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merchant banking is an industry that relies heavily on close engagement with customers through a network of sales representatives and a major merchant banking group in the UK approach the data school and asked how we could optimize how they did this so they had nine salespeople operating over these nine competing ranges and I think these different territories were pretty much drawn up free hand and roughly follow regional boundaries while these seem intuitive and manageable to traverse by car they resulted in these territories that provided radically different sales opportunities for our different salespeople so our challenge here was to redo all these sales territories based on data rather than intuition into balanced manageability with equity okay so we started with three inputs postal districts within each person's existing territory postal district shapefiles and also listed customers and their estimated turnover from which we could calculate sales opportunity so we thought we'd start from a blank slate and begin to build territories from the bottom up giving us a bit more flexibility to factor in our own conditions so to do this we first need to define some starting seeds to anchor growth around and we took these as a central point of each person sells territory existing sales territory and this was quite easy to calculate through the sovereign's tool now if you're to look at the sales potential of each person's home district what you'll see is a huge variance and if you monitor even this help you begin to assign additional districts to the likes of mark Corrigan and Jeremy Osbourne and it continues to do this and till your Crocs mate near equity and this is the whole basis of this macro here which starts by finding out which person has the lowest sales opportunity assigning them their closest district recalculating the salesperson opportunities after that and then iterating until all the postcodes are being assigned to illustrate this I've animated the process at high speed and you can begin to see the sales territories growing out around their centroids and in doing so the sales opportunities begin to even out and they stay even due to the equipment distribution of new territories however you reach some issues as you get towards the end here and as territories begin to get carefull at corners the next nearest postal district might be the other internet country which causes these violins to appear which are going to cause logistic issues and we want to get rid of to deal with the formation of these islands we redefine each person's territory as a single biggest polygon or mainland blob using a combination of these tools here and then we used a similar macro to before to acquit ibly distribute these island blobs on the salespeople but this time only two territories that they were directly butting onto and what you end up with are these quite compact homogeneous territories but at the cost of sacrificing some of the equity so to get things back on track we use one final macro and this looks a bit more complex than the others because the two spatial process is going on so like the other macros in the favour of equity this starts by finding the sales person with the lowest current opportunity and then returning a list of districts touching their territory using the spatial match then out of these districts prioritizing the one which is both close to the salesperson century to favor the growth of the quite quite circular compact territory and also the district which is from the donor with a greatest current sales opportunity so we stopped killing two birds with one stone here and swapped that district between the salespeople and iterate until you get reasonable equity here's administration of that swapping process playing out you see those postal districts changing hands and the end result is they have these relatively compact and malleable sales territories and you're almost half the standard deviation in sales opportunity so our final solution doesn't appear that different from the bank's original map the simple redistribution of districts is considerably evened up the sales opportunity from a standard deviation of 5.3 percent to one of 1.2 percent and another benefit of this process over the predefined sales territories is that these boundaries are dynamic this can update automatically in response to new information on customer opportunities we can also take this one step further and transform it into an alter it's app that allows the bank to change the parameters of the macros it's optimized territories based on their own priorities for example if they want it to begin to aggregate territories based more on sales rep of convenience or based more on customer opportunities