Authors:
Scott Schneider, Kristen Chung, Ted Clark, Anvita Godavarthi, Manda Miller, John Pistone, Brian Taylor
Abstract:
An algorithm is proposed for in-store picking that provides various selectable shopping experiences based on different need-states that a shopper might have.
Background:
Grocery shoppers continuously change their needs and desired shopping experiences depending on shopping trip needs and time. What may be considered convenient one trip, wouldn't be convenient for the needs of another trip, despite it being the same individual. This idea incorporates a shopper’s changing needs and situation to help them in a way that provides the right type of "convenience" and needs. Right now, there are algorithms to allow for picking efficiency but the do not tailor the shopping trip to different need states that a shopper might have.
Description:
1. Retailers upload planogram and store information into mobile app
2. Shoppers download the app, and add in shopping list
3. Shoppers indicate before in-store shopping, what their experience need is from a list like the following:
a. Need for Speed: get in and out quickly, tell me the fastest route
b. Cost Conscious: Find alternatives and point me to better priced items
c. Healthier options: Pinpoint on my shopping list where I can substitute healthier option
d. Exploratory: Add in recommendations, take me on a tour of the store
The app uses AI to personalize the experience and to provide promotions and recommendations that are relevant to that shopper at that time. This makes it more likely that the shopper will use the promotion and appreciate it.
Usages:
In-store shopping apps
Enabling Technology:
AI Algorithm
TGCS Reference 3231
