November 5, 2025 • 41 mins
In this episode of Let’s Talk Pricing, PPS President Kevin Mitchell sits down with Kiran Gange, Founder and CEO of RapidPricer, to explore how AI and analytics are transforming retail pricing across Europe and beyond.
With nearly two decades of experience in retail, Kiran shares his perspective on how automation, data, and ethical AI are reshaping the way pricing teams forecast demand, manage promotions, and stay compliant in a rapidly evolving environment.
They discuss practical strategies for integrating AI into pricing workflows, balancing human intuition with algorithmic precision, and ensuring innovation doesn’t come at the expense of compliance.
How AI and analytics are changing retail pricing and promotions
Balancing automation with human judgment in pricing decisions
Practical approaches to improve demand forecasting
Real-world examples of AI-driven pricing success
Navigating VAT, GDPR, and ethical AI in European markets
Building compliance-ready pricing frameworks
How non-technical professionals can start with AI and analytics
What’s next for retail pricing innovation
Kiran Gange is the Founder and CEO of RapidPricer, headquartered in Amsterdam. With nearly 20 years of experience in retail pricing and analytics, Kiran has helped retailers across Europe and beyond use data and AI to optimize decisions, improve margins, and create smarter pricing systems. He’ll also be leading the workshop AI & Analytics in Retail Pricing at PPS profitABLE: Barcelona, this December.
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Episode Transcript
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(00:00):
You're listening to Let's Talk Pricing, your connection to the
voices, stories, and strategies shaping the pricing world.
Each episode, we go beyond theory into the practical,
timely conversations that help you lead with confidence and
drive results. Let's start the show.
Hello everyone, welcome to Let'sTalk Pricing, the official
(00:21):
podcast of the Professional Pricing Society.
My name is Kevin Mitchell from PPS, and I'm your host today for
a great episode. I'm very excited that we're
talking to Karen Gangi today. And with Karen, we're going to
talk about AI, artificial intelligence.
We're going to talk about analytics in pricing,
(00:42):
concentrating on some retail items, and we're going to talk
about how these tools are transforming the way that
companies make decisions, managedemand for their marketplace and
stay compliant in an increasingly complex market.
And today, as I mentioned, very happy that joining me is Karen
(01:02):
Ganga. Karen is the founder and CEO of
Rapid Pricer, which is based in Amsterdam.
He has nearly two decades of experience helping retailers
across Europe and elsewhere makesmarter data-driven pricing
decisions. And also, I'm very happy to talk
with Karen because I'm going to see him in a little while in
(01:24):
Barcelona at the upcoming PPS Profitable conference.
And Karen's going to be leading a workshop there for us that is
focused on AI and analytics in pricing.
So Karen, welcome to Let's Talk Pricing.
It's great to have you with us here today.
Thank you. Thank you so much, Kevin.
Always a pleasure working with you and the PPS team.
(01:46):
I'm very much looking forward tomeeting you and everybody else
in Barcelona. We made so many friends together
over the years, but so much of the industry knowledge is coming
from there. I learn a lot and hopefully I
can also give back to the audience there on what I have
learned in the last 20 years like you just said.
All right, excellent. And of course, yeah, we love it
(02:06):
when our experts are especially driven to give back.
So definitely looking forward toseeing you again, looking
forward to your workshop. And also please check
pricingsociety.com because Karenhas a few online pricing courses
with us as well. So lots of great information
there. And Karen, I want to ask a
little bit about your backgroundhere.
(02:30):
And I know that you've spent a lot of time around pricing,
around analytics, but what firstdrew you to this very important
field? And how did you decide to to
start your how did you decide, I'm sorry to start your company
rapid pricer. So I grew up to be an engineer
in India because I guess you have to be an engineer or your
(02:52):
parents aren't very happy about it.
But but then I realized I liked,you know, like doing all kinds
of marketing activities. I would organize college
festivals, participate more in the sporting events, etc.
Then I went on to get my MBA in California.
Kevin and I had the most fantastic set of professors.
(03:13):
I really like my university, a tiny university called the
Pepperdine University. And I found all my marketing
courses were like really interesting and I did a lot of
projects and I connected back tosome of the retail experiences
I've had growing up working in tiny stores here and there to
make pocket money when I was a kid.
And then coincidentally or not, I got my first job with
(03:37):
Demantech. You might be familiar with this
company. I made so many friends.
We had so much fun working with Demantech in my first few years.
That's where I was able to connect a lot of my engineering
kind of mathematical technology to my speaking love for speaking
or consulting, right? Those all came together and I, I
(03:58):
did this for two years, 2 1/2 years with Demantech.
I didn't Monday to Thursday, I would go to the East Coast to
Target, to Best Buy, to, you know, Walmart, Staples and then
talk about pricing. Everybody had challenges, but
that kind of started looking similar to me after a while,
right? Everybody goes through the same
journey. Everybody's having the same
(04:18):
channel. This was back in 2008, I
believe. So.
So after that, I had a really cool job.
I got hired by the CEO of Fry's Electronics.
If you heard of Fry's Electronics.
And then he was the best boss I've ever had in my life, right?
Like super smart guy, the president of the American
(04:39):
Institute of Mathematics. He said, Kieran, go back home
and bring all your mathematics books, which you've thrown away.
We're going to connect what you know about pricing with
mathematics. And he said one thing, Kevin, he
said, I don't care what you do as long as you give me results.
And that's when I started a company with mathematicians back
home in Bangalore while I was inCalifornia, giving him what he
(05:03):
wanted, the results he wanted. But I had multiplied myself with
all my friends back home. Same time I went back to
Demantech, said Demantech. Hey, you like what I did for
Demantech? I'll do it.
I'll do it for half the money. Would you like it?
And they said, sure, why not. And that's how my first
entrepreneurial journey started while I had this job with Fry's
Electronics. And in a few years, the company
(05:25):
became big enough to support me full time.
Until then, it was just my team working for me, etcetera.
And that's how I grew from there, right?
So, so I spent about, I would say, nine years growing this
business as a consulting business in the US And then
there came a point in time whereI had to raise money.
Nobody likes consulting businesses anymore.
(05:46):
So I made a product out of my consulting business and I moved
to the Netherlands. And that's how we became about
to Rapid Pricer, which is a product company for all the
experiences I had gained until then.
Kevin, don't know if it was a long answer, but hopefully you
liked it. Oh, no, no, that's a very good
answer and thanks for the detailof your experiences there.
(06:07):
So you're truly a global citizenand also very, very well-rounded
with your background in engineering and software and
marketing. And hey, you always learn
something new every day. I knew that you studied here in
the United States, but I did notknow that you were a Pepperdine
Wave enjoying that beautiful campus out there.
(06:28):
And also, yes, a lot of us who have been around pricing it are,
of course, very, very familiar with Demand Tech and what they
did. And also, I like how you
detailed your experience with some very large retailers that
we've all heard of, like Best Buy and fries and Walmart as
well. So you're very accomplished in a
lot of different areas and that's one thing that we see in
(06:51):
pricing and in revenue management.
A lot of us kind of do have varied backgrounds like that
where we marry up the mathematics, the arts, the
sciences and kind of combine everything in order to do good
work for our customers and for our clientele as well.
So no, thank you very much for detailing the background
(07:12):
information there. Definitely appreciate that.
And Karen, let me ask you from your vantage point, what changes
have we seen in retail pricing in Europe and elsewhere with the
rise of AI, with the rise of artificial intelligence and
automation? How have things shifted with
(07:33):
these new technologies? So I, I earlier part of my
career I spent mostly in the US,right?
So, so I was very much in for a shock when I first spoke to the
Netherlands. This was back in 2017.
So one thing I noticed is there was a lot of early investments
(07:53):
in Europe around technology and hardware, especially in the
smaller, more expensive countries like the Switzerland,
the Nordics, Norway, Sweden, Finland, All of these guys were
early adapters of say, for example, the electronic shelf
label. And they had the automated
stores coming out ahead of the Usus was still like, you know,
(08:18):
it's like the Los Angeles airport.
