You know how the song goes - he’s making a list, he’s checking it twice. But if you ask us, all Santa Claus should really be doing come December is doing some light maintenance on his machine learning models before heading off on his ML-optimized route on Christmas Eve. It might take a few elves-turned-data scientists, but here are four ways that Santa could optimize with data.
More Accurate Naughty or Nice Predictions
He sees you when you’re sleeping, he knows when you’re awake - but so does the internet of things (IoT)! Collecting data from wearables or other sleep trackers would allow Santa to easily add this data to a model that determines whether you’ve been naughty or nice (and even predicts your likely behavior in the future).
But there are other possible data sources, some of them likely private to Santa’s database and some open source, for high-accuracy naughty/nice predictions (in general, the more data from different places, the better the model):
- Past years’ naughty/nice outputs
- Analysis from social media feeds (including Reddit comments - yes, it exists, talk about “so be good for goodness sake”)
- Letters to Santa (bonus: analyzing these can be automated, too!)
- Public-record crime data
Naughty -> Nice with Response Modeling
Sometimes, people go from the nice list to the naughty. Not unlike addressing customer churn, Santa might try to bring them back to the light - but how can he do it effectively when he’s short on elf bandwidth?
The answer is uplift modeling or response modeling (that is, building a predictive model that separates the likely responders from the non-responders and targeting only the likely responders with a campaign). That means he could be spending time in the off season focused on converting people from the naughty list who are most likely to make it back onto the nice list instead of trying to reach the potentially millions of naughty listers who have no hope of converting.
Predictive Maintenance: From the Sleigh to the Workshop
Santa’s biggest potential time and cost saver? Like most with high-capital assets (including manufacturing equipment and vehicles): predictive maintenance (get the guidebook).
Predictive maintenance often allows for the detection of impending failures that could never be detected by human eyes - take, for example, imaging that looks for microcracks in machinery (whether it’s producing lenses or toys), even while in use. With predictive maintenance, downtime and repairs are usually directly tied to likely failure, minimizing cost (less downtime, less labor time, less chance of unexpected failure) and maximizing asset life.
By contrast, traditional maintenance techniques for the sleigh and the workshop (run-to-failure, preventative, or some combination of the two) inevitably mean unexpected repair, which leads to longer downtime on top of unnecessary downtime due to regular inspection. So the bottom line is that predictive maintenance is the most reliable way for Santa to guarantee a working sleigh come Christmas Eve and, of course, on-time gift delivery.
Better Gift-Giving via ML-Powered Recommendation Engine
For Santa, delivering exactly the right gift involves a veritable army of elves, fawning over whether the child will like this… or that. With a simple recommendation engine, Santa could eliminate all the hemming and hawing, leaving more time for relaxation (and egg nog).
While most people think of complicated “if you like this, you’ll also like that” made famous by the likes of Amazon and Netflix, straightforward recommendation engines - if you have the right data - are not so difficult to build or maintain.
So there you have it - Santa’s job, optimized by data. If you want to create a recommendation engine of your own, check out the guidebook to do it yourself.