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Case Studies

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A leading retail company for consumer product goods (CPG) wanted to understand the effectiveness of their digital marketing efforts. Elder Research assessed the effectiveness of of paid product ads and determined the relative value of Google paid search keywords. We identified an under-performing ad characteristic that corresponded to a 55% decrease in click-through-rate (CTR). Our keyword pricing models uncovered overspending in 25% of their paid search budget and identified four of the top ten clicked keywords that were overpriced.
A government agency wanted to evaluate the efficacy of a potential policy change for handling of long term disability leave payments to injured federal employees. In some cases an injured worker’s physician fails to provide appropriate medical justification for the worker’s continued absence from work on disability leave. In these cases, adjudicators will require a second opinion (SECOP) from another physician regarding the worker’s level of capability to return to work. Elder Research was tasked to evaluate the effectiveness of these second opinions to encourage employees to go back to work.
Elder Research developed an automated data pipeline to cleanse data from millions of documents and feed a data visualization tool used to explore and identify risky network relationships. The solution enabled the client to automate significant portions of work saving time and cost, make data-driven decisions, prioritize investigative resources, and gain new business value from the data.
Elder Research implemented an automated framework for time-series forecasting at a major logistics company. Our system, combining R and Apache Spark, produces 35 million forecasts in under one hour, and selects the optimal time-series forecast algorithm in each of three forecasting windows. Forecast results from our framework were 88% accurate at a four-week horizon.
Elder Research designed and deployed tools for high-end text mining to search, index, and automatically classify information related to animal infectious diseases. The solution enabled analysts to validate incidents of animal disease and make recommendations for dealing with them at the earliest stage possible.
A Fortune 500 CPG retailer had no centralized analytics capability or long-term strategy. The executive team hired Elder Research to assess its current analytics needs, develop a strategic roadmap, and build an analytics center of excellence.
A global humanitarian organization asked Elder Research to develop a customized training and collaborative modeling framework to empower their team to develop a baseline predictive model to optimize donor segmentation strategies. The client's team developed a baseline multi-class classification model in R and was very satisfied with the quality of the training and insight provided.
A CPG client needed help integrating disparate data sources including syndicated sales, promotion, and coupon data to enable their marketing team to analyze the effectiveness and return on investment of historical marketing events.
Department of Labor Office of Inspector General (DOL-OIG) contracted with Elder Research to create a predictive model to detect fraud in Office of Workers’ Compensation Program (OWCP) data. The goals of this project were to highlight abnormalities in the claims data that could be used to form the basis of future audits and to create visualization tools to enable auditors to explore model results. The RADR tool amplifies the productivity of DOLOIG auditors and reduced case investigation time from days to hours, combining client goals and expertise with objective measures of risk
Elder Research developed an advanced analytics prototype to detect potential fraud, waste, and abuse by Medicare and Medicaid dental insurance service providers. The analytics solution increased fraud detection by a factor of ten and enabled the client to more efficiently target suspect claims for investigation.
The Analytics Center of Excellence at a national retail chain was developing a strategic plan for sustainable and pervasive use of data across their business. Their goal was to establish a competitive advantage through using their data, while remaining true to their corporate values. Elder Research developed a data strategy which identified over a dozen opportunities to use more advanced analytical techniques in their business.
After investing heavily to investigate a new potential drug, the compound was not passing the statistical tests required by the FDA. Pharmacia & Upjohn invited Elder Research to examine the data and determine the drug’s viability. The research discovered a real effect, and these decisive results were communicated to decision makers using a novel visualization technique.
A medical device manufacturer designed a sensor that is implanted within a major organ to detect disease events. The sensor is being tested on animal subjects with the Intent to move to human trials and secure FDA approval for monitoring patients with previous cases of the disease. Elder Research was engaged to provide machine learning models using the sensor data to identify abnormal activity that are predictive of the disease. The goal was to classify sensor traces as either normal or high risk.
The project goal was to use text mining and machine learning to extract economic sentiment indicators from millions of disparate documents. Elder Research built a weakly supervised text sentiment classifier for economic indicators using the latest Natural Language Processing tools. The models make valuable new sources of data available for the client to inform decision-making such as rapid portfolio rebalancing based on continuous market signaling.
Elder Research combined state-of-the-art text mining algorithms with traditional statistical techniques to create a solution for the Social Security Administration to rank disability claims for approval. The results were more accurate and more consistent than any single doctor’s decision and allowed 20% of the claims to be approved immediately.
Elder Research examined a dozen major data mining techniques to evaluate their performance and gain insight on which credit card accounts were likely to default compared to the client’s world-class baseline model. The resulting model ensemble significantly improved early identification of bad credit risks.
By applying advanced techniques for modeling and visualizing customer records, Elder Research created a combined data and text mining solution to increase mar- keting efficiency and reduce churn. The model improved targeted messages which resulted in higher profitability for nTelos, a regional mobile phone carrier.
DentaQuest engaged Elder Research to improve on existing models used for assessing the performance of Medicaid Dental providers. The goal was to create a single simplified risk score that would provide a holistic 360 degree view for each service provider. We developed a provider risk scoring model that enabled targeted intervention with low quality providers and reduced per patient cost by nearly 20 percent.
