Techtonic with Thomas Lah

[Speaker 2] (0:00 – 0:53)
After decades of user-based pricing being the most common model for software companies, is
it time to rethink that approach? Well, why not bring in an executive from a company named
Software Pricing Partners to find out? I am Thomas Law, the Executive Director of the
Technology and Services Industry Association.
Welcome to Techtonik, the podcast where we explore what makes technology business models
successful in today’s world. Today, I will be talking with Chris Mealy, the managing partner at
Software Pricing Partners, a firm that specializes in pricing software to maximize profitability.
We will be discussing the legacy of software pricing and market forces that are now putting
pressure on those models.
Let’s get the insight engine humming here. Welcome, Chris. It’s great to see you again.
And I wanna open up with an easy one. How long has Software Pricing Partners been around?
And what was the catalyst for this firm even getting created?
[Speaker 1] (0:53 – 0:58)
So it’s been around, so 44 years, so 82, actually 42 years.
[Speaker 2] (0:59 – 1:02)
I have a feeling you weren’t there at the beginning. No, I was not.
[Speaker 1] (1:02 – 2:18)
82, I was still, geez, I was still pretty young, 10 years old. So the manner by which it came
around was I started the software company and we moved to the cloud in 08. So it was about
10 years into that journey and we decided that we were gonna replatform and the market was
slowing down.
We had no idea, by the way, of how much it was gonna slow down. And along the way, one of
the investors said, hey, we really should procure some help. We’re gonna change our business
model.
We’re looking at subscription. That’s gonna change our cashflow and all this other stuff. And
then we reached out, talked to a bunch of firms that frankly were just talking about planes,
trains, automobiles, and pricing things that you could touch.

And of course, we’re talking about things that you can’t touch, intellectual property. And when I
met the team at Software Pricing Partners, I just kind of fell in love. I spent the next four or five
years realizing that’s really kind of a big ingredient.
It’s got a lot of science and a lot of history to it and a lot of decisions that were gluing things
together that were kind of unglued, but I didn’t really understand that at the time. And then
when I left that in 2013, I accepted a partnership position here and then in 2018, bought the
founding team out and continued the journey of applying technology now to that stack of what
used to be professional services.
[Speaker 2] (2:19 – 2:45)
Well, that original, because I did not realize the firm had been around that long in that history,
but the original team and when they started, I mean, those were in on-premise, pretty
traditional. That’s what they were obviously super deep in. So they, just like you as a software
provider was going through a business model transformation, they had to as well, right?
They had to move to all these different, okay, cloud-based pricing and we’re moving to more
subscription and all this kind of fun stuff. So I’m sure that was a big inflection point for the firm
itself.
[Speaker 1] (2:45 – 4:03)
Yeah, so when I procured them to help with my software company, we were on-prem and had
been that way for about 10 years. And there was a manner of licensing our software that was a
little bit different than the named users that became popular after Salesforce and others put it
into production in a bigger way, but that had been going on for a lot of years. And we’ll talk a
little bit about that, but the concurrent user model was one that I was using and that’s basically
a shared license.
And the history of that is actually pretty cute at the firm. So there were 27 CAD and engineering
firms that went through the first pricing projects in the late 80s. Back then, the network had just
come online.
So you were just untethering yourself from an ethernet cable. And I think it was Apollo
Computing and then later Globetrotter came out with the licensing manager that was going to
pass out a license on the network. And of course, this is gonna break everybody’s business
model.
You used to be able to sit on a desktop, tethered to the wall and on a single processor. And I
could share it with you, but I had to get up and move and then you’d have to go and sit. So it
was self-regulating.
And this was gonna be the antithesis of that. And so out of that model came a concurrent user
pricing strategy that became dominant. That’s what I built our software company on.

