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AMY ESCHLIMAN: I think there are several consumer behavior
trends that are really important for retailers to pay attention
to.
And the pandemic really accelerated so many
of these trends.
One example of this is omnichannel experiences.
Today, services like buy online, pickup in store,
or curbside delivery are table stakes.
And they really accelerated during the pandemic
when consumers were worried about safety and convenience,
I would say.
SACHIN PADWAL: Retail has become part of every person's journey
throughout, and it's not a linear experience anymore.
And I think that trend has only been
accelerated by COVID, where we are shopping all the time.
And we are shopping using different devices
from my Google Home to my app on my phone
to watching a TikTok video.
I'm continuously shopping.
And I'm being influenced by brands and retailers
on the product.
And the point of purchase can happen across devices.
KAMELIA ARYAFAR: If you think about it,
historically, people used to go into stores or to a mall
and to a bazaar and just look around and try
to find what they're really looking for
or they're inspired by.
And when you think about the online shopping experience,
it's just vastly different because the inventory
is so much bigger.
There are just millions of items out there.
So how do you really make the different shopping experiences
discoverable for the user so they can actually
go in and really find what they're looking for
and what they're inspired by?
AMY ESCHLIMAN: I think the trends that will stick around
in the future are those that are making customers' lives easier.
So if you think about that convenience, factor,
that thing that makes my life easier, post-pandemic,
I might continue to use curbside if I am hurrying home from work
and I need to pick up some groceries.
SACHIN PADWAL: Social commerce is going to be a big part.
As I watch that TikTok video, and I
like a particular brand of sneakers,
I want to know more about that.
Social commerce is going to come up.
Visual commerce is going to be another aspect of,
I like something, I take a picture of it,
and I shop for it.
I think all of these aspects of discovery are going to prop up.
And increasingly, when you think about discovery,
it's going to be beyond what happens with search.
It's going to be beyond with what
happens to the browse category navigation.
It's going to be on all aspects of our customer journey,
whether it's in-store, online, or while consuming
entertainment on TVs.
BELWADI SRIKANTH: I think thinking of e-commerce product
discovery as an infrastructure problem is probably
the biggest pitfall that I see in the retail industry.
The second is the lack of meaningful measurements.
Even if one invests in machine learning-based systems.
Because focusing on only downstream metrics
like conversion rates or average order
values or things like cart abandonment
essentially misses the whole point.
One must focus on, in my view, more important
top-of-the-funnel metrics like search abandonment,
for example.
Because people could be giving up
just because they can't find what
they're looking for among millions of products
that a retailer sells.
AMY ESCHLIMAN: Product discovery should be ubiquitous.
It's not just a search bar.
It's actually an entire experience.
It's your search function.
It's recommendations.
It's conversations with store associates.
It's conversations with chat bots.
Everything can really be a product discovery experience.
And so thinking about that holistically
in how to deliver the best experience to customers
is really important.
I think another common mistake I see in product discovery,
and search in particular, is kind of thinking about it,
wanting to control a bad experience.
So if you aren't happy with the search results
that you're getting, you tend to start to put on new rules.
And you start to add on other rules.
And so all in that attempt to optimize
to what you think it should be or what your brand
partners think it should be ten to kind of pile on rule
after rule after rule.
And the results start to get really convoluted.
SACHIN PADWAL: Three things that I've
seen that retailers can really figure out
as they're thinking about the pitfalls of discovery.
Firstly, I feel retailers still think about journey
as silo journeys.
They're thinking about, this is my mobile experience,
and this is what my app experience looks like.
This is my store experience.
This is my in-store app experience.
And this is my website experience.
And increasingly, what's happening
is that experience is the same, just the devices are different.
And while I do feel that we need to bring
one consistent experience, which is really
adapted for that particular device consumption,
and we need to stop thinking in silos.
AMY ESCHLIMAN: And can you start to remove rules to let machine
learning do the work for you.
Can you think about your objectives
and what you're trying to achieve and really start
cleaning up the conflicts based on that?
KAMELIA ARYAFAR: A lot of times--
I think this is not just in retail,
but across the industry--
we focus on building some fancy machine learning model
and putting it in production and expecting to see results really
quickly.
