JOANNA SMITH: Hi.
I'm Joanna Smith.
And I'm going to show you how to leverage the power of Google
to enhance product discovery, increase basket size,
and create a seamless omnichannel journey for grocery
shoppers.
COVID-19 has disrupted how we approach
many of our regular tasks.
With grocery shopping, a common errand
has been suddenly transformed by safety concerns
and social distancing requirements.
So it's no wonder that online grocery shopping
is expected to accelerate at a much higher rate
than previous years.
To set the scene for this demo, I'm
going to take you through the journey of a grocery shopper.
Remy lives in the city with a stay-at-home advisory
and has been finding ways to avoid going out.
This weekend he has plans to do a virtual dinner
party with some friends, where they will all
attempt to cook the same meal over a video call.
To find a fun recipe, Remy has been browsing YouTube
for ideas and inspiration.
He really likes this homemade pizza
and checks the video description for the recipe, where
he finds a link prompting him to buy the ingredients
with Groceries For You.
This video happens to be a co-promotion with a retailer.
And that link acts as an entry point for Remy
to do his grocery shopping online, making it easy for Remy
to shop for the ingredients he will need.
In the Groceries For You app, Remy
is brought directly to the recipe card
for the pizza he was looking at.
Since he may already have some of the ingredients,
he's offered an easy way to select what he needs and then
add everything to his cart.
In the cart, he's given the opportunity
to make modifications and add additional items based
on personalized recommendations powered by machine learning.
For example, he can select a different mozzarella
from a brand he prefers.
He can add related items and even add
some of his go-to favorites.
This experience was powered by Google Cloud Recommendations
AI.
The retailer was able to offer suggestions in several ways,
first by looking at similar items
and providing comparable products,
next by looking at the customer's cart
and launching real-time recommendations based
on selected items, and lastly, by using historical user
data for predictive replenishment suggestions.
With Recommendations AI, retailers
don't need to build their own machine learning models.
Instead they can plug their product catalog and user
transaction data into existing recommendations AI models
and be up and running in no time.
Early results from retailers using Recommendations AI
show success, with more than a 40% increase in conversion
driven by recommendations, leading to more than a
50% increase in revenue driven by recommendations.
Remy is ready to check out and selects
a window for curbside pickup so that he can minimize
his exposure to other people.
He uses G Pay, where his payment information is already
securely stored and avoids having
to type out his credit card information
on that mobile keyboard.
When he's ready to pickup his order,
Remy can easily navigate to the store
and even share his location with the retailer
to expedite the pickup process.
Each of these pieces offered a small improvement
that together created a seamless journey for the customer.
Later on, Remy is wondering what to do if an item was
out of stock for his order.
The app offers a chat functionality.
So Remy asks about the safety precautions the store has,
in case he needs to go inside.
The chat bot is able to answer Remy's question
and explains the safety measures the store has put in place.
This helps Remy feel better.
But he's hoping he won't need to go inside.
So he asks the chat bot what the process is for substituting
items in his order.
Again, the chat bot is is able to answer Remy's question.
And Remy is reassured that his order will be complete,
since he has not received a message indicating otherwise.
This integrated digital shopping assistant
is powered by Dialogflow, the same technology that
supports Google Assistant.
Dialogflow can be used to easily train
a chat bot to answer common questions
or even provide item details based on your product catalog.
At the store, the retailer has mechanisms in place
to properly receive, execute, and complete Remy's order.
A grocery agent receives the order
and collects all of the items, with guidance on specific aisle
and shelf locations, handling instructions, and even
the ability to scan on the go.
Meanwhile, the store manager can see data and metrics
for the entire store, including information on gross sales
or the overall number of orders received and completed
throughout the day.
By combining data from various sources like this,
the store manager can make informed decisions
for things like scheduling or inventory,
leading to more satisfied customers,
lower wait times, and fewer out-of-stock alerts.
With this assisted grocery experience,
Remy was able to go from watching a YouTube
video of a recipe that he liked to picking up
his groceries in a seamless shopping experience.
By bringing together consumer Google products,
like YouTube maps and G Pay, and leveraging Google Cloud
solutions like Recommendations AI and Contact Center AI,
we hope to help your business create positive and profitable
user experiences.
To learn more about Google Cloud's retail solutions,
check out cloud.google.com /solutions/retail.
[MUSIC FADING OUT]