CARRIE THARP: Hi.
Welcome to our session on driving business transformation
in retail using AI.
I'm Carrie Tharp, and I lead up retail for Google Cloud.
I'm excited to be here today to chat
a little bit about the state of AI in retail
and how Google Cloud is helping implement AI in businesses,
like yours.
We'll also share a little bit about our solutions
and then share some best practices.
When you think about what's going on in retail today,
the landscape is certainly changing.
The pace of change is accelerating
and COVID-19 has certainly thrown a wrench in many plans.
What's more important than ever is
the use of data in your business to help unlock value,
whether that's changing customer experiences
or improving operational performance.
When you think about AI and ML in your business,
it's a crucial component to the future of retail strategies.
And choices that you may be making today on your platforms
and capabilities can be a big part of your performance
tomorrow.
We're very passionate about driving the adoption of AI
and ML in retail.
Research shows that there's certainly
value to using AI and ML in your business.
It can help you make more data driven decisions,
five times faster decision making, and three times faster
to execution.
However, AI is really still seen in its hype cycle, for retail.
We did some recent research in the specialty and department
store space and found that 60% of retailers
had 20 or more pilots in progress.
But when you double clicked on that, only 45% of those pilots
had been transition to full implementation in production.
And then looking at those that had been implemented,
retailers only thought 40% of them
represented the high value use cases.
So it's great to have a mix of potentially low hanging fruit
opportunities.
But there is also still a big opportunity
to go after where the biggest value in retail is.
So what's holding people back?
In our discussions, we find that data availability
continues to be an issue.
This really covers a whole host of problems, everything
from data being siloed and not being
able to get at all the different combinations of customer
transaction product and supply chain data,
to having issues with data tagging and data modeling.
Second is technical talent.
Do you have enough resources on your team
from a technology standpoint, or the data and analytics team,
to really go after a lot of these more complicated use
cases that take more preparation of the data
and a deeper understanding of the business.
Last as financial constraints.
So we all have kind of scarce resources
when we think about the capital and expense we're
able to deploy in our businesses.
And so, do you have the right amount of resources
to go after the opportunities that you see ahead
of you, as a team?
With all the challenges of implementing AI in retail,
it's more important than ever to focus and prioritize.
We've done research to understand where the value is
locked up in retail and have found that the top 10 to 15 use
cases is actually where most of the value lies,
out of hundreds of use cases you could potentially
be thinking about working on.
Whether it's in food, drug, and [? mass ?] or specialty
and department store, you'll see, on this list,
that there's a couple of key themes around inventory,
customer experience, and store operations.
Google can join you in these conversations of prioritization
to help you understand what might be the best
opportunities for you to go after short term
and in the longer term.
How Google can help, from an ally perspective,
is in three key buckets.
First, we're building industry specific solutions
that are pre-built and ready-made to help accelerate
your digital transformation.
An example would be vision product search.
You can plug-in this API and allow your customers
to take pictures of product and automatically
find it within your catalog.
Second is using the building blocks
that we've already established.
One of the examples here is Dialogflow.
Rather than trying to take on natural language understanding
to create customer care chat bots or voice
interaction with your customers, you
can use Dialogflow as a jump start
to creating those experiences.
And third is then just using our platform
to custom build yourself.
There's a variety of use cases that you
may feel are very specific to your business
or don't leverage components that already exist.
When we think about these pre-built solutions,
we're focused in three key areas.
First, is helping drive digital and omni-channel growth.
So this is think of everything that's
a part of driving digital experience
and your e-commerce business.
Second is helping you become a data centric, customer
driven retailer.
This is all of the data platforms
and AI and ML that's needed to help
you drive new personalization, customized experiences,
and understand your product and inventory data better.
Third is operational performance improvement.
So this is helping you unlock operational value
inside the P&L, whether it's store operations
or reducing your costs in your infrastructure.
First, let's talk a little bit more
about driving digital and omni-channel revenue growth.
One of the key priorities here is helping
evolve product discovery.
When you think about e-commerce today,
it's based on very rigid structures
from site nav to using a search text box.
And customer shopping journey looks very different than that.
So we've been focused on providing
solutions that help you evolve that customer interaction.
All retailers are dealing with several key trends.
So first is the changing dynamics in shopping visits.
So COVID-19 has changed how much retailers have customers
visiting their stores, how big their basket
size is when they visit.
And so it's become more important than ever
to provide new digital discovery experiences.
