Authors:
Wendy Darby
Abstract:
This Disclosure proposes a customizable service agent that allows retailers to extend a customer service oriented brand into their self-checkout kiosks/lanes. Shoppers crave the convenience and immediacy of self-checkout but miss personal interactions. A customer service agent built into the self checkout lanes helps consumers by making sure they have everything they need and offering assistance if anything was forgotten or couldn't be found.
Background:
Customer Experience is a becoming a branding tool for many retail brands to help set themselves apart from their competitors and to win customer loyalty. Consumers crave personal attention especially with social isolation from COVID. They crave what seems like opposing experiences by wanting the convenience of online shopping or self-checkout and the assistance of a personable expert. We can help retailers meet both of these drives with a Configurable Customer Service Agent for self-checkout kiosks. A customizable service agent allows retailers to extend a customer service oriented brand into their self-checkout kiosks/lanes. Shoppers crave the convenience and immediacy of self-checkout but miss personal interactions. A customer service agent built into the self checkout lanes helps consumers by making sure they have everything they need and offer assistance if anything was forgotten or couldn't be found. A customizable customer service agent benefits retailers because it allows them to reinforce their brand identity.
Description:
Preferred embodiment of this invention:
Leverage natural language recognition, intelligent agents, and robots (or sales representatives)
to provide a customizable consumer experience at checkout.
- Add the personable customer service agent into self checkout lanes. They can be configured to align with the branding of the retailer.
- Service Agent can allow stores to customize checkout experience for customers while modeling a cashier lane experience.
Example 1- Lane asks shopper if they found everything they were looking
for? If the shopper answers no, the lane Service Agent could be
configured to offer to summon a Shopper Assistant to get help, print
off location of item that was missed and directions on printout, send
directions to item in store to the shoppers phone so they can go get the
missed item, or deploy a robot to fetch the missing item to the lane
while the shopper continues to checkout.
Example 2- shopper scans taco shells but no salsa or shredded cheese has been scanned- the lane
Service Agent may ask if they forgot those items? If the shopper answers
yes the agent may offer to get them while shopper continues
transaction. (The agent may retrieve them using a robot, drone, or sales representative.)
Example 3- if large item was scanned the lane assistant may ask if the shopper would like to signal for assistance to
their car?
Enabling Technology:
The service agent utilizes advancements in machine learning and natural language recognition to make useful suggestions to consumers to minimize the frustration from not finding items on their list or forgetting an important ingredient. The agent can also be customized to use mobile phones, cashiers, or robotics to assist the shopper.
Claims:
Part of this idea’s novelty is linking AI behavior to branding and the retailers marketing strategies. How to program an agent to be helpful is a different question from how do you systemize behavior variances so that the result aligns with branding goals. Making the agent “customizable” means designing a system for how AI components work together to create an experience unique to the brand.
One retail customer may want their agent to greet the customer as soon as they enter the store and follow closely behind to be ready to offer assistance. A second retailer may prefer their agent to give customers space to shop and only assist when customer addresses them about an item or at checkout. A third retailer may want their agent to use predictive analysis to suggest up-sale items at the checkout. Another retailer may want to use predictive analysis to suggest projects or recipes to be sent to the customers phone, email, or loyalty account.
TGCS Reference 2298
