Welcome to Episode #209 of CXOTalk, and on
this CXOTalk, we are celebrating the holiday
season.
And how are we celebrating the holiday season?
We’re going to have a great conversation.
That’s our way of celebrating.
I’m Michael Krigsman, I am an industry analyst
and the host of CXOTalk, and CXOTalk brings
together people who are shaping the future;
the most innovative people in the world, for
in-depth conversation.
And you are part of that conversation, so
as we’re talking right now, there’s a
tweet chat taking place on Twitter, with the
hashtag #cxotalk.
And I’m so thrilled, because today, we’re
speaking on the topic of artificial intelligence
- AI - in marketing.
We’re here with Sameer Patel, who is an
old friend - I’ve known Sameer for years
- who is CEO of Kahuna Software, and his colleague
Andrew Eichenbaum, who is Kahuna’s lead
data scientist.
Gentlemen, how are you?
Fantastic!
How are you, Michael?
Doing great.
I am excellent.
And you know, I was thinking we should have
brought, I should have worn a Santa hat.
Yeah.
The Starbucks-branded Christmas cup here…
Alright, well that’s good.
So we have some Christmas cheer happening
here.
Mhmm.
So Sameer and Andrew, please.
Sameer, why don’t you tell us about Kahuna
Software.
Sure.
So, thanks for having us.
So, Kahuna software is a B2C marketing automation
provider.
We have built a real-time platform that allows
brands to be able to understand what the interests
and preferences of their consumers literally
within seconds, be able to make sense of it,
add it to the profile, and put meaningful
offers in front of them, be that for a commerce
vendor or if you’re a media company and
you need to engage your prospects, this is
the new way of using artificial intelligence
to engage with your consumers on the right
device at the right time.
It’s in California, we’re a four year
old company, we’re privileged to be funded
by Sequoia Capital, and SoftTech Ventures,
and Tenaya is our lead investor, and a whole
bunch of other amazing people supporting us.
And, we’re about 60 people, mostly in California,
some in New York and the others in Vancouver.
Andrew?
Hey Andrew, you’re Lead Data Scientist,
so what is it that you actually do?
What does a Lead Data Scientist do?
So, that actually is an interesting question,
because when I was looking around at places,
I joined at the Kahuna just about a quarter
ago.
I actually had not looked at marketing places.
I had actually done consulting work with large
companies; Intel, Intuit; with their marketing
groups, and suggested things like recommendations,
personalization, and they said, “This is
great,” well, everybody had sort of left
it at that point.
When I was contacted, this was the first company
that really thought of marketing as data,
and then what can we do with this data?
How can it better help us understand people
and better mail to them; better send to them;
know “when,” “where,” “how,” and
pretty much not spam them?
So, the data scientist sort of stuck in the
middle of making sure the data’s all good,
and then being able to figure out all of these
things.
Fantastic!
So, why don’t we kick this off with a discussion
of B2B, B2C marketing; your business to consumer
marketing?
But you’re selling to … Your end customers
are consumers, but you’re selling to businesses.
\
Yeah, I mean our end-customers are consumer
brands who engage with you and me as consumers
who buy stuff, right?
So one of the great things about taking over
this business is that it’s very easy to
keep yourself honest and know if you’re
getting useful stuff, because all of us are
consumers often, or any of the recipients
of what Kahuna does, right?
So, you know and in the B2C of the consumer
space, I think in the last four or five years,
we’ve seen probably the most tectonic shifts
in terms of how you and me as consumers want
to engage, and be engaged by brands, right?
And, you know, the specific area where Kahuna
plays is the area of retention, right?
So you might use ad technology and other ways
to drive new customer acquisition.
Where Kahuna comes in place is how do you
get existing customers to engage more to make
that first purchase go from first, to second,
to third, and really drive loyalty for [...]? So,
that is a very specific place where we end
up, where the majority of our folks and all
of our folks as a company.
You know, one of the most interesting things
that when we look at some of the convergence
and a need for consumer brands to begin to
rethink how they engage, and transact with
the consumers … If you look back at the
last four or five years and see the changes
that have happened in terms of how we expect
brands to work with us, you look at some of
the most common - and I apologize because
I overuse examples, but I think that should
matter.
You know, when you look at the notion of how
you procure a ride on Uber, for example: You
go from this point of them making an offer
to you consuming the service, and finishing
the service, and at no point during the whole
lifecycle did you ever think someone was marketing
to you.