Second, this environment has certainly
made customer experience a big priority.
Making sure that your customer has all their expectations
met, whether or not they can interact with an associate has
become critical.
And then personalization.
This has been a theme in retail for years.
But it's now very important to make
sure you're putting the right product in front of a customer,
wherever their journey may be taking place.
Our offerings are focused on reducing friction for shoppers.
First is vision product search.
This is the capability that I mentioned
that allows you to use visual images to search your catalog.
So think of a grocery store customer,
instead of building their lists by writing it down or searching
for it on your app, they could simply take pictures
and preload their shopping list.
Next is recommendations AI.
This is focused on providing the best
personalized recommendations for your shoppers
with a broad understanding of their journey.
And last is search for retail.
This is bringing the power of Google Search
directly to your e-commerce websites.
When we think about recommendations,
Google Cloud brings best in class recommendations based
on our experience in building recommendations
as a foundation for several of our products
that are serving billions of users worldwide.
Recommendations AI works in three key steps.
First, you seamlessly integrate your catalog and user events
data with Recommendations AI through many of the tools
you're already using, like merchant center, Google Tag
manager, Google Analytics 360, Google Cloud
Storage, and BigQuery, as well as other data sources,
in most cases, without having to write any code.
Once your data is in, with a few clicks,
you can choose your model type, define your objective,
and begin training your model.
In three to seven days, the initial model training
and tuning will conclude.
And you'll see a visual indicator in the dashboard
that the model is ready to query.
You can then begin serving recommendations
to your customers.
You can now also visually preview the recommendations
being generated by the model before you serve them
in production to make sure that your setup has
been satisfactory.
The recommendations are then constantly
improving and optimizing with daily AI model refreshes.
Sephora is a customer that's using our Recommendations AI.
Beauty is certainly a space that recommendations
can be very important to that purchase journey.
And they've seen an increase in both their click
through rates, as well as their conversion.
Next, let's talk a little bit about being
a customer-centric data driven retailer.
How can you use data to effectively predict
customer needs and what your demand might look like?
Demand forecasting is an example of an area
that is critical to retail.
All the retailers belabor their forecast,
trying to make sure that they don't miss out
on revenue opportunities, but also
make sure that they don't have excess products sitting around.
This is a product that we have that's
in our early access program.
We took on this space because demand forecasting is still
a challenge for many retailers today.
Retailers often have trouble integrating new signals
and insights into their forecasts.
They're often based on static views of what
happened last year and maybe some more advanced
statistical modeling.
But most retailers see that there's
more opportunity in their forecast versus where
they're sitting today.
There's also challenges understanding
new products and new categories, not
having enough information to really understand
what the demand for that product could be,
maybe making those decisions based on intuition, gut,
or similar product performance.
And then last, many forecasting models
don't really understand local demand impact of things
like weather, consumer behavior, and even now,
shelter in place orders.
Our Google demand forecasting leverages our best
in class AI and ML and data sets from across Google.
It's designed to integrate with your current systems and does
not require [? written ?] [? replace ?] of platforms that
your merchants and planners may be using today.
Explainable AI then helps you better post
forecast review, where you're getting performance,
and what's really a signal inside your forecast.
As of today, we've developed ML algorithms
for demand forecasting that are driving more accurate forecasts
for customers.
The solution allows retailers to model future demand
for both long horizon order planning, as well
as short horizon use cases, to feed
into allocation, replenishment, and planning systems.
We are now building out the end to end solution
and partnering with a handful of retailers in the early access
program to take this solution from development to launch.
In addition to this product type solution,
we are also working with retailers
who are investing in their own efforts,
in this space, using our AI and ML platform offerings.
And finally, we also work with an ecosystem
of partners in this space.
System integrators like Accenture and Deloitte
and ISVs, like 09, who can help you get results quickly
and realize value from your data with integrated and out
of the box solutions.
One example of a retailer that we're
working with in this space is Carrefour, one of the largest
grocery retailers in France.
We've partnered with them across a variety
of facets of their business.
But one very exciting area is they've
[? stood up ?] an artificial intelligence lab in Paris.
This is a place that their Carrefour engineers partner
closely with our Google experts to work
on solutions like optimizing their product assortment.
It's very important, as you guys know,
to have a deep understanding of what's
most relevant at an individual store location.
And so using our recommendations and AI capabilities
to understand that assortment tailored to the local store
level has been a great value to Carrefour.
Next, let's talk a little bit about driving
operational excellence.
