected savings over 5 years was just under $99 million.

4.) Automating Demand Forecasting in Logistics

Issue:  Logistics is a mature, technologically-advanced, and analytically-sophisticated industry. Still, even after decades of improvements coming from the Industrial Engineering and Operations Research fields, major efficiencies can still be realized by applying advanced analytics, data infrastructure, and computing power. All business processes in logistics rely on accurate demand forecasting in the short, medium, and long-term to inform resourcing, planning, and staffing to support future needs. In three weeks Elder Research delivered a functioning production time-series forecasting framework using R and Spark. After six months we had scaled to a refined framework that produces timely forecasts on over several thousand locations in the client’s network.

Results: Flexible/extensible production scale solution delivering 35 million four-weeks-out forecasts in under an hour with median accuracy of 88%.

5.) Improving Customer Retention in Telecommunications

Issue: Competition in the wireless telecommunications industry is intense. To maintain profitability, wireless carriers must control churn, or the loss of subscribers to other carriers. Churn had caused nTelos’s market share and profitability to decline dramatically. nTelos hired Elder Research to help identify the causes of its high churn, and reduce it. By applying advanced techniques for modeling and visualizing customer records, we created a combined data and text mining solution to increase marketing efficiency and reduce churn.

Results: The model improved targeted messages which decreased nTelos’s churn from 3.5 percent to 2.9 percent and boosted annual profits by an estimated $1+ million.

6.) Enhancing Customer Loyalty in Retail

Issue:  The loyalty department of an industry-leading computer technology provider wanted to increase total lifetime value of customers and strengthen the re-seller program. Elder Research collaborated with the client to create an automated evaluation of the loyalty program’s impact on customer lifetime value, retention, and market share. The solution used advanced analytics to segment customers, determine loyalty metrics, and design and deliver summary reports with actionable program insight. We designed and measured targeted marketing campaigns for the client’s most important customer base to focus marketing resources on campaigns that were the most effective.

Results: The enhanced loyalty rewards program contributed to a 35% increase in program enrollment contributing more than $600 million in increased sales over five years.

7.) Reducing Fraud at the U.S. Postal Service

Issue:  The US Postal Service Office of Inspector General (USPS-OIG) wanted a more efficient and actionable way to target questionable contracts and healthcare claims in order to eliminate fraud, waste, and abuse. Elder Research partnered with the USPS OIG to customize a solution to help the agency generate investigative leads based on risk indicators and anomaly detection. RADR, a custom tool combining sophisticated predictive analytics with an intuitive, user friendly interface was deployed to help auditors and investigators identify high-risk cases.

Results: Leads generated by the contract fraud model proved to be 74% actionable; 23 out of 31 highest scored contracts showed evidence of fraud, waste, and abuse. The healthcare model results aided in over $11 million in recoveries, restitutions, and cost avoidance in the first year, reduced the number of hours spent on a case by 30%, and increased the dollars returned per case by 35%.

Data Science is powerful and proven. As shown in the examples above, companies in vastly different fields are achieving huge gains by extracting useful information from their data. But data science is a success only if the results are used to improve decision-making. The path to success involves clear thinking, the right data science technology, trust, and a willingness and ability for the organization to change.   If your organization needs help in figuring out goals and objectives, in putting together the right team, in bringing data literacy to your organization, Elder Research can help with a strategy consultation .  Contact Carter Phillips today to schedule.