Hi, I'm Noah Horton, the CEO here at Unsupervised. And I'm
going to talk today about how we can skip endless dashboards
and get straight to the why. So there's three questions that
you're asked constantly about what's going on in the
business. The first is exactly that. What is happening? For
the key metrics that we care about what is happening to
those numbers? But the minute that we answer that question,
and tell people what's happening, the next question is why?
Why are the numbers doing that? And once we know the whys,
it's how do we change this? What are the actions we can take
to improve the metrics were focused on. And so that's a
cycle, each one informs the next. And today, the main
technology that we have on our hands is dashboards. If we
want to be able to tell the business or know what's
happening with one of our metrics, we create a dashboard for
it. The problem is dashboards don't tell us why. Why are the
numbers doing that. And if we don't know the why, we can't
get to the how. You need to know why your business is doing
that before you can take an action to change the business.
And if you can't change the business, you can't improve the
metrics. You can't change the what. So these are all
interconnected. And ultimately, the dashboard is only giving
you the what it's the tip of the iceberg of what's happening
in the business. The why is the most essential part of this.
That's what we can't get out today. And thus, we have all
these questions that come at us constantly. Why is average
order value off? Why are MQL conversions going down? Why is
cart abandonment up? These are the questions that we're
constantly trying to answer. And right now our options are
just keep clicking around and dashboards or go to the
analyst and go into a very involved process. But what would
it look like if we had a different way of doing this? What
would happen if we started with our why, but we had answers
coming to us. So using an ecommerce example, a common issue
might be something with a particular promotion code. Right
now we can look at something very simply of which promotion
codes that we've put out are being most effective. But
that's not usually driving the Why. Yes, one might be
particularly effective or another might be less effective.
But it's the nuance inside of it, such as what's being shown
here. This is an example of one of the findings from the
unsupervised AI. What it's pointing out here is that this
particular promo code, when it's applied to customers who
are loyalty customers, and they're in a ecommerce session
that is between these particular lengths, about 30 minutes
to about an hour and 15, we're seeing that for those
customers average order value is off. That's the set of
customers where that promo codes not working well. That's
the kind of why that you can actually act on. That's the why
that drives the how. But it's not the only one. At any given
time, there's a huge number of things that are affecting
that metric. So for example, products in a particular family
are probably behaving differently, in one particular place,
the Northwest region, and in a particular time period. This
is another great example of something where the how of how
you'd act on this, why is totally different. This is
something you might target in ads, you might do different
campaigns around, and there's gonna be more and more of
these. Getting to these whys behind your metrics, is what
allows you to have the how's to prove the business. So
today, your only choice, if you want to get to that level of
a why is to go through the analytical process. Now that
starts off with a huge amount of data, you've got all of
these different systems in different silos, different data
warehouses, all of this different data out there. And it's
too much, you can go look at one data source at a time in
the dashboard that's associated with it. But if you actually
want to find something across all of that, you need to take
it and prepare that data for analysis. An analyst or a data
engineer is going to take a couple of the sources because
they just can't handle all that much. They're going to
choose a couple of the data sources that they think are
going to have the why's in them. They're going to bring
those datasets together, they're going to join them, they're
going to choose how to aggregate things across it, they're
going to drop a lot of features. In the process of doing
this, they might engineer a few more. Ultimately, though,
it's just a shadow of your total amount of data that's going
to make it through that, just a tiny sliver. But once you've
gone through that work, which is arduous work. Now you're in
a place where you can start looking for insights across that
data looking for potential why's. And that's hard work as
well. This is where somebody is going to be using a lot of
tools, BI tools, Python, all sorts of stuff like that to try
to find some interesting patterns in the data. Once they
find some things in that they're going to prioritize which
of those seem the most important and bring those to the
business? Those are the why's that are going to surface.
And at that point we can get to the hows and the actions. At
that point the business is going to take those they're going
to build strategies for the ones that they think are the
most actionable, they're going to execute on those
strategies. And that's what's going to drive the business
outcomes. Now, to be really specific about this, though,
that first part is entirely driven by people, I might use a
tool like an Alteryx, or Tableau or such. But those tools
are just doing what I tell them to do. So it means it moves
at the speed that I can. And it also means that it's taking
any bias that I have about, for example, which datasets
we're going to find the answers in, and any mistakes I make
along the way, and it's just going to propagate them
forward. So it's very limited in what we can do at that
point. Now, the second half of this, oftentimes, that also
is done by people, but a lot of the biggest innovation that
we've been talking about as an industry recently has been
using AI in that part. But that's also very limited in what
you can do with it. AI is not something that can help you
come up with a new product, come up with a new marketing
campaign, what it can do is that for some of the insights
that you find about something like, you know, these five
features are particularly correlated with the metric you
care about, it can let you build a model, I can let you do
automatic scoring, or automatic routing of emails, things
like that, based on the thing you'd already found. So
there's value in that that's helpful. But it's only limited
to a small sliver of those why's that you found. And it's no
addressing the top of this funnel, which is where w
actually have the biggest problem. But we add unsupervise
fundamentally do is turn this around, we're bringing AI t
the beginning of this process work can actually scale to al
of those data sources, we're moving beyond the human limit
on how much data we can take in at the top, and how muc
data we can find patterns across. So that means that no
instead of spent spending weeks or months trying to fin
things in that data, it's now minutes to hours, it als
means that we've removed a huge amount of bias from th
process, we're looking across a lot more data, we're lookin
for more patterns in it, we're looking for more comple
patterns. That means that things that I would have neve
thought to look for are going to be found through removin
my bias. And what's also maybe most exciting about this i
that this scales, we keep coming up with new systems to use
