1. The standard textbook approach to calculating the upper control limit is done by multiplying the standard deviation by 3. What amount of error are we accepting by only using a 9 month baseline period?
This is highly dependent on how your "rate of complaints process" runs over the long term. How long do you feel it should be? Do you have any data to support that it should be longer? Maybe it should be as long as your "sales cycle" or something that encompasses any seasonality in the sales trends?
2. We have had outside consultants examine our approach, and they suggest using a binomial distribution for calculating an upper control limit. Is this approach appropriate given that our data is continuous and a binomial distribution is discrete?
Actually, your data is discrete, because it is countable (you can count complaints, and you can count # of units sold). If you are talking about large numbers, you may be able to use the normal approx to the binomial.
3. Unfortunately our method is not without limitations. Our number of units sold per month is not a good denominator since it does not tell us how many products are actually in use. It also creates false alarms when sales are volatile. Does anyone have any suggestions of a better way to monitor complaints or to adjust the denominator?
Here you'll need to do some market research - but I would assume that units sold would be about as close as you can get to # of products in use...