The Importance of Data in Content Marketing
Anthony Kennada sits down with Tim Sanders, VP of Research Insights at G2, to talk about why good data is the only sustainable competitive advantage in today's competitive landscape.
Well, Tim, first of all, congratulations on your new role at G2, VP of Research
Insights.
You know, G2 is sitting on such a treasure trove of data.
Last I pulled up here, 2.5 million verified reviews, 90 million annual visitors
who are
researching products on G2.com, and over 160,000 products and services are
listed.
I'm curious as you're thinking about approaching this role, can you just speak
to the power
of data and insights in original research as a function of a content marketing
program?
Well, to quote one of my favorite artists, Wu-Tang Clan,
data rules everything around me, right? So when you think about what's powering
artificial
intelligence, it is data. And this is especially true with the rise of gener
ative AI, right?
Because generative AI allows natural language, like what we're doing, to become
the interface,
to all of the world's data. However, the outputs are only as good as the data
is high quality
and irrelevant to the situation. The other thing I think about when I ponder
this concept of like,
how important is data? Because we say it's existential. It's the most important
thing,
but I'm going to put it in a different kind of light. So when you think about,
you know, technology today where AI is becoming always included more than artificial
intelligence,
when you think about it, right? What's happened is that for the first time in
human history,
technology, software, has decoupled prediction from judgment.
First time ever, in the past, prediction and judgment happened at the same time
in a human being called an expert. And they set up, based on my experience,
I think this is going to happen. So I prescribe this action. And oftentimes we
would integrate it
into a firm with a rule of thumb. And the problem with rule of thumb is that
they have
invisible expiration dates. You really don't know that the rule of thumb doesn
't work until you've
had your third quarter in a row of things falling off a cliff. And then you're
in a doom loop. So
what happens is if we let the machines make predictions based on high quality
data,
then the humans simply pass judgment. And there is an opportunity now to scale
personalized decisions for the enterprise, right? Every situation now you're
going after it with
code or codified solutions. So it creates an incredibly dynamic organization. I
think about
ant financial in China, it's a Harvard business case, fastest growing financial
services company
in the world. They got to 500 million users with literally 10% of the employee
base of Wells Fargo.
Wow. Because they had decoupled this thing. The analogy I would use here is
that when you think
about the data behind ways or Google Maps, it's profound. The quality and the
quantity of that data.
Why is that important? Well, I'll give you an example from a recent trip.
Which the United Kingdom, Love London, always loved my London cab experience.
There's always
been a shortage of London cabs. It created huge queue times during rush hour
and surge. And the
reason why is the industry thought they had a logistics problem. I've got to
buy more cabs and
I've got to recruit more drivers. No, the problem was they had a prediction
problem.
You had to go to school for almost four years. It's called the knowledge.
Before you could be a
cab driver for London cab because you needed to learn every possible route
scenario so you never
had to pull over and look at a map. Because writers hate that. And as a result,
it created
a huge crimp in the funnel. So we're like we've seen now with GoToMarket. And
then along comes
ways. And along comes Uber who thinks differently and says, "Really, all you
need is human judgment.
So let's put an app in their hands." And if they know not to honk and not to
tailgate,
not to have music on, not to take calls judgment, then they can be a great
driver. And now you've
got a thousand percent more drivers in the United Kingdom. Mobility has been
restored with
artificial intelligence. It's just invisible. So my message is data is your
only mode. For any
organization, there is no other sustainable competitive advantage. Well, let's
stay on that
topic of AI. Well, we're here. You referenced Harvard. You're recently
appointed executive fellow at
the Digital Data Design Institute at Harvard. And my understanding is this is a
function that
enables researchers to deliver insights that drive the adoption of AI more
broadly. And I'm
curious of just, you know, we're here at this Better Together conference
thinking about the
application of really we just referenced the mobility kind of exercise that
what ways is introduced.
How is AI going to shape these emerging use cases for the GoToMarket functions
within businesses as well?
So to pivot from D-cubed, that's what they call it at Harvard, D-cubes research
has been very
use case focused. They've also looked at governance. They've looked at AI
safety. I'm looking a lot
at packaging use case work they've done to kind of meet their charter, which is
to democratize
digital transformation and AI for all of business people. Right. So I think it
's a fantastic purpose.
So I get behind it. I'm the flavor of evangelist called explainer. So I'll talk
about that in a
minute. So I want to pivot to McKinsey research because this is really
interesting.