And that’s part of the reason why I kind of felt beholden to the firm that had created the
strategy that we adopted.
[Speaker 2] (4:04 – 4:15)
Yeah, yeah, yeah. Super interesting history. So now the firm, what are the types of services you
provide to a customer?
So when we say, under this umbrella of software pricing partners, how do you help a company?
[Speaker 1] (4:15 – 6:17)
Well, so we have the design of the strategy and we break that into three parts. And these three
parts were first proposed in the 80s and they’ve sort of defined the language set that we use
today. The first one is licensing, but not the legal terms and conditions, but there is a
connective tissue there, but it’s the thing that you’re gonna count.
In this case, you used users. So the question is, well, what is that? How you define it?
Are you gonna count on an employee user the same as a subcontracting user, the same as a
part-time, et cetera? And that definition would appear in the legal license agreement, but it
really is the quantity field in the sales contract. And it defines really your valuation.
On one side of the spectrum, all you can eat, the quantity’s always one. On the other side of the
spectrum, I charge you every time you log in and nobody buys. And so the answer somewhere
in the middle.
Then you have the second discipline, which is packaging. We call that the offering model. How
do you put not just the capabilities, but services?
We’re monetizing intellectual property. That comes in different forms. It comes in the form of a
product capability, comes in the form of a service, actually comes, interestingly enough, also in
the form of insights that you can use to further monetize your offering.
And then finally, we have the pricing, which is the harder core science, the modeling, the
simulation, and the actual impacts of customer transitions and who pays what and how much
more and what does that look like? And that together is the design of a monetization strategy.
And then we also have technology that helps in the operationalizing of that in the tech stack
where we monitor the effectiveness of packaging and pricing.
But those are reserved for companies that really are turning pricing into a process. And really,
that was my big journey was it’s part of the fabric of how you build a product. But we just, we
were never onboarded that way.
I was always onboarded. That was product market fit. It was never product market profitability
fit.

And I just need a number. It’s like an attribute that I put over on that product. And it turns out
that that’s not the right way to think about that.
[Speaker 2] (6:17 – 6:44)
Well, so I wanna play a little bit of that back because I love that frame there on those three
buckets. So there’s licensing, then I get a package, and then I finally do the pricing. And then
once you go through that with a customer to help them think through those pieces, is there an
ongoing monitoring component where you come back and you’re checking six months, a year
later, hey, how did that work?
Is the packaging still right? Is the pricing still right? How does, just give me a flavor for that.
[Speaker 1] (6:44 – 8:13)
So we call that continuous monetization, and we launched that around 2020, 2021. And what
that is is a technology component that drives the pricing calculations for a firm. It allows us to
be very agile.
We can do a meeting on Friday with a CFO and adjust the pricing and publish a new API key,
and you’re off to the races globally, pricing across 1,200 salespeople. And it just makes
companies faster. As a publicly traded software company, you’d be lucky to deploy a pricing
change once a year.
It’s just that the systems are so complicated. There’s so many SKUs in class. So we’re speeding
that up, right?
And then secondly, every time that there is a salesperson or customer behavior, so you come in
and you say, well, I’d like to check out the Enterprise Edition. I’m gonna look at 5,000 units and
15,000 units. Whatever those units are, we have to design that, right?
And then I bought as a pilot with 150 units. We see that entire journey. We see what you looked
at, and we see what you didn’t look at, and we see the events that yield a win and the events
that yield a loss, and we use that to improve the packaging and pricing also.
So that continuous nature right now at our firm, after you go through that design stages and
invite only kind of process, and then we team very, very closely with that company and start to
work through the rest of the portfolio. But we’re turning pricing into a science where, as one
member of our team here says, we’re weaponizing pricing. I’m not a big weaponizing fan, but,
you know.
[Speaker 2] (8:13 – 8:14)
You probably don’t like that framing.
[Speaker 1] (8:14 – 9:03)

Yeah, you’re trying to, ideally, before you build the product, you know, we all have a roadmap,
and we prioritize that roadmap. And I used to call it prioritizing by screaming. So that has a
certain priority to it.
But what you really wanna do is you want to dip into that roadmap, and you want to say, well, if
I was to launch this particular component, what is its monetization potential? Because I might
wanna build that before this other component. And the thing with that is it’s hard to get to that.
You don’t get to that day one. First, you gotta get out of the Wild West. You gotta get some
predictability in your net prices.
And then after that, you can really start to turn it into. You’re more sophisticated. Yeah, and
subtle nuances, you know, just little changes in the 10th and 100th percent of the decimal place
at scale yields massive outcome when the company gets big enough.
[Speaker 2] (9:03 – 10:21)
So you, you know, are using this word, you know, profitable. And I’m just curious, you know, I
didn’t talk about this, but I just wanna put this framing on the table. When I think about all the
SaaS companies that we’ve benchmarked and worked with over the past, you know, decade,
profitability was not job number one, as you know.
I’m not telling you anything you don’t know, right? And what you’re describing, I’m just super
curious how you would assess this. So what you’re describing this model, right, where you say,
I’ve got licensing, I can get packaging, I do my final pricing, I’m thinking about profitability all
the way through that.
I release it into the wild, I get more sophisticated, I prioritize my roadmap based on profitability
potential. So that is a very mature, nuanced, sophisticated model, right? So let’s put that on a
scale of one to 10.
That’s like a 10, right? But I am telling you, man, these SaaS companies that I’ve seen, you
know, operating were nowhere close to what you’re describing. Up until 2022, when the shit hit
the fan, right?
Then it was suddenly like, whoa, whoa, whoa. So just tell me what your experience has been
watching this play out over the past couple of years. I mean, what was it like pre-2022 on a
scale of one to 10 in terms of sophistication of pricing where most SaaS companies like a five, a
four, because I know they weren’t 10s, but what did you see?
[Speaker 1] (10:21 – 12:32)
No, I actually think it’s probably, you know, in the one range, you know. Yeah. When I had our
software company, we were in the interior product space and we would take what was a
manual product.

