Read about the challenge here: [Link to the Tresata github page](https://github.com/tresata/hackathonclt)
**Code/Product/Concept developed at the Hackathon Charlotte 2014.**
I decided to develop a app called "Express Lane Manager", the concept was developed for store clerks/express lane managers/store managers to essentially use iBeacon to recognize customers when arriving (i.e. in a drive up lane), and display details about that customer to provide a personalized shopping experience. The app displayed potential opportunities by doing a basic basket analysis of his current purchases on the express order list, by looking at when last certain products were purchased, and were not being purchased today (maybe they shoud be due for purchase) and hopefully encouraging the buyer to increase the basket size due to convienence.
- The app would also display coupon opportunities, similar products (once again, using a algorithm to consider purchase history, product category/UPC relevance) if something was out of stock, and some basic charting analysis to provide a better shopping experience.
Data center hardware/hosting was provided by Data Chambers, (5 Node Hadoop Cluster). In order to transfer data between the Hadoop slave and the iOS app I was running a SimpleHTTP Server (Port 8890 to avoid conflicts).
App was developed using iOS, a lot of the UI was mocked up in Photoshop to save on time constraints and simple used as a reference image in XCode. The Hadoop job was written in Scalding and included below. (Obviously lots of opportunity for improvement!).
This was the data set we were working from, it was around ~650 million rows.
Data Dictionary
UPC_NUMBER long unique product code of item
MASTER_UPC_NUMBER long master UPC number, UPC numbers go under this
ITEM_DESCRIPTION string describes item
DEPARTMENT_NUMBER long department number
DEPARTMENT_DESCRIPTION string describes department
CATEGORY_NUMBER long category number of item
CATEGORY_DESCRIPTION string describes category of item
SUBCATEGORY_NUMBER long subcategory of item
SUBCATEGORY_DESCRIPTION string describes subcategory of item
RECEIPT_NUMBER string recipe number of the purchase
ITEM_QUANTITY long how many items was bought
EXTENDED_PRICE_AMOUNT float actual sale per swipe
DISCOUNT_QUANTITY float number of coupons applied
EXTENDED_DISCOUNT_AMOUNT float amount discounted
TENDER_AMOUNT float amount tendered by the customer for the transaction
TRANSACTION_DATETIME string date of transaction
EXPRESS_LANE long flag of whether the purchase was through Express Lane, tagged to recipe number. 1 mean yes, 0 means no
HHID string house hold id
