Mather Economics

Services We Offer

Profit growth requires not only pertinent data but also experts who know how to translate that data into actionable insights. Our team combines the necessary inputs, including the technical, operational, and analytical resources required to implement effective growth solutions.

Customer Lifetime Value

In the healthcare sector, we use historical customer data to model retention in econometric studies using survival analysis to measure the effect of patient characteristics, positive/negative events, service offerings, and other factors. Survival analysis was initially developed in the biomedical sciences field to analyze factors affecting time until patient death. This set of analytical methods, often called duration analysis in Economics, is now commonly adapted for use in applied economics, healthcare, marketing, finance, and publishing. Using the output of these models, we can predict the probability that a patient will continue to utilize and promote services in future periods. We weight future operating margins for each patient by the probability the patient will be “active.”


Patient Risk models assist our clients in identifying patients/patient groups that are likely to develop secondary conditions during their hospital stay, the worsening of an existing condition during or after hospitalization, readmission to the hospital, as well as other risk scoring determined by a comprehensive needs analysis. Once the predictive models have been completed, we work with our clients to develop intervention and accountability strategies within the designated risk stratification models. We also develop an A/B testing and reporting program for tracking performance once risk mitigation strategies have been implemented.

Revenue Forecasting

We create models to help our clients develop weekly revenue forecasts based on previous results combined with future predictions to gain a comprehensive view of accounts receivable trends. Our team will use historical data on accounts receivable balances and fit a model to a portion of the data. Projections from this model will be compared to actual AR balance data to verify the accuracy of the model. On an ongoing basis, actual results will be compared to predicted balances. Periodic performance reports and model updates are provided to our clients to ensure forecast accuracy.


The digital transformation of customer interaction and relationships has created a need for new data capture and analytics technologies and approaches. We have developed a toolset for digital customer analytics called Listener™ that combines data from multiple digital sources into a single holistic customer record. Analytics and reporting modules then utilize the data to implement value-adding campaigns.

Case Studies

Large Hospital


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