You thought you won: you needed a taxi, they
provided supply at that point and location
where you needed it, you used it, you consumed
it, you moved on.
You know, when you, and Michael you know I’ve
talked about this, I love when you look at
Amazon for example, right?
And you see a wish list, and you see things
that Amazon might recommend to you, for getting
to a point where I don’t think - at least
I will speak for myself, I almost never find
what Amazon is recommending to be annoying.
I may not buy, but it super relevant.
And we’re getting to the space where if
offers can be as relevant and as timely as
they seem to be becoming, for some of the
more digitally native and proficient brands,
you know, we are willing to accept more and
more of what might otherwise have looked like
spam if it were not well-targeted, right?
So Kahuna is focused on taking that kind of
an experience to every single consumer brand
out there.
How do we get brands to truly be able to engage,
just at the right time, with the right offer,
on the right device, based on the digital
breadcrumbs that you and I today are leaving,
every single day, you know?
You’re on your mobile phone, you’re browsing
stuff; you’re putting stuff in wish lists;
you’re almost buying; you’re looking at
goods ten times.
We never had this kind of information as brands
to be able to truly understand what you are
interested in; how we put that to use in a
way that respects how a consumer wants to
work; and really, that is what gets us to
work every day.
We try to get better, and better, and better
at [...].
So Andrew, this notion is very interesting,
what Sameer just said, that when you are buying
a product, say from Uber, you don’t think
that they’re marketing to you, you don’t
interpret messages as spam, and so, is relevance
the key for consumers to have that kind of
open, warmth, and acceptingness of marketing
messages so they actually seek it, rather
than push it away?
So, let me take a step back to put this in
context.
To start off with what Sameer is saying, we’re
in this sort of new era, where you can market
to anybody, probably 14-16 hours a day.
People are that connect to their cell phone,
it’s always there, there are multiple channels
to reach out to them, and that’s all through
one device.
This is something which has become ubiquitous,
at least in the US market over the past sort
of five years.
Now that being said, it’s easy enough to
spam them, and nobody wants to do that because
people have become hypersensitive to the whole
situation of, “I’ve got one bad message,
okay, you’re in the cutting block.
Second bad message two days later, you’re
gone.”
And the reacquisition cost to pull somebody
back in is amazingly high.
So, the question is it’s not just not sending
them spam, it’s knowing what to send them,
when to send to them, how you send to them,
because there’s a whole range of things.
What message do you want to send to them?
And, it just extends out.
One of the most interesting things you could
think of is not to think about goals, like
I want to sell this person a product, I want
them to move the next step down my path.
We’re now in an area where we can think
about I want to increase my expected long
term value of all my customers.
I want to increase their overall engagement
stake, and this is what marketers can now
reach to.
This can be what seemed to be a more nebulous
goal before, is now something that we can
actually move forward and try and act on.
You know, Michael, one of the things that’s
interesting to just sort of note as we have
this discussion is try to understand the baseline
first, right?
We always will talk about better ways to do
stuff, but I always like to spend a little
time just talking about where does the market
sit today?
So, against this backdrop of this very sophisticated
consumer and their expectations that I had
just described, and that we talk about, it’s
really also important to say, “Okay, how
does the existing technologies in place stack
up to that very increasingly demanding customer,”
right?
And it’s pretty daunting when you start
to think about how marketing automation created
a decade ago stacks up to that, right?
The market’s over ten billion dollars in
size, yet there is over two hundred and eighty
billion dollars worth of goods left in abandoned
shopping carts every single year.
Two hundred eighty billion dollars, right?
That’s how much you and I go and we almost
buy, and we put it in the shopping cart, and
we leave it there, and you’re just effectively
nudging the consumer to the finish line, or
providing them with a handholding and the
information and the research that might be
required to persuade them to finish buying.
So, you’re left with almost … If you look
at some of the research that is out there
right now, the conversion rates are 2-3% on
all this expense on commerce.
That’s how bad it is.
When you say the conversion rates are 2-3%,
which conversion rate specifically?
E-commerce.
So when you look across e-commerce today,
all the investment being put into what seemed
like the right offers lead to 2-3% of conversion,
right?