It's it's not going to become super modern overnight because
it is so large. It's been there for such a long
time. But if you go to Phoenix, it's a
much nicer airport because it came about later and it's a
smaller size to manage, right. So kind of like that the
European retail I see had like faster adoption to the hardware
(08:38):
side of things. But traditionally they were
reluctant to to jump into like, you know, a black box kind of an
approach with the AI. They don't want to trust it yet
they still have to like prove ROI and all of this stuff.
And there were some early moments you saw from you know,
like coop, coop, like some people say it and then I hold,
(09:03):
we did a lot of work with electronic shelf labels and I
started, they're bringing their own AI teams.
We saw a lot of accelerators developing these technologies
with in partnership with some ofthem and their own AI team
started coming into play like a few years, like like 5 years
before now they started doing these experiments and now they
(09:23):
are doing a lot of these processes using AI, which I
really like compared to the US. Again, the large, you know, the
Los Angeles airports of the retail world have also made a
lot of big moves. Now you'll see Walmart is going
ahead with full implementations in many places with electronic
shelf labels. Target is doing a phenomenal job
(09:45):
of bringing the eye to how theirassortment works, how their
customer experiences work, but it is still limited to those
pockets. I believe.
You know, there is so many othermid size, small size retailers
who still don't go with technology changes so much.
It is still a very slow change to make.
That's that I feel is the difference between the two.
But now AI as a term is no longer like, you know, a big
(10:10):
decision you have to make. People have started using AI
like bringing your phone out of your pocket kind of situations,
right? You do a quick query about your
database, see what it has to say.
What trends are you identifying all of these steps?
And in the back end, AI is beingused everywhere to crunch data,
to store data, to give you information quickly.
All of that is being done very quickly, but more in terms of
(10:33):
what promotions to make, what pricing to make those decisions
are being done a little bit morein house these days than, you
know, trusting like a system like we used to trust a demand
tech in the past to say, take mykeys and doing my pricing.
That has changed to we're going to get enough knowledge
ourselves and control this little piece one step at a time.
(10:55):
That's that's the big differenceI've seen in the evolving of the
use of VR, Kevin. Yes, it's quite a change.
And thank you for mentioning that a lot of the advancements
and a lot of the new technologies have taken place
around the world. I mean, things like this can
come from anywhere. It doesn't only have to come
(11:17):
from the largest economies or the biggest companies as you
mentioned or something like that.
Sometimes these new innovations,these new advancements can come
from mid size companies in mid size countries and then expand
up, down and sideways and all around.
And I remember travelling in Europe many years ago and being
(11:40):
a little bit surprised at seeingelectronic tags on shelves at a
car for in Paris or something like that, years before we had
anything like that here in the USA.
So the world is in that case getting smaller.
And of course, technology can flow through much more quickly
from one part of the globe to another nowadays.
(12:01):
And also, I like how you talked about how AI essentially is
letting people bring some of these decisions, some of these
best practices, strategies, tactics in house too, where you
can use your own data and make the decision that's best for you
in that way. So thank you for the explanation
about that. And I know that you are an
(12:22):
expert in all the areas we're talking about, you know,
analytics, pricing with large retailers and others, but we
still are seeing new changes with how retailers are
approaching pricing today. So your workshop with us I
believe is going to focus on some of the new initiatives and
(12:43):
new things coming out there. So what can we look forward to
with some of that information? What are some of the new things
where we're seeing AI and analytics transform retail
pricing? So, so one of the focus I want
to have in this workshop is to know what is possible with AI,
right? Where can you go eventually?
(13:05):
Who has seen success in what areas of using AI and where has
AI not performed as well as people expected to at the same
time? Let us start quickly with using
this from step one, don't wait to, you know, hire APHD with
artificial intelligence degree to come in and start doing
something. There are many low hanging
(13:28):
fruits that AI is, you know, enabled everybody to to to enjoy
right now. So let us start with that within
your pricing functionalities, how can you use it and also get
a basic understanding of how AI works.
It is not rocket science, it is something you and I can
understand, you and I can connect to.
Very similar to how we make decisions as human beings is how
(13:51):
AI works to give them that kind of a comfort on using this new,
new tool. It shouldn't feel like it is out
of reach. And maybe one day we will get
some fancy doctor to come in anddo all of this work.
Now let us start right now with where we are.
At the same time, have a goal for where we could go and how do
(14:11):
you reach there? What do you need at what stage
of your stage to get there? So again, like I do always,
Kevin, I really try to understand where each of the
participants stand and then try to make it relevant to them
through their own discussions. And then sometimes there's such
a rich amount of knowledge coming into the workshop through
the participants themselves, they end up teaching each other,
(14:34):
or, hey, this worked for me or this didn't work for me also,
and I learned at the same time. So I'm really looking forward to
this kind of a discussion again,this time in Barcelona.
Definitely, we're always in favor of the two way transfer of
information there. And I do hear sometimes from
people who attend our workshops and similar events that of
course you learn a lot from the presenters, but also you can
(14:57):
gain from your colleagues, your peers and their best practices
as well. And sometimes things that work
well in one industry may not translate 100% to a different
industry, but they're always lessons you can learn from
different industries, different verticals, different
geographies, different company types and things like that.
(15:17):
So yes, we try to get everyone together and work on some best
practices and some strategies there.
And Karen, another question for you.
When we talked about AII know that we as human beings, we
acknowledge that there's some things with large amounts of
data that AI might be able to domore quickly, more efficiently
(15:39):
than a human being. But there are also certain
things, as you alluded to earlier, where that human
intuition, the grace cells appear, where we still of
course, have lots of advantages over AI, where you see can see
areas where AI does not do a perfect job.
So since we are human beings andsince some of us have inertia
(16:01):
and maybe a little bit of resistance to change, how can we
balance AI and the new tools andtechnologies with human
intellect in order to make a kind of a great combined process
going forward? What's the best way for us to
overcome maybe some initial fears and to move forward using
AI and automation? So this is, again, depends on
(16:28):
what they were speaking about, right, Kevin?
Today we're speaking about one level of maturity with AI.
Next year, it'll be a completelydifferent story, again, with
what AI can do, say, for example, right?
But right now, everybody should definitely use AI to make their
own routine tasks automated, OK,whatever can take time in terms
(16:49):
of manual processing, cleaning up the data, getting to some
level of insights. All of this can be easily
automated using AI. I'll give you an example.
Just last week, I had a client who was looking to revamp their
strategy for 2026, right? They wanted to see who they
should compete with and should they go after margin or revenue
(17:11):
or market share. And then how is it different for
each category, for each zone, etcetera, etcetera, right.
So this information, we already had market share data, we had
point of sale data. Very quickly using AI, you can
find correlations or negative correlations between your market
share and competitive prices, right?
(17:32):
And those are numbers which are not lies.
You've seen your market share change when your price against
the competition, relative price to competition change by this
much, which is why the AI immediately, not immediately in
a quick algorithm will tell you who matters to you and by how
much. Now the human comes in, it takes
(17:53):
a looks at it and then he also knows other things that AI
doesn't know. He might know that XYZ
competitor is very aggressive orcompetitor ABC has new
investments and that's why they're investing money to do
XYZ. All of that information you can
feed into AI and ask you to makebetter decisions.
But it is we are in a much more powerful place now with AI
(18:15):
supplementing your own human knowledge and experience to make
the decision. And again, you can bring back AI
saying that AI, I want you to make sure you get me market
share here while staying competitive with this person and
make sure you get me this much margin while making sure my
private label grows by X percent.
Give me the right price and it gives you a price.
(18:38):
Don't trust it yet. You know, review it, check it
with your category managers, check it with your vendors to
see if somebody's going to be upset about it.
Somebody's Commission is going down.
