Expanding on my comment, the simplest predictive model would be to just compute the conversion rates Pr(conversion | action) for each action {A,B,C,D, ... }.
A solution in R:
library(dplyr)
library(tidyr)
set.seed(100) # for reproducibility
action <- sample(letters[1:5], 100, replace = TRUE) # dummy up sales actions (independent variable)
converted <- as.logical(rbinom(100, 1, prob = .5)) # dummy up physician conversion (dependent variable)
(df <- data.frame(action = action, converted = converted))
# Compute conversion rates for each action
conv_df <- df %>%
group_by(action, converted) %>% # unique combinations of action by converted
summarize(freq = n()) %>% # compute freq of unique combinations
spread(converted, freq) %>% # spread rows to cols
mutate(rate = `TRUE`/sum(`TRUE`,`FALSE`)) # compute conversion rate
conv_df[order(-conv_df$rate),] #reverse sort
##Source: local data frame [5 x 4]
##Groups: action [5]
##
## action FALSE TRUE rate
## (fctr) (int) (int) (dbl)
##1 d 8 13 0.6190476
##2 e 7 11 0.6111111
##3 a 5 6 0.5454545
##4 c 13 10 0.4347826
##5 b 17 10 0.3703704
The solution above assumes the application is to be some sort of guide for sales reps... if you are wanting to model the distribution of actions in sales contexts, you would need to compute the prevalence of each action as well.