And, you know, we’re now finally at a point
where you can begin to bring together both
the art and the science of marketing, given
the advancements in technology which is what
I’m going to talk about on the show later
today, to start to say how do I take those
meaningful messages; but that’s not enough,
I’ve got to find A) as meaningful as they
can be, B)
how they’re being put out at the right time
on the right device that we were saying earlier,
because the consumer is telling you, “Look,
I’m sharing my location with you on my phone,
I’m sharing with you things that I’m interested
in, this is not those days of email marketing
where you knew nothing about me and you spammed
me.
You better well be doing much better than
you are right now,” right?
And how does the backdrop where you start
to truly understand the difference between
the technology that 90% of consumers are using
today, and increasingly what the brands they’re
using today, and increasingly, the new persona
of this digitally-connected consumer.
Does that make sense?
Okay, so we’re in this situation where if
you can more accurately tailor the content,
the message, the timing, and the channel to
the consumer, to the online consumer, that
person will be more receptive, because “Oh
yeah, I’m getting something that I care
about.”
That’s the bottom line here, right?
That’s the bottom line!
I mean, it doesn’t have to be commerce only,
right?
This is deeper on all sorts of messages that
various online companies want to put in front
of you.
It could be about engaging for a show like
CXOTalk where you’re going after consumers.
It’s the same problem [...] too.
Seriously, right?
The thinking about how to put meaningful content
won, but secondly, did they hear you?
And this is why I love this business, because
we are all consumers when walk out the office
in the evening.
Okay.
So Andrew Eichenbaum, again, you’re a data
scientist, and artificial intelligence comes
into play here to solve this problem.
Tell us about that.
Artificial intelligence can solve many problems.
The question is can you define what you really
want?
You might ask the system if you tell your
data scientist to go off, and I want to increase
our revenues by X amount, and the data scientist
goes off and increases your revenues, and
then three weeks later, everybody goes away,
and nobody’s bothering to see your site
anymore and they’re asking “What happened,
data scientist?”
“I increased your revenues.
We never talked about lifetime value and things
like that, but you were able to increase your
revenue by 50%.”
[Laughter] It’s a double-edged sword.
“So I spammed everybody, I increased your
revenues like you told me, but you didn’t
tell me you wanted me to keep these people
coming back!”
That’s right.
So, it’s a double-edged sword.
Data science is sort of half science, half
black arts, much like the rest of marketing.
You have these fields and understandings of
what’s going on and what you think about
the consumer, and how you think they work.
The only difference with data science, is
data science is always asked to prove it with
numbers.
You know, looking back at data, and looking
forward.
But, the interesting thing is that all the
rest of marketing is following along those
lines too.
People are being asked to say it’s not just
hearts and minds, it’s what numbers … What
were the returns?
What was our ROI on the spend for these sets
of campaigns?
So on and so forth.
But how does the AI work?
Can you just dig in a little bit and make
that AI connection?
So, what do you mean by AI in this context?
What’s the kind of data that you look at?
How does this all …
Sure, yeah.
That’s a good question.
So, let me give an example: when a baby is
born, they don’t understand the idea that
something is hot.
So, it takes them a while: they’re standing,
they can see, say there’s a pot on the stove
on the burner, and it’s boiling away.
And they don’t know anything so they touch
it, and they burn themselves.
But, in coming closer and closer, you start
getting in those things, “Oh, that could
be hot.”
Or you start learning, you put your hand a
little closer, a little closer, a little closer,
and constantly getting warmer and warmer without
actually having to touch and hurt yourself.
It’s a feedback system.
Most of modern AI is exactly the same thing.
It’s what are called “supervised learning
systems”.
We have large amounts of historical data,
where we know the outcome.
So, we say we believe to get this outcome,
we should look at all of this data beforehand.
And so what we do is we have these programs,
which we say, “Here’s all of the inputs,
here’s what we want,” it’s either good
or bad by the definition, or however you want
to define your outcome, and then you pass
it through the system and it spits out something
like, “This is good.”
Well you know it was good, so you reinforce
and you say, “Oh, and it’s all happy.”
And you pass another thing to the system and
it says, “This is good,” it’s like,
“No, it was bad.”
You pretty much slap it and you say, “It
should really have been marked bad.”
And you do this over and over again.
So, it’s learning the exact same way someone
would learn that something is hot, or something
is cold.
You get a better understanding over time from
a whole range of input variables.
That’s a really important point, right?