McKinsey did an analysis at the end of last year and it was on point to gen AI,
which is the fast
motion right now in AI. And what they looked at is they looked at what
functions in an organization
can you have the highest revenue impact first and second were sales and
marketing.
Right. Okay. Now, it doesn't have the highest take out of cost center, highest
take out of
cost center is customer operations. Doesn't have quite the revenue impact and
it's got a lot more
cost behind it up front. But in the long term, it could take out cost. But go
back to sales and
marketing. Incredibly high revenue opportunity, low hanging fruit for an
organization. Why is that?
Because when you think about prediction and then you think about automation
based on prediction,
letting the machine now take an action on your behalf or following the machine
's advice.
It's only as good as your tolerance for hallucinations. Sure. Right. So the
last thing we'll ever see
is autonomous driving. The first thing we saw was cadence improvement with
products,
right AI, writing assistants like Grammarly, right? Because we have a tolerance
for it being slightly
wrong because we can fix it right with the human in the loop. So sales and
marketing of all organizations
more than pricing, more than R&D, certainly more than customer operations can
experiment and they
can fast fail and they can figure out what that production model looks like to
take advantage of
prediction and not collapse the business. So that's why immediately they are
the functions. Go to
market where generative AI can produce incredible results and perhaps build a
moat behind it.
Yeah. It's super interesting. I have to ask the off script a little bit, but we
've leveraged,
we've used this language in B2B or Go to Market for some time of marketing
automation,
which almost feels like in the 20 year context in which that term came up,
mistimed or too early for its time to some extent. But now, as we think about
predicting
actions using data, potentially, agentic workflows to take the action on half.
Covering that. I'm excited about the agentic economy. Yeah.
Very curious about like, is marketing automation, true marketing automation now
coming?
Well, humans, be in the loop, have to be in the loop given some of the lack of
tolerance for
hallucination, maybe for at least a high profile prospect or whatever. Just
curious, where do you
see the marketing mix headed? So first of all, when you think about the
disciplines of artificial
intelligence, there's two. There's machine learning, which is rather mature. It
is very simple.
Zeros and ones. That's the way I talk about it. Very simple. Yes, no, this,
that kind of predictions.
And then there's natural language processing. The artist known as Gen AI, much
more complicated,
much more sexy, because any fool can get value from it. M.L. is a black box. As
a result, machine
learning has become the nickelback of artificial intelligence. Even though you
know it works,
you would never tell your friends at a party. It's a private pleasure for data
scientists.
Automation right now should really be called marketing velocity, because that's
what you're
really talking about. You're talking about increasing the velocity of go-to-
market motions that capture
the value of innovations. That's what we're really talking about. Automation is
a path to
velocity. The issue though is that there's automation, semi-automation, path to
automation,
I think right now with Gen Ritavaya, we're still at path to automation because
it has a high human
and the loop requirement. Automation is only a goal in as much as velocity has
impact on your business.
You have to ask yourself, faster to market is that an advantage for us. It
could be.
The other thing is getting back to this McKinsey research. For sales and
marketing, what they
looked really hard at was the idea that when you're selling, and this is
important for the sellers,
when you're selling artificial intelligence, you've got two models to sell it
to the customer.
You've got the replacement model. They call that the cost model. I'm going to
bring down your cost
measured by headcount reduction. Then you have the capacity model where you say
I'm going to augment your existing team members so they can increase production
without increasing
headcount. Very sexy. What they say is organizations that sell based on the
capacity value proposition
will create more satisfaction, find more true value, and our terms get better
renewal and expansion.
Why? Because we're never satisfied with the substitute. That's the inherent
flaw in looking
monolithically at a concept like automation. The idea is that AI is not going
to take your job
and kill your company, but humans that are armed with AI that strategically
focus will
that take your job and kill your company? Totally. Well, you know, I want to
just coming back to
your role at G2. A big part of it, as I understand, is evangelism as well. So
obviously doing the
research, but then getting the word out to market, maybe doing conversations
like this and several
others. Evangelism as a concept has been around for a while, but it feels like
over the last few
years we've seen new terminology around things like creators or influencers
really start to
pop up as well. I'm curious, how do you think about evangelism as a function of
helping brands
build these relationships with a market? And maybe how does it differentiate
from some of these other
emerging functions as well? A creator produces content. An influence who
produces results.
