Hi again smart folks,
So I was contacted to do HTM consulting by someone from a company that visited Numenta (bMind). They're very excited by HTM and NuPIC and presented me with an application regarding sales in a supermarket. I had a couple of basic ideas for how they might fit they're data into NuPIC and I'm very curious to bounce them off of anyone on here. Below is a part of the message he sent me describing the application, and below that is what I said to him.
At this moment we need to build a POC to prove and show HTM potential in a specific task: considering a supermarket dataset containing customer profile (types, social class, credit card classification, etc) combined with purchasing transactions (history of products/SKUs acquired by each customer with timestamps) and products characteristics (group, type, etc) predicts in real-time for a given customer what type of products in several specific times or periods (season, festivities, etc) a marketing campaign would be successful. Example: if I want to push sales of a specific meat product in a Friday what group of customers should I focus on?
_Our idea is to create an environment in Google Cloud, install Nupic there, setup properly all HTM School demos available in order to use their visualization in demos when needed and then create this POC to be presented to a specific potential early adopter company. _
That makes sense? Would like to hear your thoughts!
Here was my reply:
This is certainly an interesting application area, and I'll be glad to offer my intuitions. One initial idea that comes to me is to learn a Nupic model for each group of customers. Each model would learn the temporal buying habits of its group, and then on a given day (like that Friday in the example) you could check the predictions from each model and see which group is most prone toward buying that product.
You could also create a Nupic model for each given product or group of products (that specific meat or meats in general), and learn the sequences of leading buyer groups. As in which group bought the most on Monday, then which on Tuesday, Wednesday etc. Perhaps there are some temporal patterns for who follows who in buying that product the most.
Alternatively if you weren't focused on customer sub-groups but all customers, you could create separate Nupic models for each product or group of products and learn the sequences of how much is sold day by day (or week by week, or whatever the time scale is). Maybe on one day (/week/time step) the store sells more vegetables, then the next more meat and the next more dairy. If there are solid patterns in this way you could predict that Friday will be a high meat-selling day in the store and offer discounts to maximize sales.
As I said these are just some basic initial intuitions, though if you have some test to test I could run it through Nupic to test which approaches might be most promising. Looking forward to it,
As I said if anyone has any criticisms of my basic ideas or any alternatives I'd be extremely curious to hear it! Thanks once again,