The AI doesn't know that. Then you can implement it, bring
back an AI to say continuously monitor this and let me know
when I screwed up, that that is where we are right now if you
(18:59):
really use it in the right way. But with agentic AI, blah, blah,
blah. There's so many changes coming
in and AI is getting smarter andsmarter and smarter.
We will be able to give more keys to AI, but we still need to
be responsible for what AI is doing.
Otherwise, you could lose your job, you know, if you're not
careful about it. Definitely, yes.
(19:21):
So we'll still need essentially the human being to drive the car
essentially, so to speak, but that car will go much more
quickly and much more efficiently than the human being
walking alone, so to speak. So understood there.
And I know with a lot of companies, some of the most
(19:43):
critical areas that we are facing now in these economic
times are we want to forecast our demand accurately and we
want to make sure that our promotions are the most
efficient, the most effective that they can be.
So how can retailers make sure that these very two important
(20:04):
facets of their companies forecasting demand and
optimizing their promotion, their promotions, how can we use
analytics to make sure that we're getting the most in these
two very important areas? So we both with this forecasting
and promotion, right? So basically the quality of the
(20:25):
decision is, is, is the amount of data going in and do you
trust it or not, right. Say for example, we've, we've
had the fantastic models crash when the COVID happened because
the models never knew what a COVID was.
So in that case is still there is a factor, huge factor of
(20:46):
manual intervention coming in. If you know a certain country's
president or Prime Minister is going to start reacting a
certain way, you might change the forecast differently, which
the AI will have no clue about, right.
So I would still, I would still go with using AI to give you a
forecast and you can throw like a bunch of data, right?
(21:09):
You can throw like supply chain data, point of sale data,
weather data, demographic data or it makes it little bit
smarter, little bit smarter, little bit smarter.
But end of the day, it'll still require some elements of you
overriding that forecast or at least taking a look at how why
you saying 20% higher than last year?
(21:29):
I thought it's going to be 10%. Maybe you will dig in a little
bit deeper and get a better forecast, but we're not there
yet where you can say forecast and I'll forget.
Understood. Yes.
And that's a very good point because we as human beings wake
up every day and check our news feeds and see what's going on
(21:52):
with the administration of certain companies, certain
countries and what's going to happen and what's going to drive
our decisions for the day. So certainly understand that a
human needs to be involved with that as much as we can.
So that makes sense. And Karen, I know with your
experience, you've had several situations, many situations
where you've seen AI driven pricing make a great impact on a
(22:17):
retailer or a brand. And at our event, we always like
to look at case studies and examples of success and
sometimes examples of, let's say, not so much success as well
as learning tools. So can you share an interesting
example where AI driven pricing was able to make a great
(22:38):
measurable impact for a company that you're connected with?
Yeah. So again, I must qualify this
term AI driven pricing these days, Kevin, because everything
should be driven by AI to some aspect of it, right.
So it's it's like what we do with pricing, say for example,
analyzing the market share, determining the right strategy
(23:00):
is all AI driven. And then we find the right price
for it basically saying that, OK, we know this price is going
to help you achieve these results.
And we measure it through a automated test versus control,
which is also done by AI on an ongoing basis.
And the measurement of these results in the last three years
(23:20):
averages about 6.7% increase in margin with a very large
retailer we work with, with morethan 1500 stores.
So it's not like I made one decision and this decision gave
me this much money. There are lots of mistakes CI
makes, but the good thing is it quickly corrects itself every
week for its mistakes because ofmachine learning to see if the
(23:44):
price is right or not. So, so those kind of a margin
increases we are consistently seeing across our
implementations. We see like reductions in food
waste when we use the eye to determine what price you should
sell the product at so that it doesn't go bad before you sell
it. And those are immediate,
instantaneous, right? Instead of throwing away 50
(24:05):
bananas, you're probably throwing away two or three
bananas, 5 bananas. So those kind of reductions are
real and all done by AII would say like this.
I wouldn't use the term AI driven pricing anymore.
All pricing should be driven by AI, but by how much AI is the
question we all want to think about.
(24:26):
Yes. And reducing the food waste is a
great case study. And I've seen that examples
presented as a way that pricing really does affect everything.
And since we have the availability to manage pricing
on that micro level with the electronic shelf tags and things
like that, that does give us theopportunity to reduce ways to
(24:49):
make us all more efficient and to basically to be more
connected with the business community and our marketplace as
a whole. So that's a great example.
So thank you very much for that.And Karen, we are going to take
a quick break here. So please stay with us for the
Let's Talk Pricing podcast. We'll be right back with Karen
(25:09):
Gonghay after this message. Thank you so much.
Are you ready to leave the next era of pricing?
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with a purpose. Set in one of Europe's most
vibrant cities, this event connects global pricing leaders
for cutting edge strategy, real world tools and actionable
learning. From AI and dynamic pricing to
(25:32):
value creation and market resilience, you'll gain the
insights to drive profit and lead with confidence.
Be there the 2nd through 5th of December.
Learn more and register now at pricingsociety.com/P PSBCN 25.
Hello, everyone. Welcome back to the Let's Talk
(25:52):
Pricing podcast. Today we are talking with Karen
Gonghay, CEO of Rapid Pricer. We're talking about AI and
analytics in pricing. And Karen, as a global citizen
and someone who founded a company in the Netherlands, I
know that you've addressed some issues such as the value added
(26:14):
tax, the VAT there in Europe, GDPR for data protection, and
some ethical issues around artificial intelligence as well.
Why are these so critical for pricing teams around the world,
specifically in EMEA and what are we seeing where we can
(26:36):
address these very important issues right now?
So let's start with the VAT, right?
So, so it is one thing to say that, OK, if you're going to
sell this product in this market, the VAT is a certain
percent. But sometimes with all the
promotions having the trade funds, we recalculate the
(26:57):
prices, the costs are changing continuously.
It becomes very difficult to stay on top of this, right, that
the calculations have to be updated and say for example, the
tariffs are changing now and etcetera, right.
So all of these we've ran into issues where we have done
pricing and we've realized that we weren't making enough margins
(27:20):
with the optimized prices because of the VAT.
So we had to like change our algorithms to put like a minimum
margin rule after VID to calculate the prices.
So these kind of more like a logistical adaptations to this
changing environment by country,by product group, etc, needs to
be taken care of on a continuousbasis to do make sure we are
(27:44):
compliant and also we're not losing money.
GDPR is a very, very tricky, tricky point to navigate, right?
Because the more data you put into an algorithm, the more
accurate, the better the decisions will be.
But GDPR is there for a reason, so that you don't go into the
(28:05):
privacy and you take data which is not correct, which is not
right for you to take, say, for example, Kevin, we can write an
algorithm today. And I have done this in labs
right now where a certain personwalks in front of a shelf.
And we did this experiment in 2016 in San Francisco.
We had a lab there, right? You, you walk in front of a
(28:27):
shelf and depending on who you are, how tall you are, male,
female, how old you are, whetheryou're happy or not, we're going
to change the price on the shelfbased on exactly who walked in
front of you, in front of the camera.
You can do that, but you don't have to.
You shouldn't do that because not only for GDPR, but also for
(28:48):
ethical considerations, you shouldn't be changing price on
people based on what you look like.
Right, exactly. So that's, that's the, that's
the, you know, temptations which, which you'll have to rein
in to still make use of the data, but without compromising
somebody's privacy. And good thing there is a
government rule on it. But even if there is no rule,
(29:11):
we, as you know, people who develop algorithms and implement
and entrepreneurs, whatever we are, we should also take that
into consideration. Say for example, at rapid price,
we have a rule that we will never take up prices without
notice. Like say, for example, if, if,
if it starts raining, there willbe a promotion on an umbrella
(29:33):
with our algorithms, but it willnever go up.
There is a rule which limits youfrom increasing prices based on
demand. But once in three months, once
in six months, whatever the reason, if you're changing
prices, you can take your price up.