Because I think there’s so much hype around
this topic right now, and one of the things
is … And when we talk to customers, this
is whether in the sales cycle or the customer
success programs, you have to start to have
a very open discussion with customers about
where the state of AI is today, promise them
that you’re kind of at the edge, or the
bleeding edge of it, but make no mistake,
it’s a constant process of refinement and
improvement and improvement, right?
And so, you know, maybe another way to also
build on what Andrew said in terms of where
the AI come in, there are a couple of places
where the AI comes in.
And one is, you know, what is the point of
all of this?
The point of all of this in plain English
is how do you begin to move from what I like
to call “lazy segmentation and coding,”
because the technology just has never been
sophisticated, so we kept putting random people
into buckets just to make ourselves feel good
that these segments actually matter; and starting
to move in a direction where you’re starting
to really engage and transact with an audience
of one.
Now, to do that, you’re going to have to
do … There are different places where AI
gets injected.
Maybe you can talk about it from the standpoint
of how do we do it from a [...] standpoint,
to understand how you cooperate with the end-user.
And maybe the second piece here, Michael,
that we’re talking about to where we .... And
this is again where … I’ve written a blog
post about the things I learned in the first
sixty days of coming here, and one of the
things I learned, coming from the outside
in, was the amount of peanut butter in AI
that happens in the industry, right?
And you have to also talk about the integrity
and the real-time access to data before you
apply any AI to it.
So one of the things that we do in Kahuna
is within five seconds of you making a gesture
within a mobile app on your phone, Kahuna
will record that [...] a profile; and within
thirty seconds we can put out an offer.
If you don’t have that level of immediacy,
no amount of peanut buttering … That’s
just putting AI on … It’s just making
your bad data smarter, in its own way, right?
So I don’t know, maybe you just want to
talk about those aspects of where in this
process you would specifically inject these
concepts to actually [...].
So let me touch to both of those points that
Sameer brought up.
First about the data: Data is good.
Data is great.
It’s sort of the centerpoint of data science.
But, if the data is junk, the data science
coming out will be junk as well, so a large
piece of any data scientist’s lie for the
team is making sure the data’s coming to
the system, is properly being stored, planned,
verified, so that when we finally get to have
fun and play with these advanced systems,
we can believe in the results coming out,
because if not, why bother doing it?
So, how is this different from traditional
marketing?
So again, from the techniques that you’re
bringing to bear with AI and machine learning,
how is it different from the way marketing,
digital marketing was handled up until this
point?
Yes, so let me give some direct experience.
So, I worked at a company called “MyBuys,”
it’s now part of Magnetic; this was about
eight-ten years ago; and we were providing
Amazon-like recommendations of service - various
midsize companies.
And, one of the things we did there, or I
did there, was to figure out what the optimum
time to send out to a client base, and that
being an entire consumer base, and you can
plot it out over time for the entire base
[...]. It’s best if we send out the last
email at 10AM on Tuesday morning.
And so, we backed out, starting a little beforehand
until then, and it was all good.
And then we were able to see significant lift.
But, now we have the ability to actually do
this on an individual basis.
We’ve seen an individual come in and respond
to messages, or not respond to messages for
various channels over the past couple of months.
And we know how they respond to what type
of message.
So, we’re no longer blasting out to the
entire group at a single time of the week.
We can actually set up our campaigns so that
an individual user will be sent out right
before the expected time or act, and in a
public channel.
So this is the real thing.
And you say, “Well, that’s a hell of a
lot,” when you’re literally giving the
orders of magnitude more data to do this.
And the other bit is that Moore’s Law has
helped us.
We have a huge amount more processing power,
and it’s not the limiting factor anymore.
What is the number?
Is that half a billion events today?
Yes, so Kahuna on its own process has half
a billion events today.
And, it’s literally linear scalable system
we can rerun in the cloud and simply add more
computerization as we get more data.
So I think there’s another way to also look
at this.
Michael, I know you’ve been a follower of
enterprise technology for so long, so if you
also start to look at the … If you look
at the predecessor to what Kahuna is today,
the predecessor to Kahuna were email delivery
systems that were built a decade ago.
And again, no disrespect to that model, that
was state of the art back then.
The way to not have to put a flyer in your
mailbox, email was a great alternative to
do that at scale for much of it, right?
So you totally [...].
We’ve now reached a point where the number
of engagement and touchpoints for us as consumers
has gone from one, which was email, to many.
