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>> Welcome, everyone, to this CUBE Conversation
here in Palo Alto, California.
I'm John Furrier, host of the CUBE.
We're here to talk about the state of MarTech and AI.
We're here with the co-founder
and head of AI for Blueshift, Manyam Mallela.
Welcome to the CUBE, thanks for coming on.
>> Thank you, John.
Thank you for having me, excited to chat with you.
>> Blueshift is a company you've co-founded
with a couple other co-founders
and you guys have a stellar pedigree
going in data AI back before it was fashionable,
in the old days, Web 1.0, if you want to call it that.
So, you know,
we know what you guys have been doing in your careers.
Now you got a company on the cutting edge,
solving problems for customers
as they transition from this
new, new way of doing things where users have
data and power and control,
customers are trying to be more authentic,
got walled gardens emerging everywhere
but that we're supposed to be away from walled gardens.
So there's a whole set of new patterns,
new expectations and new behaviors.
So all this is challenging, but yet it's an opportunity.
So I want to get into it.
What is your vision?
And what's your view on the MarTech today and AI,
and how do you guys fit into that, that story?
>> Yeah. Great question, John.
We are still in the very early innings of
where every digital experience is informed,
both creatively from the marketing side of our organization,
as well as the AI doing the heavy lifting under the herd
to be able to create those experience at scale.
And I think today every digital customer
and every user out there are leaving a trail of very rich,
very frequent interaction data
with their brands and organizations that they interact with.
You know, if you look at each of us,
many, many moments and hours of our digital lives
are with these interactions that we do
on screens and devices,
and that leaves a rich trail of data.
And brands that are winning,
brands that we want to interact with more,
have user privacy and user safety at the center of it.
And then they build that authentic connection from there on.
And, you know, just like when we log into
our favorite streaming shows or streaming applications,
we want to see things that are relevant to us.
They, in some sense, knowing kind of intimately
our preferences or changing taste.
And how does a brand or organization react to that
but still make room for that authentic connection?
>> It's an awesome opportunity.
And it's a lot of challenges,
and it's just starting, I totally agree.
Let me ask you a question, Manyam, if you don't mind.
How did you guys come up with Blueshift?
I know you guys have been in this game
before it was fashionable, so to speak,
but you know, solving Web 1.0, 2.0 problems.
And then, you know, Walmart Labs,
everyone knows the history of Walmart
and how fast they were with inventory
and how they used data.
You have that kind of trajectory.
When you saw this opportunity,
was it like the team was saying,
wow, look at this, it's right in our wheelhouse,
or, how did you guys get here,
and then how did it all come together?
>> Yeah, thanks for offering me an opportunity
to share our personal journey.
You know, I think prior to starting Blueshift
with my co-founders,
who I worked with for almost the past 20 years of my life,
we were at a company called Kosmix,
which was a Silicon Valley, early AI pioneer.
We were doing semantics search, and in 2011,
Walmart started their Silicon Valley innovation hub,
Walmart Labs, with the acquisition of Kosmix.
And, you know, we went into Walmart Labs,
and until then they were already an e-commerce leader.
They had been practicing e-commerce for
better part of 12 years prior to that,
but they're certainly you know, behind,
compared to their peers, right?
And the peers to be named!
(laughs)
But, they saw this lack of what it is
that they were doing so well in brick and mortar
that they're not able to fully get there
on the digital side.
And, you know, this was almost a decade ago.
And when they brought in our team
with a lot of AI and data systems at scale,
building things at the cutting edge, you know,
we went into it a little bit naively,
thinking, you know, hey, we are going to
solve this problem for Walmart scale in three months.
(laughs)
But it took us three years to build those
systems of engagement.
Despite Walmart having an enormous amount of resources
being the number one retailer in the world and
the data and the resource at their disposal,
we had to rethink a lot of assumptions
and the trends that were converging were, you know,
uses for interacting with them
across multiple formats and channels.
And both offline and online,
the velocity and complexity of the data was increasing.
All the marketing and merchandising teams said
even a millisecond delay for me is unconscionable.
And how do you get fresh data
and activated at the moment of experience,
without delay, this significant challenge at scale?
And that's what we solve for our organizations.
>> It really is the data problem.
It's a scale problem. It's all that.
And then having the software
to have that AI predictive and, you know,
it's omnichannel when you think about it,
in that retail and that brick and mortar term
used for physical space and digital converging.
And we saw the pandemic pull forward this same dynamic
where events and group behaviors
and just interactions were all converging.
So this line between physical and digital is now blurred,
completely blended,
the line between customer experience and marketing
has been erased,
and you guys are the center of this.
What does it mean for the customer?
Because the customers out there,
your customers, or potential customers.
They got problems to solved.
They're going all digital cloud-native applications,
the digital transformation.
This is the new normal,
and some are on it, are starting it,
some are way behind.
What are they-
What's the situation with the customers?
>> Yeah, that's certainly the maturity of, you know,
the, each brand and organization along that, you know,
both transformation and
from transformation to actually thriving in that ecosystem.