But our algorithm only works to go down or come back to where it
was, not take it up. And there is nobody who told us
(29:53):
we should do that. Well, we do that because
otherwise I can't sleep at nightand you know what I mean?
So, so, so like this. There are many things that
algorithm, the moment you point an algorithm at a profit, it
does, it doesn't have the human values you and I do.
It will start making calculations to give you the
results. We have to step in and find out
(30:16):
if something is getting biased. If something is like giving the
decision which is not good for humankind as a whole, we'll have
to change it ourselves. And GDPR is good to do comply
because otherwise somebody will sue you and you'll go bankrupt.
But they go hand in hand. The ethics and GDPR, I believe
you know, it's always good to have a lawyer to check what
you're doing is right, but also have your own conscience.
(30:40):
Approve an algorithm before it goes live if you can, you know,
and sometimes mistakes do happenand it goes through all the way.
But if it's in your knowledge, it is up to you to correct it.
Absolutely. And we always say that your
prices should be fair. And fair can be rather nebulous
at times, but generally we as human beings are almost
(31:03):
universal when we see something that is unfair.
So fair may be hard to determine, but unfair sticks out
like a sore thumb as we would say.
And what you mentioned about theethics and about even though
about the fact that even though we have the ability to change
prices based on physical appearance or a gender or
(31:24):
something like that, it certainly is not worth it for us
to do that. And this reminds me of the
so-called pink tax that we hear about where certain products
might have an equal, quote, male, UN quote version and a
female version, but the female version, even though it does the
same thing, is much, much more expensive.
(31:45):
So yes, we certainly want to avoid the ethical quandary of
say, if we were selling shampoo or hair products and a woman
approaches versus a man raising the price for a woman.
It is certainly not worth it. We want to make sure that our
prices are fair. And I like your discussion of
the of the VAT because that's one of these things that I think
(32:07):
makes pricing fun where you haveall of these changes and all
these extra dimensions where youhave to make sure that you are
profitable despite different dimensions in one part of the
world versus another. And that's one of the things
that gives us that extra challenge that keeps us on our
toes as well. And of course, we can use all of
(32:27):
the tools to take advantage of that also.
So it's very important that we stay compliant, and it's very
important that companies use best practices that still allow
us to innovate. So Kieran, please tell me some
other best practices that you'reaware of that companies can
(32:48):
follow to say compliant but still be innovative with AI and
with other tools and with their data.
So what are some other ways thatwe can use these tools?
So, so not, not, not everything is related to just associating
purchase with human beings, right?
(33:10):
Say for example, there is a lot of trends you can detect in the
data from what kind of which markets are buying more Greek
yogurt or let's say for example,which markets react more to
rainy weather versus which markets don't right?
Or, or say for example, when youuse a machine vision cameras
(33:31):
instead of Justice optical cameras, you can see a lot more
than I can when it comes to meatand fresh produce, say for
example. And AI can use that information
to deduce how many days it's going to last accurately, which
you might not be able to do while looking at it.
So there is so much you can do with the eye without having to
(33:53):
get into complications of, you know, I know Kieran and Kieran
always likes to have this kind of water, etcetera.
It doesn't have to be like that.You know, there is so much else
you can do. And I also talked about general
logistic things like, you know, processing data, cleaning it up,
making it easily available to access you, you're on your train
(34:14):
on the way to work. And you can just query your own
database saying that, hey, what kind of promotion should I run
this week, make this decision based on whether my inventory
and my future supply. And here I can do that query and
give you the result right now. And there is, there should be no
GDPR, you know, violation in there.
But but always what I've learnedfrom experience is always have a
(34:36):
good lawyer in place to check your policies when possible.
Otherwise, you might not be doing the same job again after a
few years, you know? Definitely, we want to make sure
that we are staying compliant and make sure that everything we
do is legal and fair and just inevery way possible.
So we are due for another break,but please come back and join us
(35:01):
after the break. We'll talk more with Kieran
Ganje from Rapid Pricer. We'll be back with the Let's
Talk Pricing podcast soon. Are you ready to lead the next
day of pricing? Join us this December for PPS
Profitable Barcelona Profitable with a purpose.
Set in one of Europe's most vibrant cities, this event
connects global pricing leaders for cutting edge strategy, real
(35:22):
world tools, an actionable learning.
From AI and dynamic pricing to value creation and market
resilience, you'll gain the insights to drive profit and
lead with confidence. Be there the 2nd through 5th of
December. Learn more and register now at
pricingsociety.com/P PSBCN 25. Hello, everyone.
(35:46):
Welcome back to the Let's Talk Pricing podcast.
I'm Kevin Mitchell and we are still talking with Kieran Gangi,
who's giving us lots of great information about AI, about
analytics. Kieran is the CEO of Rapid
Pricer in the Netherlands and weare very, very happy that he's
going to deliver a full day CPP workshop at Profitable Barcelona
(36:09):
coming up from the 2nd to the 5th of December.
So for more insights and to connect with Karen in person,
make sure to check pricingsociety.com and register
for his workshop for more great information about AI and
analytics and Karen. We have time for a couple more
questions on this edition of Let's Talk Pricing.
(36:30):
But one question is, for those of us who are in pricing, in
revenue management, in sales enablement, in related fields
and we don't have the best technical background yet,
where's the best place to start when we start looking into AI
and analytics if we're rather new to the field?
(36:54):
So very simply, I mean, there are lots of EI engines which we
can all start using, Kevin, say,for example, you could have your
technical team connect your sales database to a querying AI
engine, which could be like a ChatGPT or, or whatever else we
use. And then we could start clearing
(37:16):
the data using AI today ourselves.
Or you could just upload the data to the to ChatGPT and say
that I have just done this. In fact, I was doing this about
15 minutes ago. I'll give you an example.
This is not related to pricing. I was just talking to you about
my farming, right? So I had planted about 3000
pepper plants and some of them were planted before the rain,
(37:40):
some of them after the rain. Some of them were purchased from
a certain vendor, different variety, etcetera.
And I had this report come in saying these many plants have
died from the plantation, right?So I, I had my team build a
spreadsheet, uploaded this into AI and I said, give me an
analysis of this data. Tell me which of these plants
(38:00):
are dying? What is the reason for it?
Is it the vendor? Is it the breed?
Is it the time of plantation? Is it the location of the plant?
And it gave you really good accurate insights immediately.
That is absolutely the latest AIyou can use to really analyze
your own sales data. It doesn't need you to hire
somebody to do something like this, right?
(38:22):
So something like that, you should absolutely start
immediately and then slowly add more sources of data, more
infrastructure using another team, say for example, so that
you can build on these capabilities one step at a time.
All right. So we just learned that in
addition to being a marketing expert, a software expert, a
(38:44):
consultant, a retail pricing master, and also someone who's
up to date on AI and analytics, our guest today, Karen Gangi, is
also an expert in farming and planting and using the data to
take advantage of everything there.
So truly a a master of quite a few trades there.
(39:06):
And Karen, we are of course, very, very excited about your
workshop coming up with us in Barcelona at PPS Profitable.
So what excites you most about the innovations that we're going
to see with AI and analytics? And tell us a little bit about
some other things that your attendees will be able to take
(39:28):
advantage of when they participate in your workshop
with this coming up. Yeah, I think, I think the most
exciting part of the eye and analytics, Kevin, is you don't
really need to understand codingor programming or data
engineering to use AI. And that's going to become more
and more the case going forward,right?
(39:48):
So, so right now, say for example, AI is limited in giving
you information and sometimes itsounds so confident, you don't
know if it's right or wrong, andyou still have to do another
round of research to trust it. All of that is going to improve,
but I'm looking forward to the stage where AI will actually
complete a task. Then just give you here's
(40:11):
something, go do something aboutit.
