The story eventually had a less than desirable ending. After producing accurate daily forecasts for months our work was replaced by another group's work, with the predictions that were much higher than ours. It turned out that having attrition sometimes higher than predictions and sometimes lower was very stressful to upper management and what they really wanted to be told wasn't an accurate prediction of attrition but that they were beating the forecast.
Ultimately the problem was a large difference between what management wanted and what they said they wanted. What management said they wanted was an attrition forecast at a daily level that was very accurate. To this end my group was constantly refining and testing models using the most recent data we could get. What this meant was that all the most recent attrition programs were already baked into the forecasts.
What management really wanted to be told was the effect of their attrition programs, and by the design of the forecasts there was no way they could see any effect. It must have been very disheartening to look at the attrition forecasts month after month and being told in essence your programs were having no effect.
What my group should have done is to go back roughly a year, before all of the new attrition programs started, and to build our forecasts using older data. Then we could make the comparison between actual and forecasts and hopefully see an effect of programs.
Surprisingly, I've met other forecasters that found themselves with this same problem: their forecasts were accurate and they got the project taken away and given to a group that just made sure management was beating the forecast.