So I think CRM sales, purchasing, receiving for things like windows and doors, faucets and
fixtures, stairways and railings, roofs and trusses, cabinetry, and all these configurable
products. And it was very, very complicated space, but we would take purchasing departments
down from like 40 people to one, like part-time, and a backup, right? Because you needed to
have a backup in case that person went on vacation.
But the scale was tremendous. And I remember at the time having discussions with people
where I would lose a sale because they couldn’t come to grips that 38 people that they knew
and loved were gonna be let go. But that’s a phenomenon of like a space age.
You know, you’re showing up with a rocket ship to bring somebody manual. So I think it’s like
that now. And Briar, to the 2022 shit show, as you said, and boy, was it crazy.
We spent a lot of time fighting the story that there’s growth and then there’s profitable growth.
And you gotta be really careful depending on what’s in that licensing model, because if you’re
selling something that has a lot of quantity and then people aren’t using a lot of quantity, like
eventually when the contraction comes, you’re gonna have a really big mess with people
wanting to right size and all this stuff. And so nobody wanted to hear, it was growth, growth,
growth, growth.
You know, show me how the revenue’s gonna climb. And really fighting for decades, even prior
to when I arrived, you know, growth trumps a lot of sins. And so all those sins were like
uncovered in 2022 and beyond.
So now all the phone calls are like profitability, being more thoughtful. And I think a lot of, I
think it really was a catalyst to rocket what I call that, again, product market fit was the
orientation that everybody was onboarded in. And it’s just a symptom, but it’s product market
profitability fit.
It’s that third component of that profitable growth that you’re seeking. And I think that is gonna
be really interesting over the next decade, because that really is everybody’s focus right now.
[Speaker 2] (12:32 – 14:11)
You know, the main reason I really wanted to bring you onto Tectonic was to talk about how the
pricing models are shifting yet again, if you will. So if you think about it, 2022 starts a whole
new discipline around profitable pricing models. And so, again, the scale one to 10, they’re like
a one or a two, and they’re like, oh my gosh, I need to be at least a six or seven or eight in how
sophisticated I am.
So there’s been this mad dash over the past two years to get better there. And I’m sure a lot of
software companies are still clawing out of that in terms of getting the capability in place,
getting more sophisticated. And just as they’re trying to do that, this next wave of reality has
been playing out where, as you know, a lot of companies are user-based pricing.

And lo and behold, the renewals are coming up. And when the companies that we’re selling to
are always adding more employees, there’s natural growth there. No matter what you do with
your pricing model, right?
Because you have more salespeople, more customer success people, more whatever in play.
And now, in tech, definitely our industry itself has been really flatlining on headcount growth.
Now there’s not natural growth by just charging for users.
And that’s part of the fiscal reality that these companies have been more conservative. AI is
definitely playing out here. It’s slowing the number of headcount people want to hire or are
going to hire.
And so now there’s this second sort of shock to the system for these software companies to
say, oh, not only do I have to have more discipline to get profitability, but now I gotta maybe
even rethink how I’m pricing because user-based pricing may not be the winning play. So I’m
curious, are you seeing this second shock start to unfold with the companies that you’re
working with? How are you thinking about this?
[Speaker 1] (14:11 – 19:05)
So let’s rewind in history a little bit to put that in perspective because that shock has been an
undercurrent for decades. And then you’re right, it’s starting to really kind of peak up. But let’s
start the conversation with the typical product management focus of, we have actors.
We have a human actor, we have a machine actor. So as early as in those 80s, people were
introducing API layers. And what that meant was some of the capabilities would not necessarily
be consumed through the UI or the UX.
They would be consumed programmatically. And early on, a lot of that was just given away for
free. And then people started to realize, well, there’s like some stuff happening back then.
Now I’m talking 80s, 90s, yeah. So there’s stuff happening back there and there’s a lot of stuff
happening back there. And this is not commodity stuff.
In fact, maybe this is at the core of an algorithm or an optimization routine or some part that
the firm, the software company feels like is the intellectual property. And so people have had
API layer pricing for decades. So this machine human actor phenomenon expresses itself in
other ways.
So for example, in security, there’s the concept of an identity. And so we’ll just play a little game
here. And if I was selling to you and I was charging based on the identities or based on the user
licenses, I have an employee that has access or you have an employee that has access to a
couple hundred apps.
They need to have that controlled. Then we have a subcontractor that has access to a couple