You know what I mean? So say for example, we will soon
reach a stage where we can say, hey, I want you to run 10
promotions and give me the results of it in at the end of
it, pick the best products, pickthe best price and run 10
promotions and it should be ableto connect all of the
components. The agent take AI, which is
(40:33):
which is more of a buzzword now,which will become more of a
reality in the coming days. And that's what I'm looking
forward to even now say, for example, if I analyze these
spreadsheets, I should be able to say, once you found out the
reason for this, I want you to make calls to these vendors who
didn't give me the right variety.
Or I want you to instruct the people who've planted it to say
(40:55):
that don't do it again, translate it into their language
and have them send me a confirmation e-mail.
All of that should be able to bedone in one AI, you know,
instruction set without having to learn coding, without having
to hire somebody in my team, etcetera.
That's what I'm more excited about.
It will get there very quickly. It will get there and and.
You're listening to Let's Talk Pricing, your connection to the
voices, stories, and strategies shaping the pricing world.
Each episode, we go beyond theory into the practical,
timely conversations that help you lead with confidence and
drive results. Let's start the show.
Hello everyone, welcome to Let'sTalk Pricing, the official
(00:21):
podcast of the Professional Pricing Society.
My name is Kevin Mitchell from PPS, and I'm your host today for
a great episode. I'm very excited that we're
talking to Karen Gangi today. And with Karen, we're going to
talk about AI, artificial intelligence.
We're going to talk about analytics in pricing,
(00:42):
concentrating on some retail items, and we're going to talk
about how these tools are transforming the way that
companies make decisions, managedemand for their marketplace and
stay compliant in an increasingly complex market.
And today, as I mentioned, very happy that joining me is Karen
(01:02):
Ganga. Karen is the founder and CEO of
Rapid Pricer, which is based in Amsterdam.
He has nearly two decades of experience helping retailers
across Europe and elsewhere makesmarter data-driven pricing
decisions. And also, I'm very happy to talk
with Karen because I'm going to see him in a little while in
(01:24):
Barcelona at the upcoming PPS Profitable conference.
And Karen's going to be leading a workshop there for us that is
focused on AI and analytics in pricing.
So Karen, welcome to Let's Talk Pricing.
It's great to have you with us here today.
Thank you. Thank you so much, Kevin.
Always a pleasure working with you and the PPS team.
(01:46):
I'm very much looking forward tomeeting you and everybody else
in Barcelona. We made so many friends together
over the years, but so much of the industry knowledge is coming
from there. I learn a lot and hopefully I
can also give back to the audience there on what I have
learned in the last 20 years like you just said.
All right, excellent. And of course, yeah, we love it
(02:06):
when our experts are especially driven to give back.
So definitely looking forward toseeing you again, looking
forward to your workshop. And also please check
pricingsociety.com because Karenhas a few online pricing courses
with us as well. So lots of great information
there. And Karen, I want to ask a
little bit about your backgroundhere.
(02:30):
And I know that you've spent a lot of time around pricing,
around analytics, but what firstdrew you to this very important
field? And how did you decide to to
start your how did you decide, I'm sorry to start your company
rapid pricer. So I grew up to be an engineer
in India because I guess you have to be an engineer or your
(02:52):
parents aren't very happy about it.
But but then I realized I liked,you know, like doing all kinds
of marketing activities. I would organize college
festivals, participate more in the sporting events, etc.
Then I went on to get my MBA in California.
Kevin and I had the most fantastic set of professors.
(03:13):
I really like my university, a tiny university called the
Pepperdine University. And I found all my marketing
courses were like really interesting and I did a lot of
projects and I connected back tosome of the retail experiences
I've had growing up working in tiny stores here and there to
make pocket money when I was a kid.
And then coincidentally or not, I got my first job with
(03:37):
Demantech. You might be familiar with this
company. I made so many friends.
We had so much fun working with Demantech in my first few years.
That's where I was able to connect a lot of my engineering
kind of mathematical technology to my speaking love for speaking
or consulting, right? Those all came together and I, I
(03:58):
did this for two years, 2 1/2 years with Demantech.
I didn't Monday to Thursday, I would go to the East Coast to
Target, to Best Buy, to, you know, Walmart, Staples and then
talk about pricing. Everybody had challenges, but
that kind of started looking similar to me after a while,
right? Everybody goes through the same
journey. Everybody's having the same
(04:18):
channel. This was back in 2008, I
believe. So.
So after that, I had a really cool job.
I got hired by the CEO of Fry's Electronics.
If you heard of Fry's Electronics.
And then he was the best boss I've ever had in my life, right?
Like super smart guy, the president of the American
(04:39):
Institute of Mathematics. He said, Kieran, go back home
and bring all your mathematics books, which you've thrown away.
We're going to connect what you know about pricing with
mathematics. And he said one thing, Kevin, he
said, I don't care what you do as long as you give me results.
And that's when I started a company with mathematicians back
home in Bangalore while I was inCalifornia, giving him what he
(05:03):
wanted, the results he wanted. But I had multiplied myself with
all my friends back home. Same time I went back to
Demantech, said Demantech. Hey, you like what I did for
Demantech? I'll do it.
I'll do it for half the money. Would you like it?
And they said, sure, why not. And that's how my first
entrepreneurial journey started while I had this job with Fry's
Electronics. And in a few years, the company
(05:25):
became big enough to support me full time.
Until then, it was just my team working for me, etcetera.
And that's how I grew from there, right?
So, so I spent about, I would say, nine years growing this
business as a consulting business in the US And then
there came a point in time whereI had to raise money.
Nobody likes consulting businesses anymore.
(05:46):
So I made a product out of my consulting business and I moved
to the Netherlands. And that's how we became about
to Rapid Pricer, which is a product company for all the
experiences I had gained until then.
Kevin, don't know if it was a long answer, but hopefully you
liked it. Oh, no, no, that's a very good
answer and thanks for the detailof your experiences there.
(06:07):
So you're truly a global citizenand also very, very well-rounded
with your background in engineering and software and
marketing. And hey, you always learn
something new every day. I knew that you studied here in
the United States, but I did notknow that you were a Pepperdine
Wave enjoying that beautiful campus out there.
(06:28):
And also, yes, a lot of us who have been around pricing it are,
of course, very, very familiar with Demand Tech and what they
did. And also, I like how you
detailed your experience with some very large retailers that
we've all heard of, like Best Buy and fries and Walmart as
well. So you're very accomplished in a
lot of different areas and that's one thing that we see in
(06:51):
pricing and in revenue management.
A lot of us kind of do have varied backgrounds like that
where we marry up the mathematics, the arts, the
sciences and kind of combine everything in order to do good
work for our customers and for our clientele as well.
So no, thank you very much for detailing the background
(07:12):
information there. Definitely appreciate that.
And Karen, let me ask you from your vantage point, what changes
have we seen in retail pricing in Europe and elsewhere with the
rise of AI, with the rise of artificial intelligence and
automation? How have things shifted with
(07:33):
these new technologies? So I, I earlier part of my
career I spent mostly in the US,right?
So, so I was very much in for a shock when I first spoke to the
Netherlands. This was back in 2017.
So one thing I noticed is there was a lot of early investments
(07:53):
in Europe around technology and hardware, especially in the
smaller, more expensive countries like the Switzerland,
the Nordics, Norway, Sweden, Finland, All of these guys were
early adapters of say, for example, the electronic shelf
label. And they had the automated
stores coming out ahead of the Usus was still like, you know,
(08:18):
it's like the Los Angeles airport.
It's it's not going to become super modern overnight because
it is so large. It's been there for such a long
time. But if you go to Phoenix, it's a
much nicer airport because it came about later and it's a
smaller size to manage, right. So kind of like that the
European retail I see had like faster adoption to the hardware
(08:38):
side of things. But traditionally they were
reluctant to to jump into like, you know, a black box kind of an
approach with the AI. They don't want to trust it yet
they still have to like prove ROI and all of this stuff.
And there were some early moments you saw from you know,
like coop, coop, like some people say it and then I hold,
(09:03):
we did a lot of work with electronic shelf labels and I
started, they're bringing their own AI teams.