And we haven’t even seen this play out yet,
right?
So today the dominant ones are email still,
and mobile; SMS.
We’re going to have beacons tomorrow, we’re
going to have IoT after that, we’re going
to have chatbots, right?
It’s going to go … The places where we
engage are going to increase.
The other amazing thing about where we are
right now is that every one of those engagement
touch points are going to start sending different
events to us, that again, email jsut never
sends back.
So if you do not have a system today that
can accept these signals from these different
engagement touchpoints, make sense of them,
add them to Michael’s profile, but still
have the luxury and the [...], if you will,
to say, “I may have learned something new
by Michael through a gesture on a mobile phone,
but the right way to engage with Michael based
on machine learning, is 7PM on Thursday nights,
via email, because that’s when he seems
to be on his laptop and he seems to want to
engage and buy stuff.”
You have to start as a marketeer, decouple
the smarts of the system, and where you want
to engage with people.
This is where the technology has fundamentally
shifted from what were truly nothing more
than email delivery machines.
You know, batch and blast, batch and blast,
batch and blast, right?
That allows marketeers to engage in a fundamentally
different way.
Andrew, can you drill down a little bit, and
give us some concrete examples to kind of
maybe walk us through how you look at the
data, in order to personalize to that level
of the individual that Sameer was just describing,
using AI?
Sure.
We can … Let me talk about a project we’re
working on right now, and it’s a question
of whether we should send an email or not.
So we set up this batch, you optimize everything,
each individual has a send time, but the question
is: Should we be interacting with them at
all?
You can ask yourself the question, do we expect
them to interact with this message, or do
we even need to send the message because we
expect them to interact with the site, the
media anyhow, so that the cost of this message
isn’t lost because there was no reason to.
I guess the best way to put this is we look
at their history.
We look at all the ways that they are interacting
with the site over the past roughly hundred
days.
We look at all the different messages that
they receive, how they interacted with those
messages, or had they not interacted with
those messages that we see, but that their
site usage has gone up.
There is a bunch of other things going in
there, probably a bit too much at this point;
it’s trying to understand the expectations
of using the site and whether people want
to pay the costs of the email, where they
say it’s not worth it at this point in time.
MIchael Krigsman: So you have this body of
historical data, [on] an individual, about
what they’ve done, their open rates on that
email, is that correct?
And then you’re looking at that historical
data and doing analysis on it?
So there’s two pieces though, right?
There’s the data that Kahuna collects, and
then because of how the system is architected,
if a customer - if it’s a commerce example,we
can even pull in point of sale data enriching
the profile, right?
So that’s again the benefit of having built
an API first-model of four years ago, and
not 20 years ago where the system [...].
So also, remember we’re not looking at just
email.
We’re looking at email, we’re looking
at push notifications, SMS, a whole range.
Because when that message was sent out, even
before I ask a question - the sort of last-minute
question - “Should we send the thing out
or not?”, we need to understand what’s
the best way to reach out to them.
And [after we]look up the message and the
time of day, and the requirements from the
marketeer, we might want to send out a push
notification, which is completely separate.
This comes back to a more interesting question
that even the other modern marketers aren’t
sort of attacking, in that this is really
a derivative problem.
It’s not just how many times have you opened
your emails and moved forwards?
How many push notifications like, “Ok, you
that your last two messages were push notifications
over this past two and a half weeks, and your
interaction for this, and a number of other
things, how should we message?”
There’s this whole encompassing profile
of where you are in the short-term, the mid-term,
and the long-term, and how that defines how
we should interact with you going forward.
So it’s not just a matter of counting up
times.
[Laughter]
You can do that, but you don’t get the lists
that you do.
You’re back to where we were five years
ago.
Okay.
So now, you’ve got this body of data, you’re
making these inferences essentially about
individual consumers, the type of content
they like, when they like to read it or receive
that content, and which channel, and so forth.
So we’ve got all of that, and we’re applying
these advanced AI and machine learning techniques
to it, what do we get as a result?
Why are we doing this?
You’re not spamming your consumer, is the
easiest result.
You’re telling them what they want to know
on their timescale.
My goal is not to market to people, it’s
to make suggestions to people.
If they never feel like they’re marketed
to, but we’re sending them messages, then
I feel that we’ve reached the ultimate goal.
We’ve influenced the people without them
ever thinking that they were being influenced.
They were being passed information that is
relevant to them at the point in time that
it would be most relevant to them.