And that's just not how science works.
So we want to build the patience,
the expectation-setting and the experimentation culture
to really get the algorithms, the machine learning that
goes into product discovery, to go from 0 to 100, hopefully
with that culture.
SACHIN PADWAL: Remove the paradigm
that one size fits all.
Go back to the paradigm that each person is unique.
Their needs are unique.
And start thinking about how do you bring that personalized
experience to a person of one.
KAMELIA ARYAFAR: If you think about a shopping experience
that you have had, any of you, I'm
sure you have been frustrated by searching different things
or browsing for different things.
And you just know that retailer has that item.
You have seen it somewhere.
Maybe you have seen it in the store.
Maybe your friend bought it.
Maybe you've got a promotional email for it.
But you can't find it on the website.
And then you're going to bounce and leave.
And yes, that's going to be an impact to their revenue.
But then there's that longer-term impact,
which is the impression you have from that retailer that
comes in the form of your customer lifetime value.
AMY ESCHLIMAN: Google Cloud is working
to reduce the issue of search abandonment
with the recent introduction of Google Retail Search.
We're very excited about this introduction.
It brings retailers the ability to greatly improve their search
results.
So Google's search platform leverages our understanding
of shoppers' intent.
So what do you mean by face mask?
Do you mean the face mask that goes
on your face for a beauty treatment
or do you mean an N95 mask.
And those have very different contexts and different intents.
That combined with the historical behavior
of the retailer's own customers on their site
and their app and the ability of the platform
to be configured to what the retailer is looking
for in terms of optimizing results
makes it a really effective way of reducing search abandonment.
The advice I would give retailers
that haven't yet invested in AI and ML is, don't be afraid.
I think it can be really intimidating.
But look for use cases that drive value, where
you know that the effort you put into your AI and ML solutions
is going to pay off.
The other thing I would say is look
to partners and independent software vendors to help you.
There are a lot of great AI and ML solutions that are more
out-of-the-box and can really help accelerate your efforts
if you are starting from zero.
SACHIN PADWAL: With the advent of AI and ML
and with the technology that Google
has built with its knowledge graph,
you build learning systems.
You've got to start thinking about how can I
start learning from every user interaction that is out there.
And that learning is powered by AI and ML.
And it's fed into your algorithms each time.
We need to move away from rule-based system which
creates one consistent experience for all the cohorts
and really leverage the power of AI and ML
to build personalized search results and personalized
customer experiences which are only possible if you use
the power of AI and ML to enable all
of your search and recommendations and discovery
suites.
BELWADI SRIKANTH: Another advice I
would have for our retail partners
is to optimize for the entire journey,
not just for transactions.
Context is everything.
Make it a personalized experience
by understanding user context.
Journey can sometimes last for months.
It's not a single transaction.
If someone is building or remodeling their kitchen,
their experience with the store could actually last for months.
AMY ESCHLIMAN: I think the idea of bringing brand experiences
outside a company's four walls and their digital properties
is going to meet with some really interesting innovation
and changes in customer behavior.
And I'm excited to see what happens there.
The second area that I'm really excited about--
and I've been working at this problem for a long time
in my retail career--
is thinking more holistically about optimizing
and personalizing digital experiences
for consumers across all marketing
and experiential channels.
BELWADI SRIKANTH: I think we are at an inflection point.
On the consumer side, we know there's
a dramatic shift towards omnichannel commerce,
with digital transactions increasing every day.
But then the consumers have the same expectations
of personalized service that you get in the store.
Right.
Under the technology side, the advancements
in ML technologies, be it natural language understanding
or vision AI, are now accessible to the entire industry.
It's no longer limited to large tech companies.
And this combination enables true consumer choice.
And the new innovations that technology now makes possible
is even more exciting.
Right.
For example, we're already seeing
some of these where users find the t-shirts and color that
fits them best without even going to physical store
or a cabinet that looks great in your kitchen at all
from the comfort of your homes.
KAMELIA ARYAFAR: I think we're just scratching the surface
in terms of really getting state-of-the-art AI and machine
learning into retail offerings.
And I'm for one really excited to see
how that can change the whole retail landscape in the future.
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