Authors:
Vikrant Maheshwari, Kip Kirkpatrick
Abstract:
This disclosure aims to build better and more empathetic communities by recognizing and rewarding positive human behavior within retail environments. Customers often display helpful actions that go unnoticed—this system records such acts and offers rewards to promote a more considerate society.
Background:
There are many instances inside retail stores where customers assist associates or fellow shoppers. These helpful gestures often go unnoticed and unrewarded, despite having meaningful impact on safety, efficiency, and store culture.
Examples include:
- Customers placing caution cones near spills
- Giving up their spots to senior citizens and/or expecting mothers in checkout queue
- Taller customers helping others get products from the top of the shelf rack.
- People helping / prioritizing assistance to disabled individuals for different items in store
- Customers / sometimes kids helping the stocker to put back the fallen items from the orchid bins / square tables (e.g., oranges, etc.)
Some of these actions like placing caution cones can help prevent safety hazards and legal liabilities that could cost stores millions in lawsuits.
The current lack of recognition discourages these behaviors from being more widespread. The idea here is to build empathetic communities by highlighting these good behaviors. By highlighting and rewarding such acts, we foster community-minded behavior and create educational value for future generations.
Description:
The core idea is to record instances of good behavior in retail environments and appreciate the customers who perform them. Retailer may decide the reward the customer as well.
This can be achieved using two primary approaches:
I. Manual Recording:
On floor store associates can manually report acts of good behavior by:
- Printing and handing out one-time-use barcodes to the customer
- Scanning the customer's loyalty card and tagging the recorded event to it
If surveillance footage is available, clips from the relevant time can be archived and associated with the reward. During checkout at Point of Sale either through manual using cashier or self-checkout these identifiers can be scanned, and customers may receive discounts or loyalty rewards.
II. AI-Based Recording:
AI systems can be trained to detect good behaviors, such as:
- A customer picking up produce that has fallen or helping others reach items or assisting individuals with limited mobility
The AI model can:
- Capture and tag video segments
- Alert store assistants or the back office for validation
- Serve as input to further train the AI (closed feedback loop)
Where possible, AI can identify the individual or accompanying shopper (e.g., parents of helpful children). At checkout, rewards can be automatically applied.
Promotion of Good Behavior:
With customer consent, the video footage can be played during checkout or shown on store displays (e.g., hoardings, video walls) to inspire others and promote positive actions.
Usages:
- Encouraging empathy-driven actions in retail environments
- Reducing potential store liabilities through proactive customer behavior
- Training AI for recognition of positive customer behavior
Enabling Technology:
- AI/machine learning models for behavior detection
- Loyalty program integration
- Point of Sale / Self-Checkout software enhancement
TGCS Reference 00353