We saw a lot of accelerators developing these technologies
with in partnership with some ofthem and their own AI team
started coming into play like a few years, like like 5 years
before now they started doing these experiments and now they
(09:23):
are doing a lot of these processes using AI, which I
really like compared to the US. Again, the large, you know, the
Los Angeles airports of the retail world have also made a
lot of big moves. Now you'll see Walmart is going
ahead with full implementations in many places with electronic
shelf labels. Target is doing a phenomenal job
(09:45):
of bringing the eye to how theirassortment works, how their
customer experiences work, but it is still limited to those
pockets. I believe.
You know, there is so many othermid size, small size retailers
who still don't go with technology changes so much.
It is still a very slow change to make.
That's that I feel is the difference between the two.
But now AI as a term is no longer like, you know, a big
(10:10):
decision you have to make. People have started using AI
like bringing your phone out of your pocket kind of situations,
right? You do a quick query about your
database, see what it has to say.
What trends are you identifying all of these steps?
And in the back end, AI is beingused everywhere to crunch data,
to store data, to give you information quickly.
All of that is being done very quickly, but more in terms of
(10:33):
what promotions to make, what pricing to make those decisions
are being done a little bit morein house these days than, you
know, trusting like a system like we used to trust a demand
tech in the past to say, take mykeys and doing my pricing.
That has changed to we're going to get enough knowledge
ourselves and control this little piece one step at a time.
(10:55):
That's that's the big differenceI've seen in the evolving of the
use of VR, Kevin. Yes, it's quite a change.
And thank you for mentioning that a lot of the advancements
and a lot of the new technologies have taken place
around the world. I mean, things like this can
come from anywhere. It doesn't only have to come
(11:17):
from the largest economies or the biggest companies as you
mentioned or something like that.
Sometimes these new innovations,these new advancements can come
from mid size companies in mid size countries and then expand
up, down and sideways and all around.
And I remember travelling in Europe many years ago and being
(11:40):
a little bit surprised at seeingelectronic tags on shelves at a
car for in Paris or something like that, years before we had
anything like that here in the USA.
So the world is in that case getting smaller.
And of course, technology can flow through much more quickly
from one part of the globe to another nowadays.
(12:01):
And also, I like how you talked about how AI essentially is
letting people bring some of these decisions, some of these
best practices, strategies, tactics in house too, where you
can use your own data and make the decision that's best for you
in that way. So thank you for the explanation
about that. And I know that you are an
(12:22):
expert in all the areas we're talking about, you know,
analytics, pricing with large retailers and others, but we
still are seeing new changes with how retailers are
approaching pricing today. So your workshop with us I
believe is going to focus on some of the new initiatives and
(12:43):
new things coming out there. So what can we look forward to
with some of that information? What are some of the new things
where we're seeing AI and analytics transform retail
pricing? So, so one of the focus I want
to have in this workshop is to know what is possible with AI,
right? Where can you go eventually?
(13:05):
Who has seen success in what areas of using AI and where has
AI not performed as well as people expected to at the same
time? Let us start quickly with using
this from step one, don't wait to, you know, hire APHD with
artificial intelligence degree to come in and start doing
something. There are many low hanging
(13:28):
fruits that AI is, you know, enabled everybody to to to enjoy
right now. So let us start with that within
your pricing functionalities, how can you use it and also get
a basic understanding of how AI works.
It is not rocket science, it is something you and I can
understand, you and I can connect to.
Very similar to how we make decisions as human beings is how
(13:51):
AI works to give them that kind of a comfort on using this new,
new tool. It shouldn't feel like it is out
of reach. And maybe one day we will get
some fancy doctor to come in anddo all of this work.
Now let us start right now with where we are.
At the same time, have a goal for where we could go and how do
(14:11):
you reach there? What do you need at what stage
of your stage to get there? So again, like I do always,
Kevin, I really try to understand where each of the
participants stand and then try to make it relevant to them
through their own discussions. And then sometimes there's such
a rich amount of knowledge coming into the workshop through
the participants themselves, they end up teaching each other,
(14:34):
or, hey, this worked for me or this didn't work for me also,
and I learned at the same time. So I'm really looking forward to
this kind of a discussion again,this time in Barcelona.
Definitely, we're always in favor of the two way transfer of
information there. And I do hear sometimes from
people who attend our workshops and similar events that of
course you learn a lot from the presenters, but also you can
(14:57):
gain from your colleagues, your peers and their best practices
as well. And sometimes things that work
well in one industry may not translate 100% to a different
industry, but they're always lessons you can learn from
different industries, different verticals, different
geographies, different company types and things like that.
(15:17):
So yes, we try to get everyone together and work on some best
practices and some strategies there.
And Karen, another question for you.
When we talked about AII know that we as human beings, we
acknowledge that there's some things with large amounts of
data that AI might be able to domore quickly, more efficiently
(15:39):
than a human being. But there are also certain
things, as you alluded to earlier, where that human
intuition, the grace cells appear, where we still of
course, have lots of advantages over AI, where you see can see
areas where AI does not do a perfect job.
So since we are human beings andsince some of us have inertia
(16:01):
and maybe a little bit of resistance to change, how can we
balance AI and the new tools andtechnologies with human
intellect in order to make a kind of a great combined process
going forward? What's the best way for us to
overcome maybe some initial fears and to move forward using
AI and automation? So this is, again, depends on
(16:28):
what they were speaking about, right, Kevin?
Today we're speaking about one level of maturity with AI.
Next year, it'll be a completelydifferent story, again, with
what AI can do, say, for example, right?
But right now, everybody should definitely use AI to make their
own routine tasks automated, OK,whatever can take time in terms
(16:49):
of manual processing, cleaning up the data, getting to some
level of insights. All of this can be easily
automated using AI. I'll give you an example.
Just last week, I had a client who was looking to revamp their
strategy for 2026, right? They wanted to see who they
should compete with and should they go after margin or revenue
(17:11):
or market share. And then how is it different for
each category, for each zone, etcetera, etcetera, right.
So this information, we already had market share data, we had
point of sale data. Very quickly using AI, you can
find correlations or negative correlations between your market
share and competitive prices, right?
(17:32):
And those are numbers which are not lies.
You've seen your market share change when your price against
the competition, relative price to competition change by this
much, which is why the AI immediately, not immediately in
a quick algorithm will tell you who matters to you and by how
much. Now the human comes in, it takes
(17:53):
a looks at it and then he also knows other things that AI
doesn't know. He might know that XYZ
competitor is very aggressive orcompetitor ABC has new
investments and that's why they're investing money to do
XYZ. All of that information you can
feed into AI and ask you to makebetter decisions.
But it is we are in a much more powerful place now with AI
(18:15):
supplementing your own human knowledge and experience to make
the decision. And again, you can bring back AI
saying that AI, I want you to make sure you get me market
share here while staying competitive with this person and
make sure you get me this much margin while making sure my
private label grows by X percent.
Give me the right price and it gives you a price.
(18:38):
Don't trust it yet. You know, review it, check it
with your category managers, check it with your vendors to
see if somebody's going to be upset about it.
Somebody's Commission is going down.
The AI doesn't know that. Then you can implement it, bring
back an AI to say continuously monitor this and let me know
when I screwed up, that that is where we are right now if you
(18:59):
really use it in the right way. But with agentic AI, blah, blah,
blah. There's so many changes coming
in and AI is getting smarter andsmarter and smarter.
We will be able to give more keys to AI, but we still need to
be responsible for what AI is doing.
Otherwise, you could lose your job, you know, if you're not
careful about it. Definitely, yes.
(19:21):
So we'll still need essentially the human being to drive the car
essentially, so to speak, but that car will go much more
quickly and much more efficiently than the human being
walking alone, so to speak. So understood there.
And I know with a lot of companies, some of the most
(19:43):
critical areas that we are facing now in these economic
times are we want to forecast our demand accurately and we
want to make sure that our promotions are the most
efficient, the most effective that they can be.