And that’s a very good point.
I think one of the things that, you know,
Andrew very astutely said, is being … The
rhetoric right now, which is kind of disappointing;
and again, because I came from the outside
into this world, it’s one of the first things
I’ve picked up when I took the role as I
tried to understand how the industry was speaking.
And we kind of went from this “Let’s drop
the email, email’s dead.
It’s all about mobile,” and you get to
a point where none of us wants to get excessive
notifications on our phones.
That certainly doesn’t make it correct,
because it’s become [...], right?
So, this notion of being cross-channel at
your core, where you can actually engage with
people in a way that respects the pace at
which they want to go through this journey,
we’ve got brands, we’ve got big consumer
brands that have massive segments of their
customers, who say, “Look, the primary engagement
model needs to be email, and mobile once in
a while.”
That same customer has very lucrative segments
of people who want to buy from them that say,
“Absolutely not.
I need more mobile, some SMS.”
We have to let every brand decide what is
that right combination for every demographic
or user type that you have, and respect the
pace at which those consumers want to engage
with these brands and transact.
Sometimes, it helps the brand figure out,
“You know what, it’s going to take four,
or five, or six engagement touchpoints before
you get to a place where you can get to whatever
you define as your goal, be that conversion
or what-have-you.”
We can’t assume steep cliffs, nor can you
assume gradual rises.
You have to start to respect the pace at which
you run the most.
So these are some of the things we think really
hard about, and brands are starting to understand
that we can’t switch completely to the other
end, and just start hammering people with
mobile notifications, right?
It’s an interesting problem that I’m not
seeing everybody solve, but I feel like we
spend an adequate amount of time thinking
about it.
Do you agree with that?
Yes.
So, when you deliver information that is cued
again to the type of content, who it’s being
delivered to, tailored to, how they like to
receive it, when they like to receive it;
in a sense what’s happening is you are bypassing
the mental filters that the receiver sets
up to block out that we all have, to automatically
try to block out the meaningless barrage of
marketing noise, that we get all the time.
Yeah, and I mean you know, yeah.
Because I care about it.
I care about this.
I happen to be interested in things like cameras
and microphones, and plug-ins for audio, and
all that kind of stuff.
Of course you are!
And when they … and isn’t everybody?
And when I look at websites, and I see these
things, I don’t look at it and say, “That’s
creepy.
I hate that.”
I look at it and say, “Oh, that’s interesting.”
You know, yeah.
I think you nailed it, and I think this is
where…
You know, unfortunately as consumers, we have
normalized in some ways to a world of batch
and blast that was created ten and fifteen
years ago, where we get an adequate amount
of spam and it’s not until I think now,
in the last two or three years, where there’s
actually enough technology advancement for
us to sort of wake up and say, “Why are
we dealing with the second grade way of living
and getting hit, and getting spammed?
And why can’t every commerce retailer be
… Why can’t the recommendation engines
be as targeted as some of the big brands that
have been invented in the last four or five
years?”
And that to us is success, when we get customers
to begin to sort of …
Every e-commerce vendor, every media company,
every travel company, all of the industries
that we play heavily in; if we can get them
to that place, our job’s done.
For me, those banner ads are whatever pushes
or emails that you get are great, but I hate
them.
Like I just purchased this a day ago.
I don’t want to be seeing these.
These are now the most annoying ads I could
ever see.
I purchased it, why are you bothering me?
And being able to understand that sort of
level I think is really the next step for
most marketing out there, that even knowing
where your best intent is most likely spent.
And you know Michael, I think the other interesting
thing is: We always talk about, “The world
is change and it’s a new world, and it’s
a new consumer,” but there are many things
that have not changed.
Let’s be honest about that, right?
There are things that continue, like when
we talked about the conversion rates, two
or three percent e-commerce conversion rates,
this is a problem that every marketeer, and
frankly every CEO and consumer brand has been
waking up to for decades, right?
The amount of money they plow into customer
acquisition costs, to drive new customer acquisition,
has only gone up.
But the amount of focus and available technology,
once they’ve acquired that customer, to
get from that, from the point where you are
now a customer to the first purchase.
What is the actual work required to get you
from first purchase to second purchase?
We all know, and I think you would attest
to this, that as a consumer, forget all the
gobbeldygook technology for a second.