Elder Research designed and deployed an automated fraud detection solution for the New York Department of Labor Unemployment Insurance Integrity Center of Excellence. The tool was estimated to have identified 1200 claims annually before the current investigative process with annual projected savings of $972,000 in recoverable and nearly $392,000 in non-recoverable overpayments.
Elder Research designed and deployed an automated fraud detection and visualization solution for a national Workers’ Compensation Insurance Program to improve the efficiency of fraud detection and investigation by prioritizing high-risk cases.. Investigations and forensic analysis that took hours can now be completed in minutes, making the most efficient use of limited and valuable resources.
Elder Research identified a molecular signature for asthma and developed a predictive algorithm to define asthma, and identify asthma sufferers who are ill and at risk for major respiratory complications, allowing for intervention prior to onset of acute asthma symptoms, thereby potentially preventing hospital stays and even loss of life.
A leading retail company for consumer product goods (CPG) wanted to determine how product demand changes with price in order to determine the optimal price for each product and increase their ability to respond to market changes. The models identified several products that were consistently offered above pricing thresholds.
Elder Research developed a data-driven risk assessment framework to triage Worker’s Compensation claims, prioritizing high risk cases for review and fast-tracking other cases to avoid manual review and adjudication. Claims that are routed to the fast track are assigned risk-based maximum payment limits for intelligent ongoing claims management.
Elder Research developed predictive models for the Michael J. Fox Foundation to score and rank medical tests and biomarkers to find those that offer the greatest value in predicting Parkinson’s disease. Deployed in a clinic, the Elder Research solution will help clinicians diagnose Parkinson’s based on available tests, and recommend the fewest additional (or next best) tests to improve disease prediction.
Elder Research partnered with a diversified bank to predict account closures, prioritize marketing interventions, and understand precursors to customer churn. We built a model that was 20% more effective at predicting customer churn than existing techniques.
Elder Research developed a predictive model to score and rank patients for outreach by development officers, based on their likelihood to begin a philanthropic relationship with the health foundation. The model identified 20-30% more patients with a high likelihood of becoming a grateful donor.
Elder Research created a risk model to help the U.S.Postal Service Office of Inspector General prioritize review of facility leases that were due for renewal. The risk model enabled business analysts renegotiating lease rates to focus on facilities with the most risk or highest financial impact. Projected savings was $99 million over 5 years.
Elder Research developed a risk scoring model to optimize the management of long-term care claims. The regression model successfully predicted which claims would experience expanded payout over time, identifying claims most in need of clinical review. The proactive recommendations for patient service resulted in better patient care and more efficient claims management.
Elder Research partnered with the U.S. Postal Service Office of Inspector General to develop and deploy a custom solution to identify and prioritize questionable contracts and healthcare claims for investigation. Leads generated were 74% actionable, resulting in over $11 million in recoveries, restitutions, and cost avoidance in the first year.
Elder Research designed, prototyped, piloted, and deployed an automated service provider and warranty fraud detection system. The solution prioritized investigative workload to maximize resource utilization and minimize loss to fraud and is credited with saving over $67 million in service provider and warranty fraud.
Peregrine Systems advanced to the forefront of the IT management industry by partnering with Elder Research data mining and software development experts to develop its DecisionCenter software. IT executives can now accurately predetermine how changes made to staff and infrastructure resources will affect business.
Elder Research partnered with Excella Consulting to build an end-to-end grant risk estimation solution in the client’s AWS cloud. It used text mining and document classification to extract CPA Findings from audit reports and assign risk scores to federal grant recipients. More than 260 auditors, investigators, evaluators, and lawyers now use the tool to support audits, evaluations, and major investigations. This project has become one of the five most important initiatives of our client.
Elder Research text mined survey data and provided exploratory and predictive analysis to identify insights and trends that affected conference attendance. This helped guide the client’s conference content programming and global conference planning.
Elder Research largely automated the extraction of valuable underwriting information from scanned Attending Physician Statement documents using text mining tools and techniques. Extracted text features were transformed into electronic formats suitable for predictive modeling.
Elder Research applied state-of-the-art text mining techniques to the problem of sentiment analysis. The solution focused on the entire survey text rather than limiting analysis to lists of positive and negative keywords. This approach more accurately identified important issues and ignored off-topic comments, leading to more focused action and improved customer loyalty.
Elder Research computationally generated more than 30 million descriptive metadata entries for more than 300,000 images using AI and ML technologies. We used the digitized text in conjunction with image-to-image matching algorithms to link digitized metadata records to their source images, joining this information with computationally generated image-based metadata and facial recognition results to provide the client with a rich and searchable image archive.
Elder Research created an automated evaluation of the impact a printer supplies loyalty program had on customer lifetime value, retention, and market share. The solution increased program enrollment by 35%, resulting in more than $600 million in sales over five years.
Elder Research developed a user segmentation model based on SolidWorks’ software usage logs. These segments, reproducible with 92% accuracy, served as the basis for helping SolidWorks employ product log analytics to better understand, communicate with, and serve their users.
Elder Research used sensor data to develop risk models that predicted well freezes with 70% accuracy, enabling targeted well intervention to reduce freeze remediation cost and recover gas production that would have been lost or deferred.
Elder Research validated the processes and methodologies applied by RightShip in the development of RightShip Qi, a new model that enhances maritime safety by predicting the casualty risk of ships. With the new RightShip Qi model, the organization seeks to take ship vetting to an entirely new level by implementing a data-driven solution with machine learning techniques.