So how can retailers make sure that these very two important
(20:04):
facets of their companies forecasting demand and
optimizing their promotion, their promotions, how can we use
analytics to make sure that we're getting the most in these
two very important areas? So we both with this forecasting
and promotion, right? So basically the quality of the
(20:25):
decision is, is, is the amount of data going in and do you
trust it or not, right. Say for example, we've, we've
had the fantastic models crash when the COVID happened because
the models never knew what a COVID was.
So in that case is still there is a factor, huge factor of
(20:46):
manual intervention coming in. If you know a certain country's
president or Prime Minister is going to start reacting a
certain way, you might change the forecast differently, which
the AI will have no clue about, right.
So I would still, I would still go with using AI to give you a
forecast and you can throw like a bunch of data, right?
(21:09):
You can throw like supply chain data, point of sale data,
weather data, demographic data or it makes it little bit
smarter, little bit smarter, little bit smarter.
But end of the day, it'll still require some elements of you
overriding that forecast or at least taking a look at how why
you saying 20% higher than last year?
(21:29):
I thought it's going to be 10%. Maybe you will dig in a little
bit deeper and get a better forecast, but we're not there
yet where you can say forecast and I'll forget.
Understood. Yes.
And that's a very good point because we as human beings wake
up every day and check our news feeds and see what's going on
(21:52):
with the administration of certain companies, certain
countries and what's going to happen and what's going to drive
our decisions for the day. So certainly understand that a
human needs to be involved with that as much as we can.
So that makes sense. And Karen, I know with your
experience, you've had several situations, many situations
where you've seen AI driven pricing make a great impact on a
(22:17):
retailer or a brand. And at our event, we always like
to look at case studies and examples of success and
sometimes examples of, let's say, not so much success as well
as learning tools. So can you share an interesting
example where AI driven pricing was able to make a great
(22:38):
measurable impact for a company that you're connected with?
Yeah. So again, I must qualify this
term AI driven pricing these days, Kevin, because everything
should be driven by AI to some aspect of it, right.
So it's it's like what we do with pricing, say for example,
analyzing the market share, determining the right strategy
(23:00):
is all AI driven. And then we find the right price
for it basically saying that, OK, we know this price is going
to help you achieve these results.
And we measure it through a automated test versus control,
which is also done by AI on an ongoing basis.
And the measurement of these results in the last three years
(23:20):
averages about 6.7% increase in margin with a very large
retailer we work with, with morethan 1500 stores.
So it's not like I made one decision and this decision gave
me this much money. There are lots of mistakes CI
makes, but the good thing is it quickly corrects itself every
week for its mistakes because ofmachine learning to see if the
(23:44):
price is right or not. So, so those kind of a margin
increases we are consistently seeing across our
implementations. We see like reductions in food
waste when we use the eye to determine what price you should
sell the product at so that it doesn't go bad before you sell
it. And those are immediate,
instantaneous, right? Instead of throwing away 50
(24:05):
bananas, you're probably throwing away two or three
bananas, 5 bananas. So those kind of reductions are
real and all done by AII would say like this.
I wouldn't use the term AI driven pricing anymore.
All pricing should be driven by AI, but by how much AI is the
question we all want to think about.
(24:26):
Yes. And reducing the food waste is a
great case study. And I've seen that examples
presented as a way that pricing really does affect everything.
And since we have the availability to manage pricing
on that micro level with the electronic shelf tags and things
like that, that does give us theopportunity to reduce ways to
(24:49):
make us all more efficient and to basically to be more
connected with the business community and our marketplace as
a whole. So that's a great example.
So thank you very much for that.And Karen, we are going to take
a quick break here. So please stay with us for the
Let's Talk Pricing podcast. We'll be right back with Karen
(25:09):
Gonghay after this message. Thank you so much.
Are you ready to leave the next era of pricing?
Join us this December for PPS Profitable Barcelona Profitable
with a purpose. Set in one of Europe's most
vibrant cities, this event connects global pricing leaders
for cutting edge strategy, real world tools and actionable
learning. From AI and dynamic pricing to
(25:32):
value creation and market resilience, you'll gain the
insights to drive profit and lead with confidence.
Be there the 2nd through 5th of December.
Learn more and register now at pricingsociety.com/P PSBCN 25.
Hello, everyone. Welcome back to the Let's Talk
(25:52):
Pricing podcast. Today we are talking with Karen
Gonghay, CEO of Rapid Pricer. We're talking about AI and
analytics in pricing. And Karen, as a global citizen
and someone who founded a company in the Netherlands, I
know that you've addressed some issues such as the value added
(26:14):
tax, the VAT there in Europe, GDPR for data protection, and
some ethical issues around artificial intelligence as well.
Why are these so critical for pricing teams around the world,
specifically in EMEA and what are we seeing where we can
(26:36):
address these very important issues right now?
So let's start with the VAT, right?
So, so it is one thing to say that, OK, if you're going to
sell this product in this market, the VAT is a certain
percent. But sometimes with all the
promotions having the trade funds, we recalculate the
(26:57):
prices, the costs are changing continuously.
It becomes very difficult to stay on top of this, right, that
the calculations have to be updated and say for example, the
tariffs are changing now and etcetera, right.
So all of these we've ran into issues where we have done
pricing and we've realized that we weren't making enough margins
(27:20):
with the optimized prices because of the VAT.
So we had to like change our algorithms to put like a minimum
margin rule after VID to calculate the prices.
So these kind of more like a logistical adaptations to this
changing environment by country,by product group, etc, needs to
be taken care of on a continuousbasis to do make sure we are
(27:44):
compliant and also we're not losing money.
GDPR is a very, very tricky, tricky point to navigate, right?
Because the more data you put into an algorithm, the more
accurate, the better the decisions will be.
But GDPR is there for a reason, so that you don't go into the
(28:05):
privacy and you take data which is not correct, which is not
right for you to take, say, for example, Kevin, we can write an
algorithm today. And I have done this in labs
right now where a certain personwalks in front of a shelf.
And we did this experiment in 2016 in San Francisco.
We had a lab there, right? You, you walk in front of a
(28:27):
shelf and depending on who you are, how tall you are, male,
female, how old you are, whetheryou're happy or not, we're going
to change the price on the shelfbased on exactly who walked in
front of you, in front of the camera.
You can do that, but you don't have to.
You shouldn't do that because not only for GDPR, but also for
(28:48):
ethical considerations, you shouldn't be changing price on
people based on what you look like.
Right, exactly. So that's, that's the, that's
the, you know, temptations which, which you'll have to rein
in to still make use of the data, but without compromising
somebody's privacy. And good thing there is a
government rule on it. But even if there is no rule,
(29:11):
we, as you know, people who develop algorithms and implement
and entrepreneurs, whatever we are, we should also take that
into consideration. Say for example, at rapid price,
we have a rule that we will never take up prices without
notice. Like say, for example, if, if,
if it starts raining, there willbe a promotion on an umbrella
(29:33):
with our algorithms, but it willnever go up.
There is a rule which limits youfrom increasing prices based on
demand. But once in three months, once
in six months, whatever the reason, if you're changing
prices, you can take your price up.
But our algorithm only works to go down or come back to where it
was, not take it up. And there is nobody who told us
(29:53):
we should do that. Well, we do that because
otherwise I can't sleep at nightand you know what I mean?
So, so, so like this. There are many things that
algorithm, the moment you point an algorithm at a profit, it
does, it doesn't have the human values you and I do.
It will start making calculations to give you the
results. We have to step in and find out
(30:16):
if something is getting biased. If something is like giving the
decision which is not good for humankind as a whole, we'll have
to change it ourselves. And GDPR is good to do comply
because otherwise somebody will sue you and you'll go bankrupt.
But they go hand in hand. The ethics and GDPR, I believe
you know, it's always good to have a lawyer to check what
you're doing is right, but also have your own conscience.