dozen apps. They need to be controlled.
The focus in security is often on that human component, but it turns out that on the machine
level, like you can do a lot of damage with a machine identity. In fact, you can do a tremendous
amount more damage because you can do it at scale than you can as a human. But if I told you,
Thomas, we’re charging based on identities, not users.
So this is in the licensing model. And I’m gonna count up these identities and say, well, we have
human identities and machine identities and the human identities are, I’ll keep math easy. This
is not a recommended price point, but a dollar.
And the machine identity is $10. You’re gonna have most likely a visceral reaction to that ratio,
to that differential. And that visceral reaction is because machine is machine and a human is
something valuable.
And so what I’m doing is I’m connecting this thread from the monetization approach into the
sales part of the discussion. Now, if I played that back the other way and I said, well, the human
identity is a dollar and the machine identities are 10 cents, you’d probably have a much easier
way to do that. But that creates this really hard problem that, but my IP is on the machine and
it’s being accessed by the machine and my value prop is protecting against, in this case, the
machine scaling and doing harm.
How do I get paid fairly for that? So this pattern, my point is, has existed for decades, right? For
a long, long, long time.
So the question at its heart then is, now we have a novel new innovation in the AI space. That is
still very early. It does have reliability issues and there’s a different podcast you can do on,
some people think it’s gonna be like self-conscious next year and other people are like, you
know, I was listening to that MIT professor, Asamoglu, Asamoglu, Darren Asamoglu, I think is
his name.
And he thinks it’s a 5% of GDP because it’s just such a narrow range of use cases that it applies
to right now. But that will change. So now we hit, it doesn’t change, by the way, the fact that
everybody’s jumping all in and creating all this stuff in the roadmap.
So now we have this phenomenon of, how do we get paid fairly for the machine portion? And I
just wanted to highlight that what we’re looking at when we’re monetizing is intellectual
property, the delivery of the value, but not monetizing its use. You’re monetizing the value from
the use.
So this is why I hate the term usage-based pricing, but because you don’t charge based on the
use. I’m gonna use your software, I’m gonna pull out the value, I’m gonna embed it into my
workflow back here, I’m gonna extract some additional value and I’m gonna go play golf with it,
in which case no value creation, or I’m gonna like return it back to my company and that’s what
you’re trying to value. So it’s a little bit of a different phenomenon.

