Claim your CPD points
Several years ago my company undertook some work for a retailer to find good potential locations for new stores. It took a month or so for a smallish team (two to three analysts) to turn around the work - perhaps a few hundred hours in all.
Analytics tools and workflows have continued to evolve since then, so a question occurred to me recently. How quickly could a similar piece of work be done today?
I picked pharmacies (no particular reason, but they do have those charmingly complex rules around whether you're allowed to open or move a store) and set myself the task of a proof of concept analysis around whereabouts in Sydney is best to put new pharmacies.
The analysis involved:
Code developed and data inputs is available here for those interested. It uses a combination of LLM-generated and self-written code.
And here's the result - a map with the 15 locations deemed valuable by the model. If you were planning to open a pharmacy in the next year, you're welcome (and caveat emptor !).
The results are moderately interesting (to me, at least).
Under our model setup, the population size of the Inner West (relative to the number of pharmacies) lead to a strong clustering of potential sites. It also implies quite large average revenues for existing pharmacies in the area.
The result is relatively sensitive to the choice of the 70% decay factor, so a good understanding of how far people travel for pharmacies is important.
So, how long did this take to put together? In the end, it was just four hours of work - one or two orders of magnitude less than that motivating project.
The comparison is, of course, not particularly fair. Going from a proof of concept to a proper recommendation would need to incorporate many things, such as:
Doing these properly would add significantly to the time required - my exercise deliberately focuses on the low-hanging fruit.
Yet, I do feel the example is instructive since it reveals much about how efficiency in data science work has been boosted over time:
It does feel like something of a magical golden age, when analysis like this can be turned around quickly and cheaply. But perhaps this is premature, assuming things will only continue to get better and easier.
And as the analysis portions become easier, it creates more room for value-add on strategic thinking, value to business, model governance, and other higher-level topics - so plenty of work for actuaries. For now.