Providing the best in class customer care
and support through your call centers and chats has always
been important, but has become ever more important
in the times of COVID-19, as call centers
have been overwhelmed with all sorts of questions from,
do you have certain inventory in stock?
And what are your special shopping hours?
Like all organizations, retailers
are facing a revolution in customer care.
33% of retail customers will consider
switching to a different retailer,
if they've had a bad experience.
And estimates show that over the next several years,
AI will be a large component of providing service
to customers in call centers.
So it's important to be thinking about how
you can leverage artificial intelligence
and make impact in your business.
Google Cloud's context center AI solution
is built for this new world, based
around conversational core that understands human interaction
and incorporates the context.
At its core, this virtual agent AI
handles incoming calls or messages
and engages in natural human-like dialogue
to handle common use cases and flows, including
having to go off script.
When the call can't be handled automatically,
it's passed to a human agent.
And the Agent Assist AI steps into support
with the relevant content from the knowledge base and context
about the user and their situation.
The insights AI then surfaces trends and analysis
to help the entire system improve.
For retailers, the fact that it's centralized
means you can apply consistent intelligence
across every application in the customer journey.
The biggest cost savings can often
be found from deflecting voice calls into your contact center.
But there's been challenges in the past
because conversations can meander
and [? IVR ?] trees may not be able to adapt.
The inability to flex to human conversation style
has also historically limited the ability
to assist agents in real time.
Contact Center AI has the ability to go off script,
so you can work outside the predefined flow,
answering complex questions, full of unexpected stops
and starts, word choices, or implied meanings.
Contact Center AI also has the ability
to handle multiple use cases for your customers,
like taking a payment, updating information, like a phone
number, giving them information about store hours, which then
allows you to fully automate routine tasks
and further deflect calls.
You can start with an easy use case
and grow your adoption depending on your organization's needs.
During COVID-19, for example, many retailers
were faced with a deluge of customer inquiries
specific to store timing and special hours.
Our team offered up a rapid response virtual agent
that could be set up in days and take the volume of calls
off your busy agents, and most importantly, make sure
that customers felt supported through this period
of heightened anxiety.
Marks & Spencer is one of the customers
we worked with in the contact center space.
They work with our Partner Develop to leverage Dialogflow
to help manage their contact center interactions.
Marks & Spencer is one of the best beloved retail brands
in the UK with over 30 million loyal customers.
And they had switchboards in 13 different store locations
across their store footprint.
And they were taking nine million calls a year.
You can imagine that's a lot to understand the different things
that customers may be calling about,
specific to that store, but also broader questions that can
be answered more effectively.
Using Dialogflow, they were able to reduce that call volume
to the stores by 50% and route more than 7 million calls
into the centralized call center.
Now that we've talked more about our solutions and some
of the challenges that retailers have
using AI and ML in their business,
I'd like to share a little bit about some
of the best practices we see across all the customers we're
working with.
First is understanding your data for strategy,
making sure you're thinking about your data
in a holistic platform based approach that really expands
to meet all the needs you have going forward, unlocking
those data silos and bringing together the product
transaction, customer, and supply chain data.
Second is AI is truly a team sport.
Getting the value out of AI can often
sit at the intersection between several different stakeholders
in retail.
Whether it's the merchants, the planners, or the marketers,
they may have a very different view of where value sits
and how your brand should really take on using AI.
Your CEO is often your top advocate
to help bring the adoption of AI and ML in the business.
Third is thinking about how you can test for scale.
So given there are so many different use cases
you could go after, it's a best practice to use proof
of concepts to prove the value to your leadership team,
and then get kind of resources and more attention
to move forward with more use cases, going forward.
It can give executives and your stakeholders
a firm sense of what type of value they can expect
and how they can be interacting with data to drive value.
And then last is going mainstream.
So thinking about how you can better expose data and insights
to all of the users within your organization,
thinking about tools like Looker to help visualize and help
people understand what the data might be telling them.
This will speed your rate of change
and bring people along the journey much faster.
This is kind of taking data out of the rear view mirror
and putting it into something that
directs the future of your organization.
So now that we talked about solutions, best practices,
I'm excited to see what you can do to drive the adoption of AI
and ML in your business.
We have a variety of resources available,
if you're interested in learning more
about each of these solutions, customer reference examples,
to go a little bit deeper, as well as
partners that can help you on your journey.
I'm excited for what comes ahead in retail.
Data is at the center of all digital transformations.
And there's great things to come.