I would say, in my own buying pattern, if
I buy from a given mobile e-commerce app for
the third time, my mind starts to get training
around certain categories.
I associate that brand to certain categories.
And I would not go to a search engine.
If I’m looking at products within these
two or three categories, I will see if this
vendor actually has it.
And that is something we think really, really
deeply about.
And I don’t think the retention part of
marketing automation technology has advanced
ever to a point like it is now, where you
can begin to really, really say, “First
point of first goal, get the customer to download
the app; second goal, get them to make the
first purchase; third goal, get them to make
the second purchase.”
And having that level of discipline, we’re
now at a point where you can do that if you
use machine learning-based technologies, right?
So it’s going to change how we interact
in the next decade.
We’re just getting started.
Now, what about the flip-side of this, which
is the fact that you’re looking so closely
at consumer data; and yes, they’re my digital
tracks, and they’re not particularly secret,
I’m going to these websites; but, when you
start aggregating this kind of data, what
about the privacy implications?
So, this is a question that comes up in every
company that deals with marketing and AI.
They say, “Well now that we can draw these
correlations between all this data, what does
it mean?
Are we going to spoof our customers?”
I can tell you numerous stories.
And what’s really interesting is it’s
actually based upon the older the age range,
the more worried they are about security and
privacy.
Say their generic internet traffic.
Now there are other places like medical, and
banking, where people just don’t want to
share.
They’re extremely secretive.
And, these are where things like personally
identifiable information comes in, and making
sure that nobody can see these things easily
out on the web, or advertised to that specifically.
But if you look at …
Heck, just look at social media.
How much do people post, and they’re specifically
sharing it with the world.
Is it that much more than just viewing your
… I don’t view my web history trail as
any less personal than what I’m putting
up there: pictures of my kids, conversations
that might be going on…
People have become accustomed to a certain
amount of sharing, and they understand that
that sharing will be used to profile them
by multiple people.
What the smart companies are doing is they
are saying, “Yes, we’re going to do this,
and we’re going to make it better for them.”
You can see this on Google, on Facebook, it’s
like, “Is this ad valid to you?
Do you want us to advertise to this?”
They are up there saying, “We’re going
to profile you based upon your activity.
What pieces do you do not, and do want to
be marketed to?
How do you want us to use this information?”
So basically, opt-in, essentially.
I mean, yeah.
You’re leaving certain digital breadcrumbs
proactively, openly, right?
This is no more than, you know, what you have
agreed to leave open.
Now usually, also understand in our context,
because we sit on the retention side, you
know.
There is an association with a brand already,
you have the app, we’re helping you just
go through a more meaningful purchase with
that particular brand which you’ve always
had business with.
There’s no denying that this stuff can be
used in ways that are not kosher, right?
It really boils down to, you know, you having
the faith in the brand and the customer success
teams, and the people you work with day-in
and day-out to … because again, I think
every brand knows they’re recovering from
brand [...] that’s far more expensive than
the initiative use, right?
So, stay within the lines of what the consumer
has agreed to expose, and you’re okay.
So, maybe this is an important point, that
essentially what you’re saying is smart
brands will engage in behaviors that will
engender trust among consumers.
And you started to talk about this, but maybe
give us some advice to brands for what are
behaviors that will engender trust?
How do you engender brand confidence and trust?
So, one of the easiest ways that data science
has been viewed as important is explaining
why we do what we do.
Giving the user, if they ask, the reasons
why we’re marketing this to you.
And you see this on Netflix, if you go on
their streaming.
If you go down lower and lower in the recommendations,
they say “movies because you watch this-and-that,”
films based upon this genre that you seem
to like, based upon your viewing history.
Just give a brief explanation which seems
plausible and at the root of the reason, or
the way you were targeting them, and people
find it much easier to ingest, and understand
and trust those [recommendation].
I want to come at this from a different side
as well.
This is an indirect way, but I truly believe
that there is going to be more and more of
this over the next coming years.
One of the things that is least talked about
in this concept of trust between a brand and
a consumer.
So one is actually the things that we talked
about, there’s a level of “Will you screw
me over or not?”
There is definitely that side.
But there’s another discussion too, which
is I start to have more trust and faith and
respect for a brand, as a consumer, when I
see that they have taken the extra effort
to understand that.
You know, you’re going to … Do you think
Amazon breaks your trust when you see five
things that they suggest based on your interest?
