This page features some of our work for organizations in various industries:
- For the US Department of Agriculture (USDA), provided statistical consulting on effects of slaughter establishment line speeds on microbial quality (2018-2020).
- Created predictive analytics models for hypertension and cardiovascular risks based on blood lead levels and other individual-level predictors (2019).
- For an animal antibiotic manufacturer, assessed public health consequences of animal antibiotic use in China and the US (2018-2019).
- For European petroleum companies, analyzed data from Chinese factory workers to determine how well concentrations of benzene in air predict levels of urinary benzene metabolites (2018-2020).
- For health insurance back office operations giant TriZetto, assessed healthcare predictive analytics trends and vendor offerings, advised top management on predictive analytics technology acquisitions.
- For Rogers Communications, developed causal models of customer satisfaction; identified high-impact interventions for improving customer satisfaction; helped to develop achievable targets and strategies for improving customer experiences in different channels.
- Delivered a statistical analysis of the causal drivers of customer satisfaction to top executives at Comcast Cable; identified realistic targets and interventions for improving customer satisfaction.
- For a top cable company, worked in partnership with North Highland consulting company to deliver a predictive model that identifies which customers are most likely to drop accounts, well before the event and with much higher accuracy than previous models.
- For an energy utility, worked in partnership with North Highland consulting company to deliver a predictive model of customer bad debt and account write-offs that greatly extended the lead time over which high-risk customers could be identified and targeted for intervention.
- For a telecommunications company, worked in partnership with North Highland consulting company to develop a predictive model of customer marketing channel choice, and usage as a function of quantity of channel experience (e.g., for web site, call center, retail store, and other channels.) Used the model to quantify financial impacts of improving web-based customer care.
- Also in partnership with North Highland consulting company, analyzed employee survey data for a major telecommunications provider and quantified patterns of internal communications (conference calls, managing e-mail, company news letters and bulletins, meetings, etc.); time spent on these activities by employees with different job roles and in different VP areas; and potential to reduce employee burden and improve the value and efficiency of internal communications.
- For a European wireless telecommunications provider, analyzed customer data to help develop more predictive segments; held a one-day intensive course in Brussels on advanced statistical models and methods for quantifying customer value in the short and long runs, based on probability and statistics models of customer behaviors in response to company offers.
- Delivered to an international telecommunications company a needs-based predictive segmentation model for cell phone customers.
- In partnership with North Highland consulting company, delivered to a directory company a credit scoring and data mining model for identifying customers at greatest risk of defaulting on Yellow Pages agreements.
- Delivered to an internet services provider (ISP) a decision-support model for predicting customers with the highest churn potential and recommending specific interventions to reduce churn. This system was found by the client to reduce churn by over 40% within 4 months among at-risk customers.
- Delivered to a financial services company a set of predictive clusters for simultaneously predicting churn, upsell, and cross-sell potentials for existing customers. The predictive validity, stability, and high practical value of the predictive clusters were confirmed by the client.
- Completed a study to identify ways to predict which competitive local exchange carrier (CLEC) customers would experience the most revenue growth in the next quarter and which would be most likely to drop accounts.
- For an insurance customer data showing that combining information from homeowner, auto, and other insurance lines using classification trees and transition models could dramatically improve accurate identification of cross-sell, up-sell, and retention opportunities.