The influence, that's the difference. There's an old Chinese proverb,
if they're not following you, you're just taking a walk, right? You're not a
leader. So an influencer
inherently grows their following in a measurable sort of way. They grow the
experience of people
taking their advice and putting it into action. So I think a lot about
connecting with influencers.
One of the things I'm doing for DQB at Harvard is we're syndicating insights to
certified digital
influencers to spread the word beyond what we can do with say Harvard Business
Review or whatever.
Let's talk about evangelists. I got interested in this concept maybe 25 years
ago when I was
working from Art Cube and Guy Kawasaki, by all accounts, would be your first
codified chief of
evangelist for Apple, right? So he was a mobilizer. What he did is he mobilized
the emotion of design
excellence and he transferred it from Steve Jobs Mind to the end users where
they said,
you know what? Computer's personal computers should be beautiful. They should
be able to be up and
running in five minutes and they should be simple and they should almost
disappear into the fabric
of our lives instead of being this thing that a Hewlett Packard or Gateway
sending us. So his
job is to mobilize people to make decisions based on design and not price. And
as a result, when you
think about the effect of Guy Kawasaki and we're here in 2024 to quote Prof G.
Scott Galloway
my favorite podcast out there markets, Apple has become the number one luxury
retailer in the world
who now sells scarcity, right? We never think about the price of an iPhone. It
is ridiculously high
because we think about the design opportunity, right? So when I fast forward
because that was
exciting to me, I fast forward just a few years when Yahoo buys broadcast.com
and I go to Silicon
Valley. I work in the value lab, which is kind of what I'm doing at G2. We're
converting data into
actionable insights to increase leverage for the sales team and get clients and
keep them.
I really had my eye on being the next Guy Kawasaki and I got very lucky because
our CEO at the time
felt like we had a what you would call a go-to-market problem. No one believed
in digital advertising.
The trope was banner ads didn't work and all of a sudden market is say we're
going to pay only
on clicks. Well, we ship impressions and that should be rewarded but there was
a real problem in the
market associating us with the kind of impact of television printer radio. So
now we had to sell
on clicks which could take profit out of the model 80, 90 percent. So the job
of ValueLab
was to create data but what we lacked was something public facing to make it
simple for people to
understand. So I became Chief Solutions Officer which was a proxy for Chief
Evangelist and my job
is to get in front of all of our significant quality opportunities and serve as
what I call an
explainer. Okay, so there's two kinds of evangelists right now. And I'm just
making this up. There's
what I can tell you about. There's the explainer that takes the mystery out of
something and
democratizes it for the listener. The listener feels empowered and the listener
usually becomes
an evangelist. Okay, then there's the mobilizer. Mobilizer connect and this is
really based on the
corporate executive board research from Challenger customer. The fantastic
follow-up to challenge
the sale. The mobilizer knows how to get people within their organization to
embrace change and
they have the gravitas to unseat incumbents. Okay, mobile hours are powerful.
They don't just
have influence. They have power, right? So that's a different kind of evangel
ist because that
evangelist, their job isn't to democratize the product and the technology and
the innovation
because the problem for them is in mystery. Their job is to do a change
management because there's
some kind of scaffolding at the customer level that's inhibiting utilization
which shows up with
where's the value. Right. You know, because I believe right now for software
the biggest problem
when it comes to renewal or perceived value is that we don't as sellers take
ownership for
utilization. Right. We put it on them. Yeah. Right. We do a couple of cursory
moves. So right now the
mobilizer's job is to convince everyone in the organization through the
acquisition of mobilizers
and they become evangelizers too. Change is critical. There's a burning
platform. Change is easier than
you think with rapid collaboration and change is going to be your competitive
advantage because
once you learn how to be agile and adaptive learning effects, think of it like
network effects on
steroids, learning effects will create a win for you that no one can catch up
with. So those are the
two types and they approach different problem spaces based on the enterprise.
You don't have to be
both. Yeah. But I found in the last year is that if you're selling artificial
intelligence,
especially you're not selling Nickelback, you're selling this new kind of AI as
a higher cost
structure, probably a higher on ramp that you have to suffer through and you
have to have a higher
tolerance for mistakes because it's not as clean as ML. Right. If you're
selling that, I think you
need to be an explainer. Interesting. Okay. Right. So I find that when it comes
to getting people to
the table with their checkbook and CFO right next to them, just democratizing
that AI is just a
prediction machine. Yeah. And that's all it does. It takes information that you
currently have and
produces data that you don't have. Yeah. Just that bit can unlock C suite who
currently see it as a
threat, see it as a bad right, see it as something that is currently overpriced
. We're going to wait
for the bubble to break all of those really come from that internal ego pushing
back and says,
I don't get it and I'm a smart person and that bothers the crap out of me.