we keep coming up with new data sources. Every time we d
that today, it's creating more work for the analyst is goin
to try to use those ultimately. But in this world, it'
limitless, we can keep adding more and more data. And i
keeps giving us better and better insights, better why's. B
t the other thing that's still incredibly important in this
s the human aspect. There's a huge role for people in thi
. Because if we can actually find that much wider set of why
s, and put it in front of the business experts in the s
me simple form that they get just the what today, if the s
me people who look at charts can actually see these why'
in plain English, that unlocks huge amounts of value. Yo
've got a much wider funnel of why's that you can ch
ose between, you've got more things that are low hanging fr
it, you can come up with more potential strategies,
ore actions, new products, new campaigns, new policies, al
of the range of levers that you can pull in the business
hat you can use now. So that means ultimately, bigger outc
mes more money. So let's talk about how it works. There's a
lot of different types of data that you're bringing in r
ght now. Some of those are unstructured data, such as p
ain English text. And many of those are structured data sou
ces like traditional databases. And that data is coming fr
m a lot of places, it's on premise data sources, and its c
oud data sources, were able to take all that data from all t
ose locations and bring it into the system. The core of
our system is the semantic fabric. Semantic fabric connect
to all of those data sources, and then takes sema
tic information from you information about the data and a
out the business. Understanding relationships in the data,
and that a customer ID in one system is the same thing
s a customer number in another. It gets that kin
of information, which it can then use to intelligently ha
dle the data, joining it on its own, be creating new featu
es, aggregating intelligently, taking that context that you
ave in your head and intelligently applying it to the dat
to make that available for all the rest of the process. Now
the next part of the process is that insight engine at
the bottom of this diagram, the insight engine is what
can handle all of the complexity of the data that sema
tic fabric is creating. It's able to go across all of t
ose hundreds of 1000s or even millions of features, and
ind patterns across t
It's then able to share those patterns through the Insight
explore. That is an easy to understand user interface that
people who right now are limited to charts can look at and
see exactly what are the most important insights that the AI
is found and interact with those insights. But we don't stop
there. The most important part of that funnel at the end of
the day is the actions that are being taken and the impact
that's being gathered. So we support entirely through that
process as well. You can take the insights that are found,
and take the ones that you are choosing as being the most
important, the most actionable, and turn them into what we
call data opportunities. And with opportunities, you're able
to plan what you're going to do around those, keep track of
your investigations, go through estimations of effort and
return track through approval and implementation processes,
and ultimately capture exactly how much ROI came out of
that. And that still feeds back in. Because we're capturing
that information, it means this entire AI system can keep
getting smarter, we can be looking for more and more
patterns that look like the exact type of ones that have
driven the most impact for you in the past, as well as
completely new styles of insights. This is how we unlock
nearly unlimited data to give you the full value that's in
it. So let's go to some real examples of this. One of my
favorite examples here is Charlotte's Web. They're the
largest producer of CBD products in the United States. And
they look like a lot of organizations that are probably
watching this right now. So they are primarily a CPG
company, they sell through channels, a lot of the biggest
drugstores in the world sell their products. But they're
also building a direct consumer channel that's incredibly
strategically important. That's where we come in. They have
a three person team that's managing that channel, compared
to armies of people and other organizations that are also
selling their products, or competitive products. And they
need to understand why critical metrics are behaving the way
they are. average order value is a great example. But things
like customer lifetime value are even more important. Let's
just not get big orders today, let's take every one of these
precious customers and make sure we're maximizing their
impact. The data that they've analyzed is incredibly
complex. It's web interaction data, its customer data from
CRM, and it's all the transactions that those people are
completing. And that adds up to a lot 40 million rows of
data, and growing 17 different source tables that that's all
coming from across all those systems with over 1200 columns
to start with. But we go a lot further than that, as that
semantic fabric layer is taking that data and bringing it
together, engineering new things off of it, that data has
expanded by over 400% with new features that the AI has
found. And it's then going through that at scale to find the
things that matter in that data, the result of this more
than 50 actionable insights in just the first six months of
usage in an interface that they can understand every week
coming in seeing the most important why's that they can act
on. And they're following that through. They're taking those
into the data opportunities as well. So that they're
planning what they're going to do about them and making sure
that actions are getting taken across all sorts of different
aspects of the business. And so what are the results of that
they've more than doubled the revenue per customer, they've
over $2 million in opportunities that they've identified.
And some of the actions that they've taken are incredibly
impactful, such as over $100,000 a month in revenue from a
product that they previously not focused on, that was
identified by our insights. And I'll let the customer speak
for themselves on this. This is something that has
ultimately led a team change the game. They're outperforming
those data armies in other organizations, because they're
starting with the why. Now they're not alone. A great
example of financial services company that's just started
with us understanding what's happening with loan
applications. That's the most important what for them. And
we're giving them the why.
One of the most popular consumer services companies a major
franchise organization, understanding what's happening with
their customers returning, they get them in the door the
first time, but what's more important is do we get them in
the second time? And how do we do that most effectively over
$4 million of opportunities in the first eight months of
usage. And one of the largest HR organizations trying to
understand what's happening in terms of customer retention,
and all sorts of segments in the business, over $2.4 million
in opportunities. These are all very different
organizations, looking at very different businesses. But at
the end of the day, it's all about the most important what's
for them and the why's that are driving it. So that's just a
few of the organizations that are getting to the why's
driving their business with Unsupervised AI. And I hope that
you'll join them soon reach out to get to the why that's
driving your business