Experience.We have worked with businesses across many different industries to help them use data science to solve some of their toughest challenges. Some of our projects are highlighted below. We also have several others that we are not able to publish here. We would welcome the opportunity to discuss further details of your needs and our other projects. Please reach us via our Contact Us page.
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Experience Highlights
Trading - Electricity Price Forecasting
Challenge: An energy and commodity trading software firm was seeking a data science development partner to create a predictive analytics price forecasting product that they wanted to sell to their customer base - wholesale electricity traders, generators, load serving entities and commercial/industrial users.
Answer: Developed PA technology, modeling architecture and processes that underlie a range of new electricity price forecasting products. These products deliver to their customers, day ahead predictive forecasts and real time alerts to optimize bid, offer and trading decisions.
Healthcare/Medical Devices - Improved Care Delivery; Economics of Technology Adoption
Challenge: In orthopedics sector, predict clinical and cost benefit to practices and systems for new technology.
Answer: Developed insights to bridge clinical & economic domain by modeling data for impact on hospitals (geographic based), patients, payers, practice and service line. Analytics included halo and direct benefit effects of technology adoption. Produced prediction values used to provide cost and clinical justification to drive sales by supplier and acceptance by user/purchaser.
Challenge: In diagnostic imaging sector, predict impact of new technology on purchase behavior and procedure volumes.
Answer: Created insights across key raw materials markets, OEM bill of materials, patient workflow, facility operating costs and impact on product margins to predict and understand value of technology adoption to manufacturers and providers. Results served as key component to building economic basis for successful major capital raise to further fund commercialization.
Retail - Market Analytics
Challenge: Understand when customers and prospects will be in-market to purchase in order to align direct response spend and increase marketing ROI.
Answer: Increased marketing efficiency and return by building models, developing customer and prospect scores and delivering strategic and tactical insights that allowed investments to be optimized, increasing yield and ROI.
Financial Services - Performance Improvement
Challenge: An FHA mortgage originator sought to uncover what factors were causing a sudden but persistent rising default rate when underwriting standards had not changed.
Answer: Created project to identify, understand and predict new factors causing shift and what changes are necessary with practices to resolve rising default rate.
Challenge: For a multi-national bank, uncover what strategies and tactics (both operational and technical) were causing their credit card marketing return on investment to fall.
Answer: Created the processes needed to identify, understand and predict the drivers of credit card marketing return, including the implementation of new modeling algorithms, measurement and decision processes to focus spend and efforts on highest return population.
Enterprise IT - Risk Management
Challenge: Can outages be predicted in cloud deployments where differing service level agreements with providers could cause unacceptable business disruption.
Answer: In-process. Exploring the application of data science and PA in the area of business continuity management and disaster recovery to support decision making for early fault prediction for a system integrators client with revenues exceeding $1B
Challenge: An energy and commodity trading software firm was seeking a data science development partner to create a predictive analytics price forecasting product that they wanted to sell to their customer base - wholesale electricity traders, generators, load serving entities and commercial/industrial users.
Answer: Developed PA technology, modeling architecture and processes that underlie a range of new electricity price forecasting products. These products deliver to their customers, day ahead predictive forecasts and real time alerts to optimize bid, offer and trading decisions.
Healthcare/Medical Devices - Improved Care Delivery; Economics of Technology Adoption
Challenge: In orthopedics sector, predict clinical and cost benefit to practices and systems for new technology.
Answer: Developed insights to bridge clinical & economic domain by modeling data for impact on hospitals (geographic based), patients, payers, practice and service line. Analytics included halo and direct benefit effects of technology adoption. Produced prediction values used to provide cost and clinical justification to drive sales by supplier and acceptance by user/purchaser.
Challenge: In diagnostic imaging sector, predict impact of new technology on purchase behavior and procedure volumes.
Answer: Created insights across key raw materials markets, OEM bill of materials, patient workflow, facility operating costs and impact on product margins to predict and understand value of technology adoption to manufacturers and providers. Results served as key component to building economic basis for successful major capital raise to further fund commercialization.
Retail - Market Analytics
Challenge: Understand when customers and prospects will be in-market to purchase in order to align direct response spend and increase marketing ROI.
Answer: Increased marketing efficiency and return by building models, developing customer and prospect scores and delivering strategic and tactical insights that allowed investments to be optimized, increasing yield and ROI.
Financial Services - Performance Improvement
Challenge: An FHA mortgage originator sought to uncover what factors were causing a sudden but persistent rising default rate when underwriting standards had not changed.
Answer: Created project to identify, understand and predict new factors causing shift and what changes are necessary with practices to resolve rising default rate.
Challenge: For a multi-national bank, uncover what strategies and tactics (both operational and technical) were causing their credit card marketing return on investment to fall.
Answer: Created the processes needed to identify, understand and predict the drivers of credit card marketing return, including the implementation of new modeling algorithms, measurement and decision processes to focus spend and efforts on highest return population.
Enterprise IT - Risk Management
Challenge: Can outages be predicted in cloud deployments where differing service level agreements with providers could cause unacceptable business disruption.
Answer: In-process. Exploring the application of data science and PA in the area of business continuity management and disaster recovery to support decision making for early fault prediction for a system integrators client with revenues exceeding $1B