Right. So
that's what I think the most important motion is for AI. However, what I think
a little bit
downstream and I look at at software solutions that you define as being in
competition with the
status quo in that situation, I think you need a mobilizer. Gotcha. Right. So I
think you need to
accordingly authors of a challenger, customer, Matt Dixon in particular, you
need a dog whistle
that brings the mobilizers out and that's where content marketing, getting
connected with revenue
organization and product and success where all of those functions coming
together can
absolutely bring mobilizers out so we can create relationships with them. We
can enable them through
a variety of different tools to go back home and sell, especially to all the
decision makers we
can't round up and talk to. So I think you have to make a decision what your
problem is.
Are you selling something that's just new and confusing or are you selling
something that
requires some internal change and behavior for them to utilize and realize
value and then you should
go pick your evangelizers wisely. Right. That's so good. Well, look, we're one
time for one last
question here. We're here at the Better Together event and I'm curious.
Obviously, you're ramping
up in the AG2 in this new role. We're even at Harvard or as you kind of think
about the work
required to educate, to build the insights and so on. What are two kind of
tools that you leverage
to technologies, to services that work better together in your own work?
You know, I have to say one of the most powerful collaborative tools that I use
anywhere is Miro. I love that. Right. And it's because I've learned it. Right.
You can use a
variety of different tools, but I like Miro. And the reason why is because it
helps us kind of
the prototype and visualize concepts and kind of bring them to life for people
because we're
very visually oriented. One of my old mentors, Tom Peters, he wrote a great
book called 40 Years
ago Called In Search Of Excellence. You said the value of a prototype is that
someone can point at
it and say that's not it. Oh, I understand. Right. It solves this ambiguity of
like you and I just
sitting around talking. Right. So I really like Miro as a tool that I'm going
to use all the time
and I can't live without my slack. Yeah. Right. I mean, slack's like so much
better than traditional
messaging products. Slack is so much more collaborative than asynchronous email
, etc. So I started to
try to find more and more innovative ways to use slack. I've also tried to
become much more conscious
of not reaching out to people on slack during times I should know better. Right
. So if they
don't have calendar on, if I'm in a slacksum, but I literally take a 10 second
journey to Google
Cal to see where they are. And I've learned that if you know when to get them
live and once you have
them live, you can clearly pursue the next decision, slacks a killer
application right now. I love
that. I do want to say one thing before you forget because, you know, people
ask me all the time,
like, what have you figured out, like, in the last few years that that caused
you to leave your
career at Upwork after five successful years and go to G2 and it was this
reality. I read a book
several years ago by Daniel Kahneman, a great researcher on statistics and data
and all that
type of thing. He just recently passed away. He wrote a book called Noise that
everyone watching
should read. He talked about the idea that you should look at data like oil. It
has grades.
Right. And crude oil is not really usable by an enterprise. I think that's very
interesting.
So what I began to look at a few years ago is the concept of data bias. So data
is only as good
as the context in which it was collected. Oh, interesting. Okay. So what I
figured out, and this
caused me to be like, I got to go to work for G2, a customer's first party
voice of the customer
data is so biased and noisy, it would actually decrease your sales if you
completely relied on it.
Because it's the context for that gathering is usually perceived by the
customers of business
development conversation. The most biased data in the world is data from a
buyer before a renewal.
Like, how are we doing? Well, I'll tell you how we're doing because I want to
discount,
I'm getting ready to turn. I've learned that the G2 data, the second party
verified, well-structured
data is rocket fuel, especially for training a large language model via buyer
intent, market
intelligence, or just informing decision making. So to me, this idea that
second party data is
completely better than third party data, obviously, because the context is
terrible there. We're
literally making stew a first party data. And that's an insight because when I
talk to organizations,
they always went, we never thought of it that way because they're so precious
about what they
collect. So that's the reason I joined G2 because they're the leaders in the
world by far, much more
than Gartner at capturing high quality, well-structured second party data
because it's in their DNA from
GoDard all the way down to the rest of the org. Well, congrats again on being a
part of that team
and appreciate you being on the show and looking forward to seeing all the
great things to come
out of the research arm. Thank you so much, man. Nice to meet you. Thank you.
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