(30:40):
Approve an algorithm before it goes live if you can, you know,
and sometimes mistakes do happenand it goes through all the way.
But if it's in your knowledge, it is up to you to correct it.
Absolutely. And we always say that your
prices should be fair. And fair can be rather nebulous
at times, but generally we as human beings are almost
(31:03):
universal when we see something that is unfair.
So fair may be hard to determine, but unfair sticks out
like a sore thumb as we would say.
And what you mentioned about theethics and about even though
about the fact that even though we have the ability to change
prices based on physical appearance or a gender or
(31:24):
something like that, it certainly is not worth it for us
to do that. And this reminds me of the
so-called pink tax that we hear about where certain products
might have an equal, quote, male, UN quote version and a
female version, but the female version, even though it does the
same thing, is much, much more expensive.
(31:45):
So yes, we certainly want to avoid the ethical quandary of
say, if we were selling shampoo or hair products and a woman
approaches versus a man raising the price for a woman.
It is certainly not worth it. We want to make sure that our
prices are fair. And I like your discussion of
the of the VAT because that's one of these things that I think
(32:07):
makes pricing fun where you haveall of these changes and all
these extra dimensions where youhave to make sure that you are
profitable despite different dimensions in one part of the
world versus another. And that's one of the things
that gives us that extra challenge that keeps us on our
toes as well. And of course, we can use all of
(32:27):
the tools to take advantage of that also.
So it's very important that we stay compliant, and it's very
important that companies use best practices that still allow
us to innovate. So Kieran, please tell me some
other best practices that you'reaware of that companies can
(32:48):
follow to say compliant but still be innovative with AI and
with other tools and with their data.
So what are some other ways thatwe can use these tools?
So, so not, not, not everything is related to just associating
purchase with human beings, right?
(33:10):
Say for example, there is a lot of trends you can detect in the
data from what kind of which markets are buying more Greek
yogurt or let's say for example,which markets react more to
rainy weather versus which markets don't right?
Or, or say for example, when youuse a machine vision cameras
(33:31):
instead of Justice optical cameras, you can see a lot more
than I can when it comes to meatand fresh produce, say for
example. And AI can use that information
to deduce how many days it's going to last accurately, which
you might not be able to do while looking at it.
So there is so much you can do with the eye without having to
(33:53):
get into complications of, you know, I know Kieran and Kieran
always likes to have this kind of water, etcetera.
It doesn't have to be like that.You know, there is so much else
you can do. And I also talked about general
logistic things like, you know, processing data, cleaning it up,
making it easily available to access you, you're on your train
(34:14):
on the way to work. And you can just query your own
database saying that, hey, what kind of promotion should I run
this week, make this decision based on whether my inventory
and my future supply. And here I can do that query and
give you the result right now. And there is, there should be no
GDPR, you know, violation in there.
But but always what I've learnedfrom experience is always have a
(34:36):
good lawyer in place to check your policies when possible.
Otherwise, you might not be doing the same job again after a
few years, you know? Definitely, we want to make sure
that we are staying compliant and make sure that everything we
do is legal and fair and just inevery way possible.
So we are due for another break,but please come back and join us
(35:01):
after the break. We'll talk more with Kieran
Ganje from Rapid Pricer. We'll be back with the Let's
Talk Pricing podcast soon. Are you ready to lead the next
day of pricing? Join us this December for PPS
Profitable Barcelona Profitable with a purpose.
Set in one of Europe's most vibrant cities, this event
connects global pricing leaders for cutting edge strategy, real
(35:22):
world tools, an actionable learning.
From AI and dynamic pricing to value creation and market
resilience, you'll gain the insights to drive profit and
lead with confidence. Be there the 2nd through 5th of
December. Learn more and register now at
pricingsociety.com/P PSBCN 25. Hello, everyone.
(35:46):
Welcome back to the Let's Talk Pricing podcast.
I'm Kevin Mitchell and we are still talking with Kieran Gangi,
who's giving us lots of great information about AI, about
analytics. Kieran is the CEO of Rapid
Pricer in the Netherlands and weare very, very happy that he's
going to deliver a full day CPP workshop at Profitable Barcelona
(36:09):
coming up from the 2nd to the 5th of December.
So for more insights and to connect with Karen in person,
make sure to check pricingsociety.com and register
for his workshop for more great information about AI and
analytics and Karen. We have time for a couple more
questions on this edition of Let's Talk Pricing.
(36:30):
But one question is, for those of us who are in pricing, in
revenue management, in sales enablement, in related fields
and we don't have the best technical background yet,
where's the best place to start when we start looking into AI
and analytics if we're rather new to the field?
(36:54):
So very simply, I mean, there are lots of EI engines which we
can all start using, Kevin, say,for example, you could have your
technical team connect your sales database to a querying AI
engine, which could be like a ChatGPT or, or whatever else we
use. And then we could start clearing
(37:16):
the data using AI today ourselves.
Or you could just upload the data to the to ChatGPT and say
that I have just done this. In fact, I was doing this about
15 minutes ago. I'll give you an example.
This is not related to pricing. I was just talking to you about
my farming, right? So I had planted about 3000
pepper plants and some of them were planted before the rain,
(37:40):
some of them after the rain. Some of them were purchased from
a certain vendor, different variety, etcetera.
And I had this report come in saying these many plants have
died from the plantation, right?So I, I had my team build a
spreadsheet, uploaded this into AI and I said, give me an
analysis of this data. Tell me which of these plants
(38:00):
are dying? What is the reason for it?
Is it the vendor? Is it the breed?
Is it the time of plantation? Is it the location of the plant?
And it gave you really good accurate insights immediately.
That is absolutely the latest AIyou can use to really analyze
your own sales data. It doesn't need you to hire
somebody to do something like this, right?
(38:22):
So something like that, you should absolutely start
immediately and then slowly add more sources of data, more
infrastructure using another team, say for example, so that
you can build on these capabilities one step at a time.
All right. So we just learned that in
addition to being a marketing expert, a software expert, a
(38:44):
consultant, a retail pricing master, and also someone who's
up to date on AI and analytics, our guest today, Karen Gangi, is
also an expert in farming and planting and using the data to
take advantage of everything there.
So truly a a master of quite a few trades there.
(39:06):
And Karen, we are of course, very, very excited about your
workshop coming up with us in Barcelona at PPS Profitable.
So what excites you most about the innovations that we're going
to see with AI and analytics? And tell us a little bit about
some other things that your attendees will be able to take
(39:28):
advantage of when they participate in your workshop
with this coming up. Yeah, I think, I think the most
exciting part of the eye and analytics, Kevin, is you don't
really need to understand codingor programming or data
engineering to use AI. And that's going to become more
and more the case going forward,right?
(39:48):
So, so right now, say for example, AI is limited in giving
you information and sometimes itsounds so confident, you don't
know if it's right or wrong, andyou still have to do another
round of research to trust it. All of that is going to improve,
but I'm looking forward to the stage where AI will actually
complete a task. Then just give you here's
(40:11):
something, go do something aboutit.
You know what I mean? So say for example, we will soon
reach a stage where we can say, hey, I want you to run 10
promotions and give me the results of it in at the end of
it, pick the best products, pickthe best price and run 10
promotions and it should be ableto connect all of the
components. The agent take AI, which is
(40:33):
which is more of a buzzword now,which will become more of a
reality in the coming days. And that's what I'm looking
forward to even now say, for example, if I analyze these
spreadsheets, I should be able to say, once you found out the
reason for this, I want you to make calls to these vendors who
didn't give me the right variety.
Or I want you to instruct the people who've planted it to say
(40:55):
that don't do it again, translate it into their language
and have them send me a confirmation e-mail.
All of that should be able to bedone in one AI, you know,
instruction set without having to learn coding, without having
to hire somebody in my team, etcetera.
That's what I'm more excited about.
It will get there very quickly. It will get there and and.
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