Insurance agency productivity and prospecting

 we want to try to identify and ferret out of our Prospect pool in addition to that we also has a part of that output we can determine whether or not they're operating an increasing decreasing or constant returns to scale excuse me and then for those agencies that are not on the right fish in front to your will how much is there shortfall in each product type not in our creative process so we have this distribution Network we make it bigger we have underperforming agencies that we're going to call how to leave how to identify those that we are going to call License to sell in Westfield products for mathematical approaches used 8 envelopment analysis in order to be able to do that and what we identify them as our 80 agencies that end up using an output orientation if we're dating don't know this was here till then gets scraped off and hand it off for predict process those are those are the model agencies that we want to try to identify and ferret out of our Prospect pool in addition of that we also has a part of that output we can determine whether or not they're operating an increasing decreasing or constant returns to scale sees me and then for those agencies that are not on there I fish in front to your will how much is there shortfall in each product type not in our process so we have this distribution Network and make it bigger we have underperforming agencies that we're going to call how to leave how to identify those that we are going to call but then even more importantly backfill for those after we've done that for independent variables are from that third-party data source that we have them both the agencies that are currently in the fleet the of the 1100 as well as their agencies that are at the prospect within the footprint as well and are dependent variables for logistic regression is one of the end up on the end efficient Frontier and 0 if they are not you are prepared for what we wanted we want to predict the productivity propensity to the likelihood that the propensity to be on the efficient Frontier but then we're also going to use and item based collaborative filter in order to identify agencies that are similar to this Prospect will also use that collaborative filtering to come up with an estimate for what we expect there a written premium to be as well two outputs here we have her protiviti propensity similar agencies as well as similarity score so when you go to Amazon and you see how you read this book you already read this book you bought this book you might also like this one in addition to just just like they said today that well for those people that did read that they gave it four and a half stars were also only going to display the similarities score that would be a value between 0 and 1 that then I'll allow the decision maker to see if you visit of agencies that are most like this prospect that I'm considering 4 how much are they alike is it are they saying I did something hi just start or is it a very low score giving them the confidence and whether or not they would want to appoint this agency in addition to using the collaborative filter to predict that premium we don't we're also going to predict the number of policies then that that agency would produce if appointed all of that falls into than our present process we have all that we've done all this Advanced analytics work we have all this data that we really merge together for the first time at Westfield that how can we take all that put it in one up display so that the decision maker can make an informed decision that is what are freezing process does both in terms a tabular format as well as a mapping that in addition to the things that were either inputs or outputs from each of the by the previous processes were also going to display their performance over an extended period of time so they can see their compound annual growth rate and then but also how well the day just doing the previous year so are they doing well consistently or do they just have one good year and we're using one one calendar year then as this that as the measure 


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