No, I don’t think you - this is again, shrinking
that distance between what an offer is, what
valuable information is.
And I think as we start to do that in a meaningful,
respectful way, I’m not going on and telling
Michael Krigsman’s friends what Michael’s
interests are.
I’m sticking to Michael, and only Michael
gets to benefit from that.
You start to build a mutually respectful relationship
with a brand.
And I think that implicitly starts to go a
long way.
The problem has been the opposite, which is,
brands have had to try to get very cute with
the consumer because of really crappy, excuse
my French, the really, really bad, shitty
marketing technology.
And that time is done.
The consumer’s done, the state of the technology
has changed over like “Clap, clap!
Wake up!
We’re done with all that!”
It’s time to move on, right?
And I truly think that better relationship
with every single consumer by putting meaningful
stuff in front of them, and not screw them
over with the data that they give you.
That’s important, just to stay there.
So we’ve got about three minutes left.
And, how about for our closing, if each one
of you just offer your continuing thoughts
on what must a brand do to, in the immortal
words of Sameer Patel, not screw over their
consumers?
Andrew, you want to start with that one?
There are so many ways you can screw over
your consumer.
… Or to do the right thing.
What should brands do, to do the right thing?
That’s another way of saying it.
It’s not as much doing the right thing.
Marketers want to increase their market share,
and it’s inherent in the system.
The real piece is not to go above and beyond
to the point where people become distrustful
of you.
It’s really just building up what Sameer
said.
You can easily go off, given your consumer
information, and go to a third party, spend
seventy five cents a person, and literally
get their full history in there: their employment
history, where they’ve lived, so on and
so forth.
And you can use that and it becomes extremely
powerful.
But if you start doing that, and people realize
you do that, they’re going to go nuts.
You’re going to make the front page of the
news, and there goes your profits for the
next wherever.
And we hear about companies that make the
front page of the news because they do that
kind of stuff.
But if you keep it at the level that you’re
interacting with my site, you’re buying
stuff.
You are doing this and that, and from that,
we are moving you forward, you’re at a much
safer space.
Now, there are places like Target, which sometimes
overreach a little; if somebody gets a congratulations
on being pregnant, you might say, “What?”
[Laughter] Maybe because they haven’t told
anybody yet.
But the interesting thing is, you hear more
of those stories than the opposite.
So Sameer, you have the last word.
We have one minute left.
Yeah, look.
I think it’s really simple.
I think we are at a really critical, and an
amazing time in terms of revitalizing and
rethinking every piece of the enterprise technology
stack and B2C marketing automations, and all
different things.
Marketers, for the first time, have the ability
to start thinking about B2C marketing automation
as workflow automation technology, and truly
put the consumer in the center of the entire
experience process, and figure all the business
processes that actually emanate from it.
How do I want to engage with an audience of
one, Michael verus Andrew versus Sameer, and
what are the kinds of campaigns and things
I need to be running that have to follow that?
This is the opposite of how ten year old technology
was designed.
That was designed around scaling and batching
and blasting stuff out there, consumers are
way smarter than that.
Unfortunately, or fortunately for you, Netflix
and Amazon and AirBnB and all the amazing
digital brands have risen.
You know, just the tolerance level of short-e
batch-and-blast technology is just not going
to cut it anymore.
A very simple way to consider this is to say,
“Everything I do on a day-to-day basis:
Is the consumer at the center of the process,
or is the convenience of my marketing automation
just at the center of the process, and you
will immediately be able to figure out where
the gaps are in the technology.”
Okay.
What a great summary in the end: Is the consumer
at the center of our process, and our life
as a seller, as a marketer, or are we doing
things because…
For our own convenience, right?
You know …
Michael Krigsman:
For our convenience.
It’s your convenience.
It has to be about the consumer, and everything
has to surround that.
And on that note, this has been a very fast
conversation.
We’ve been talking with Sameer Patel, who
is the CEO of Kahuna, and Andrew Eichenbaum,
who is the lead data scientist at Kahuna Software.
I am Michael Krigsman.
You have been watching Episode #209 of CXOTalk,
talking about the role of AI, artificial intelligence
in marketing.
Next week, there’s no show because of the
New Year’s holiday, and everybody, thank
you so much for watching.
And Sameer Patel and Andrew Eichenbaum, thank
you so much.
Thank you for having us.
Thank you.
Everybody, have a great week.
Bye-bye.