And how do we actually win, you know,
share of mind and then share of, like,
that market that they're looking to does take a while.
And, and many are, you know,
kind of midway through their journey.
I think, there was, initially there is a lot of, you know,
push towards let's collect all the data that we can
but then, you know, how does the actually data
becomes something useful that changes experience
for Manyam versus John is really that critical moment.
And that moment is when, you know,
a lot of things come into place.
And if I look at, like, the broader landscape,
there are certainly lines of powers like Discovery,
like Udacity and LendingTree, and Zumper car pods
across all these industries.
Who would've thought like, you know,
all these industries who you would not think of actually
as solving a digital engagement problem
are now saying that's the key to our success and our growth.
>> Yeah. It's absolutely the number one problem.
This is the number one opportunity for all businesses,
not just verticals here and there, all verticals.
So walk me through your typical customer scenario.
You know, what are the challenges that they face?
You're in the middle of it, you're solving these problems,
what are their challenges that they face
and how do you guys solve them?
>> Absolutely. So I'll talk through two examples,
one from a finance industry,
one from online learning, you know, o
One of our great customers that we partner with
is LendingTree.
They offer tens of millions of customers'
finance products that span from home loans,
students loans, auto loans, credits, all of that.
And, and let these people come
into their website and collect information that is relevant
to the loan that they're considering, but engage them
in a way for the next period of time.
So if you typically think about engagement,
it's not just a one interaction,
usually that follows a series of steps
an organization has to take
to be able to explain all their offerings
in a way that is digestible and relevant
and personalized to each of those millions of customers
and actually have them through the funnel
and measure it and report on it
and make sure that that is the most relevant to them.
So in a finance setting that is about
consuming credit products, consuming loan products,
consuming reporting products in an online context.
I'll give you an example of one of our customers, Udacity.
Imagine you are a marketing team of two people,
and you are in challenged with,
how do you engage 20 million students.
You're not going to write 20 million communications
that are different for each of those students, certainly.
I think you need a system to say
what did actually all these students come for?
How do I learn what they want at this moment in time?
What do they want next?
If they actually finished something that they
started two months ago,
would they be eligible for the right course?
Maybe today we are talking about self-driving cars.
That's the course that I should bring in front of them.
And that's only a small segment of the students
but someone else maybe on the media and the production side.
How do I personalize the experience so that
every single step of the way for that student is, you know,
created and delivered at scale?
And that's kind of the problem that we solve for our brands,
which is they have these millions of touchpoint that are,
that they have, how do they bring all their data,
very fresh and activated at the moment of action?
>> So you guys are creating the 10x marketer.
I mean, kind of-
>> That's right. That's a very (indistinct)-
>> 10X engineer, the famous, you're 10X engineer.
>> Right.
>> You guys are bringing a lot of heavy lifting
to short staffs or folks that don't have a data science team
or data engineering team.
You're kind of bringing that 10x marketing capability.
>> Absolutely. I think that's a great way to put it.
I call it the mission impossible, which is, you know,
you're signing up for the mission impossible,
for every marketing team, it's like,
now they're like, they are the product managers
they're the data scientists, they're the analysts.
They are the creator, you know, author,
all of that combined into a role.
And now you're entrusted with this really massive challenge.
And how do you actually get there?
And it's that 10x marketer who are
embracing these technologies to get there.
>> Well, I'm looking forward to challenging though
because I can imagine you get a lot of skeptics out there.
I don't believe you.
It sounds too good to be true.
And I want to get to that in the next segment,
but I want to ask you about the state of MarTech
and AI specifically.
MarTech traditionally has been on Web 2.0 standards,
DNS, URLs.
It's the naming system of the internet.
It's the internet infrastructure. So-
>> Right.
what needs to change to make that scale higher?
Does, is there any new abstraction or
any kind of opportunities for doing things in just managing
you know, tokens that need to be translated?
It's hard to do cross to-
I mean, there's a lot of problems with Web 2.0 legacy
that kind of holds back the promise of
high availability of data,
privacy, AI, more machine learning, more exposure of data.
Can you share your vision on this next layer?
>> Absolutely.
Yeah, I think, you know, there's a lot of excitement
about what Web3 would bring us there in the very
early innings of that possibility.
But the challenge of, you know,
data that leads to authentic experience
still remains the same whichever metaverse
we might actually interact with a brand name, like,
you know, even if I go to a Nike store in the Metaverse,
I still need to understand what
that customer really prefers and
keep up with that customer as they change their preferences.
And AI is the key to be able to help a marketer.
I call it the, you know, our own group call it like IPA
you know, which is ingest all possible data, even
from Metaverse, you know, the protocols might change,
the formats might change, but then you have to
not only have a sense of what happened in the past.
I think there are more than enough tools
to know what happened.
There are only emerging tools to tell you what might happen.
How do I predict?
So ingest, predict, and then next step is activate.
Actually you had to do something with it.
How do I activate it, that the experience for you,
whether it's Web3 or Web2 changes,
and that IPA is kind of our own brew of,
you know, AI marketing that we are taking to market.