But that intellectual property comes, like we talked about in multiple forms and that piece to
the puzzle is the piece that companies have struggled with for decades. In fact, companies have
been charging on a non-user basis. I can think of one in the open source arena in the late 80s,
charging based on the number of vulnerabilities you would find in the code, for example.
So this usage, this named user, this user-based pricing, I think has been an inefficiency that has
sat here for decades and now it’s really starting to come. Just like people are starting to come
online, oh my gosh, pricing is a discipline and there’s a science to this, it’s not as simple as I
thought.
[Speaker 2] (19:06 – 22:02)
So let me play that back because you put a lot of really great insights on the table there and
things that are definitely, I’m learning a lot in this conversation. So the fact that charging
beyond a user, charging, like you said, for an API access, all those types of things has been on
the table for quite a while and it’s been a conundrum that people have had to deal with. One of
the escape valves was, well, if we can do something user-based pricing, that’s easy.
What I heard there, that’s super tangible to the customer, they can kind of get it. I’ve got an
employee, they’ve got access to that software now, I get paying for that. And there’s always
been this challenge of, well, but if they’re paying for something else, like you said, if my API is
defending against a machine, but what I heard was, the value to your company is actually
higher because the threat from the machine is higher.
I should be charging you more because there’s more value, but just emotionally and
intellectually, it’s hard for customers to wrap their heads around that. So that challenge has
been on the table for quite a while and AI is just basically another instance of that challenge
because what you articulated is what I’m hearing from our members is I’ll give you a classic use
case. Hey, we provide software to support centers and the support centers are basically
declining their headcount because the staff can do so much more with these new AI capabilities
and they can serve more customers, blah, blah, blah.
And we’re putting these AI capabilities in, but how do we take the value out because we have
this user-based pricing model. So do we just, and we have a tough time just saying, well, we
want to jack up our user price here. So that is the conundrum is there is incremental value in
the software that is doing, how do we take that money off the table and decouple it?
Because I was just in a conversation earlier today with one of our members. And I said this
reality that you have customers that historically if their revenue grew, their employee base
grew with it. There’s a very strong correlation there.
And now we’re seeing this decoupling. Revenue can grow and then employee base can flatline
or sometimes decline. And if you are, again, in a user-based pricing model, that’s problematic.
And again, the reason I wanted to get you on here and we’ve got this conversation going on

with our members is I see this challenge coming and I think it’s going to be a big tsunami for
them to deal with. And they’re going to have to scramble really fast due to the types of things
you’re talking about to get way more maturity and way more sophistication around the pricing
model, or they’re going to be leaving a lot of value on the table. And this issue of profitability is
going to become a problem.
So I think it’s super interesting times. And the other thing here that is starting to play out is the
concept of having more data-driven, analytics-driven, even AI-driven pricing models. So I’m
curious what you’re seeing, because I know you guys have a history of the firm, you have
platforms to help people, software to help with pricing.
How do you see software changing the pricing game in terms of how you set your prices?
[Speaker 1] (22:03 – 22:49)
Well, so when we say AI, we can put a lot of stuff under the umbrella, including LLMs that
hallucinate. So we don’t use the LLM phenomenon in pricing because our goal when I came on
board was to 100% eliminate Excel. I don’t know if you remember the London Whale incident.
It was a $6 billion chase mistake from a copy-paste error. So I had this incident when I had my
software company where we were testing and we had a hole in our testing process and a
rounding issue occurred on a purchase order calculation. And by the time that our customer
and we had found it, we had already accumulated $32,000 of losses.
That was only after like a day and a half or something like, and so I was very panicked at that
time.
[Speaker 2] (22:49 – 22:50)
I would not sleep, right?
[Speaker 1] (22:51 – 26:11)
Thank God the customer was very understanding and we ended up extending their term. But
that could have been millions at scale across the rest of the customers. And so when we were
using Excel, when I first came on board and that was kind of the history of it, I just had this
terror.
And so what started as an internal tool for us just became an edict of just nothing but the
highest degree of accuracy. Like the worst case phenomenon is we’re messing with somebody’s
revenue model and we have a rounding error or something. Somebody copies and pastes some
transaction data or does something goofy.
So Levelsetter is our platform and it carries with it some machine learning components. And
those machine learning components are patterned after how net prices scale at volume based
on our research and our understanding of customers’ ability and willingness to take down lots

