The phrase "attrition model" is correct and usable in written English.
It can be used in contexts related to statistics, business, or research, particularly when discussing the rate at which participants drop out of a study or customers leave a service.
Example: "The company implemented an attrition model to better understand customer retention and improve their services."
Alternatives: "dropout model" or "turnover model".
The continuous attrition model and intermittent attrition model were used for the sulphation-attrition model.
Attrition model plays an important role in determining limestone utilization efficiency, whereas the reaction model played only a minor role.
Alternatively, an exponential decay attrition model is proposed and confirmed to describe the time-dependent attrition behavior.
A simplified growth and attrition model is developed for the diameter change of the particles in the granulator.
A first approach of a new design procedure is therefore provided, where an attrition model is implemented in a conventional cyclone model.
For test series with fly-ash recirculation during coal combustion, the continuous attrition model under steady-state sulphation-attrition conditions yielded values that resembled the experimental results.
Based on a synthesis of traditional attrition models and the empirical literature, an online doctoral persistence model was developed using archival data from 148 candidates.
Many HR vendors, including Oracle, now offer some AI capabilities for "smart candidate lists" or employee attrition modeling.
Its endurance is explained in part by the attraction-selection-attrition model first introduced by Benjamin Schneider: People are drawn to organizations with characteristics similar to their own; organizations are more likely to select individuals who seem to "fit in"; and over time those who don't fit in tend to leave.
With the aim of quantifying a difference in tag losses between the conventional and the Betyp tags, we integrated the tag type into the tag-attrition model used to fit the recoveries per unit of time.
When asked what they were doing with AI, the most common responses in terms of applications (first choice in a list) were "identifying at-risk talent through attrition modeling" (25%), "modeling our talent pipeline" (22%), "predicting high-performing recruits" (18%), and "sourcing best-fit candidates with resume analysis" (15%).