>> And that's the enablement piece,
so how does this relate to the customer's data?
You guys are storing all the data?
Are they coming in?
Is there a huge data lake involved?
Can I bring in third party data?
Does it have to be all be first party?
How is that platform-level enabling
this new form of customer engagement?
>> Absolutely.
There's a lot of heavy lifting
that the data systems that one has to
you know, bring to bear upon the problem,
data systems ranging from, you know,
distributed search, distributed indexing,
low latency systems,
data lakes that are built for high velocity,
AI machine learning,
training model inference, that validation pipeline.
And, you know, we certainly
leverage a lot of of data lake systems out there,
including many of the components that are, you know,
provided by our preferred partner,
AWS and open source tools.
And these data systems are certainly very complex to manage.
And for an organization that, with a, you know,
5 to 10 people team of marketers,
they're usually short staffed on the,
the amount of attention that
they get from rest of the organization.
And what we have made is that
you can ingest a lot more raw data.
We do the heavy lifting, but both data management,
identity resolution, segmentation, audience building,
predictions, recommendations,
and then give you also the delivery piece,
which is, can I actually send you something?
Can I put something in front of the user and measure it
and report on it and tell you that, this is the ROI?
How do, if all this would be for nothing,
if actually you go through all this and there's no real ROI.
And we have kind of, you know,
our own forester did a total economic impact study with us.
And they have found, they have found 781% ROI
for implementing Blueshift.
And it's a tremendous amount of ROI you get
once you are able to reorient your organizations
towards that.
>> You know, Manyam,
one of the problems of being a visionary
and a pioneer like you guys are, you're early a lot.
And so you must be scratching your head going,
oh, the hot buzzword these days is the semantic layer,
in Khan, you see snowflake and
a bunch of other people kind of pushing this semantic layer.
It's basically a data plane essentially for data, right?
>> Right.
>> And you guys have done that.
Been there, done that, but now that's in play,
you guys have this.
>> That's right.
>> You've got all this semantic search built
in into the system,
all this in data ingestion, it's a full platform.
And so I need to ask you how you see this vectoring
into the future state of customer engagement.
Where, where do you see this intersecting
with the organizations you're trying to bring this to?
Are they putting more investment in, are they pulling back?
Are they, where are, where are they
and where are you guys relative to this, this technology?
And, and, and, and first of all
let's get your reaction to this semantic layer first.
>> Right, right. It's a fantastic,
you know, as a technologist, I love, you know,
kind of the ontology and semantic differences, you know,
how, how, you know,
data planes, data meshes, data fabrics are put together.
And, you know, I saw this, you know,
kind of a dichotomy between CIO org and CMO org, right?
The CO says like, you know,
I have the best data plane, the data mesh, the data fabric.
And the CMO says like,
but I'm actually trying to accomplish something
for this campaign.
And they're like, oh, that,
does it actually connect the both of pieces?
>> So I think, the-
>> Yeah?
>> The CMO org certainly will need
purpose-built applications, on top of the data fabric,
on top of the data lakes, on top of the data measures,
to be able to help marketing teams
both technical and semi-technical
to be able to accomplish that.
>> Yeah. And then, and the new personas they want turnkey,
they want to have it self-service.
Again, the 10x marketer is someone with a small staff
that can do the staff of hundred people, right?
>> That's absolutely- >> So that's where it's going.
And this is, this i6s the new normal.
>> So, we call them AI marketers.
And I think it's a,
it's like you're calling a 10x marketer.
I think, you know, over time we didn't have, you know
this word, business intelligence analyst,
but then once the tool are there, then they become
business intelligence analysts.
I think likewise, once these tools are available
then we'll have AI marketers out in the market.
>> Well, Manyam, I'd love to do a full, like,
one-hour podcast with you.
You can go for a long time with these topics
given what you guys are working on,
how relevant it is, how cool it is right now,
and with what you guys have as a team and solution.
I really appreciate you coming
on the CUBE to chat.
For the last minute we have here,
give a quick plug for the company,
what you guys are up to, size, funding, revenues,
what you're looking for.
What should people pay attention to? Give the plug.
>> Yeah.
Yeah, we are a global team, spanning, you know,
multiple time zones.
You know, we have raised $65 million to date
to build out our vision and, you know,
over the last eight years of our funding,
we have served hundreds of customers and continuing to,
you know, take on more.
I think, you know, our hope is that over time,
the next 10,000 organizations see this
as a very much an approachable, you know,
problem to solve for themselves,
which I think is where we are.
AI marketing is real doable, proven ROI.
Can we get the next 10,000 customers to embrace that?
>> You know, as we always used to say
in the kind of web business and search,
it's the contextual and the behavioral,
you got to bring 'em together here.
You got all that technology for the, for the
sites and applications for the behavior
and converting that contextually into value.
Really compelling solution.
Thanks for sharing your insight.
>> Yeah. Thank you John, really appreciate this.
>> Okay, this is CUBE Conversation.
I'm John Furrier here in Palo Alto.
Thanks for watching.
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