of quantity of software, not just user-based. But if you think about the history, we were getting
into, if I discover a million bugs this year, what does that look like?
And so these scaling issues have kind of, we had a lot of stuff in Excel to handle that and that
was what the terror, that was the reminder. Now I think it was fine and I’m sure it was, but that
was the reminder. So part of then bringing the sophisticated technology to bear is that there
really is not, there are things that we do with our algorithms that don’t work well in the office
suite.
And then secondarily, my mission then became, how do I get rid of PowerPoint? How do I get
you to viscerally experience a model? So for example, you log in, you use a quoting system as
your model comes online and you know what the different licensing metrics are.
We don’t just IBM fellow put our feet up on the desk and say, oh, I think this would be great
qualitatively. We consume the usage data, we do a lot of modeling, we do a lot of simulation
and we’re playing out revenue impacts. And what we learned over the years is that there are a
lot of answers to a problem when it comes to recommended pricing.
And it turns out there is an optimal. And so one of the biggest things that we do is we
determine that optimal. So if I’m gonna give you a pricing model, I can give you a pricing model
that just absolutely wreaks havoc in customer transition with large price differentials because
this customer got a 40% discount, that customer got an 80% discount.
And now when it comes time for my new cloud offering that maybe isn’t on-prem and I wanna
get you to that promise land, the price differential is so large that you’re just never gonna go
without special treatment. And so what we do is we compute and compress that so that I can
give you a model by saying, this is the best possible outcome. This absolutely greases the skids
from old to new because if you’re gonna change pricing, you gotta figure out how not to trap
legacy customers because what you really wanna have happen is you launch the new model,
you stimulate new profits and new revenue for new customers and then your older customers
over time say, man, that new offer has a ton of value in it.
I’d like to upgrade. And of course, upgrade is more revenue. And if you remember your water
skiing days or wake boarding days, there’s a thing called the double up.
There’s a pattern that you drive the boat in and two waves collide and create this really big
wave that you can do all these crazy tricks and jumps on it. So we were trying to create that
double up and it’s very hard to create that double up if the price differentials are too big, but
now we can do that.
[Speaker 2] (26:12 – 28:15)
Yeah, interesting. When you’re describing not the double up, the double up, I can tell you, I can
assure you I’ve never made it. I could barely stand up on water ski, so I’ve never made it
through a double up.

But yeah, I’ll kill myself. But as you’re describing that, there are companies I’ve spoken to
historically that they’re going through the transition you’re talking about. I had all this on-prem
software.
I now have SaaS. I’m moving customers over. And about halfway through that, they’re calling or
saying, hey, Tom, what are other companies doing?
Because I’ve got this customer who has a bunch of stuff they’re not really using that we sold
them in the old model. It’s shelfware. I’m moving them to the new model, and they don’t need
it, so they’re not gonna move that stuff over to a subscription, right?
And how do I basically keep the ARR the same? And so they’re describing this, and I said, let me
play that back. I said, you’re asking me what’s the best practice to get people to pay for stuff
they’re not using.
I just wanna make sure that’s the ask, right? And they’re like, well, no, that’s not what I’m
asking. I’m like, no, that’s what you’re asking, right?
But I think that I’m telling you that story because I think there are a lot of software companies
who have just been stumbling through that transition of what is the model, what you just
described, what is the optimal pricing model to move legacy to the new, to keep as much
money on the table as possible, to keep it fair to the customers, like you said, to incent the
ones, et cetera. And I think it has been a very blunt instrument for a lot of these software
companies, and about halfway through the transformation, they start to have these oh shit
moments around their revenue because they’re like, oh, this new model is, and we’ve been
screaming this for years, like folks, you have to understand, the margin profile of SaaS is lower
than on-prem software for a whole host of reasons. And so what is your pricing model and your
business model?
How are you accounting for that, right? Most people did not. They did very rough math and just
got caught in the middle of it.
And I’m sure you get involved with companies that are halfway through it, starting to realize
they have a problem, then that’s when they’re calling you going, hey, this isn’t working out for
us.
[Speaker 1] (28:15 – 29:59)
Yeah, and so that’s a terrible scenario and a hallmark of what happens when the homework
assignment wasn’t done. And specifically, that homework assignment was taking our firm
hundreds of hours of Excel and using Excel for like, think about it, like order level data. You’ve
got a header, deal record, and details and SKUs climbing out your noses.
Like to do that in Excel is absurd and it’s just not conducive to doing what we call an impact
analysis. And that impact analysis is something that we compute automatically in our software.

So every model we ever do just carries, it just gears every decision we ever make is that impact.
And so we can have a very discreet conversation around those impacts, where they occur, what
channel, what customers, what are the characteristics? And you wanna do that before you start.
So the worst case scenario is you made a bunch of assumptions, you start converting, and then
the revenue isn’t there.
Now, when we get a call, we have to deal with old world pricing, which probably has an event or
two of a price change in its history. Plus you’re a quarter or halfway through this event, which is
yet another drama thing to deal with in the customer base. And now you gotta correct the ship
and come out with the future.
So you’ve got like multiple points to kind of converge. And oh, by the way, you’ve got to do that
in a way that customers don’t get really pissed off. And it’s really hard to tease out causality in
that scenario because, and this is where the analytics come into play.
You’ve gotta get something that says, is that new thing that I’m creating better than the old
thing that I had? So for example, just literally, we’re in an engagement right now. This happens
to be from a competitor’s recommendations.
We consumed the price points, everything else, ran it through the, and we can do this very
quickly.