How to build recommendation model for calling prospects - r

Question

My goal is to better target prospects at a higher call success rate, based on time of day and prior history.
I have created a "Prodprobability" column showing the probability of a PropertyID answering the phone at that hour for the history of calls. Instead of merely omitting Property ID 233303.13 from any calls, I want to retarget them into hour 13 or hour 16 (the sample data doesn't show but the probability of pickup at those hours are 100% and 25% respectively).
So, moving forward, based on hour of day, and history of that prospect picking up the phone or not during that hour, I'd like to re-target every prospect during the hours they're most likely to pick up.
sample data
EDIT: I guess I need a formula to do this: If "S425=0", I want to search for where "A425" has the highest probability in the S column, and return the hour and probability for that "PropertyID". Hopefully that makes sense.
EDIT: :sample date returns this

Answer 1

The question here would be are you dead set on creating a 'model' or an automation works for you?
I would suggest ordering the dataframe by probability of picking the call every hour (so you can give the more probable leads first) and then further sorting them by number of calls on that day.
Something along the lines of:
require(dplyr)
todaysCall = df %>%
dplyr::group_by(propertyID) %>%
dplyr::summarise(noOfCalls = n())
hourlyCalls = df %>%
dplyr::filter(hour == format(Sys.time(),"%H")) %>%
dplyr::left_join(todaysCall) %>%
dplyr::arrange(desc(Prodprobability),noOfCalls)
Essentially, getting the probability of pickups are what models are all about and you already seem to have that information.
Alternate solution
Get top 5 calling times for each propertyID
top5Times = df %>%
dplyr::filter(Prodprobability != 0) %>%
dplyr::group_by(propertyID) %>%
dplyr::arrange(desc(Prodprobability)) %>%
dplyr::slice(1:5L) %>%
dplyr::ungroup()
Get alternate calling time for cases with zero Prodprobability:
zeroProb = df %>%
dplyr::filter(Prodprobability == 0)
alternateTimes = df %>%
dplyr::filter(propertyID %in% zeroProb$propertyID) %>%
dplyr::filter(Prodprobability != 0) %>%
dplyr::arrange(propertyID,desc(Prodprobability))
Best calling hour for cases with zero probability at given time:
#Identifies the zero prob cases; can be by hour or at a particular instant
zeroProb = df %>%
dplyr::filter(Prodprobability == 0)
#Gets the highest calling probability and corresponding closest hour if probability is same for more than one timeslot
bestTimeForZero = df %>%
dplyr::filter(propertyID %in% zeroProb$propertyID) %>%
dplyr::filter(Prodprobability != 0) %>%
dplyr::group_by(propertyID) %>%
dplyr::arrange(desc(Prodprobability),hour) %>%
dplyr::slice(1L) %>%
dplyr::ungroup()
Returning number of records as per original df:
zeroProb = df %>%
dplyr::filter(Prodprobability == 0) %>%
dplyr::group_by(propertyID) %>%
dplyr::summarise(total = n())
bestTimesList = lapply(1:nrow(zeroProb),function(i){
limit = zeroProb$total[i]
bestTime = df %>%
dplyr::filter(propertyID == zeroProb$propertyID[i]) %>%
dplyr::arrange(desc(Prodprobability)) %>%
dplyr::slice(1:limit)
return(bestTime)
})
bestTimeDf = bind_rows(bestTimesList)
Note: You can combine the filter statements; I have written them separate to highlight what each step does.