All right. Well, hello and thank you all
for tuning into another episode of the Professional Pricing
Society podcast. My name is Terence, and today
we're going to be discussing B2B pricing in the era of AI and
data science. Spearheading this conversation
is a director at Gap named Vivek Anand.
Vivek is an operations research professional with over a decade
of expertise in leveraging machine learning and AI and
pricing analytics and optimization.
In his previous role as the Director of Science at a leading
B2B pricing vendor, Vivet cultivated an impressive track
record where he successfully developed analytically driven
pricing solutions that led to revenue and profit growth for
numerous Fortune 500 companies across various traditional B2B
industry verticals. Additionally, his expertise
extended to the niche industry segments such as oil and gas,
legal advisories and subscriptions and HR solutions,
where he innovative price optimization frameworks for
these industries. Vivek, how are you doing today?
I'm pretty good. It's great to be on this podcast
with you. It's really a leading
marketplace for pricing ideas. Yep, great to be with you.
Good, good. We're glad to have you.
Let's go ahead and just dive right into this conversation.
B2B pricing in the era of AI and data science.
So can you share with our audience a bit about your
professional journey and delve into the specific ways your
expertise and B2B pricing has kind of evolved over the years,
just to kind of give everyone a better grasp of your background?
Yeah, absolutely. So currently I'm Director of
Advanced Analytics at a Fortune retailer Gap where my team's
managed really is to optimize business outcomes.
It could be profitability, productivity, actually it's both
for use of analytics for the enterprise.
So basically my team primarily deals with application of data
science analytics in pricing analytics, inventory analytics
and buying analytics. And like I said, prior to my
current role, I was Director of Science at a leading pricing
solution provider. And in that role I worked with a
number of B to B clients across various verticals where I help
kind of build or improve upon their existing pricing programs
by developing machine learning driven practice optimization
solution. I kind of also worked with like
you said, niche industries like oil and gas and subscription
legal solutions where like you did not find many academic or
industrial scientific literature on how to use data science
analytics to optimize prices. And to your second question
about like how it has evolved, When I started in this industry
like 5-7 ten years ago, it was like slightly different.
The data science uses were like not prevalent.
A typical price optimization solution like still constitute
segmentation, computing, elasticity and optimization.
But all of those 3 components have evolved over time, right?
So previously when I started segmentation, attributes were
kind of divided decided by the business stakeholders like they
would feel like, hey these attributes are important.
They will have like a short list of attributes and you use
primarily, primarily use like basic or simple statistical
techniques to identify like which of those attributes made
sense and which one of those did not.
And similarly when you go to elasticity again it used to be
like a regression and still is a regression can be log log
regression, linear regression. But those methods are not always
appropriate all the time. So for example there have been
cases where you do not have enough data and in that case
you're trying to create a regression.
Then like a few months in, you have new sets of data come in
and all of a sudden your results are different because like 1
outlier can change it, right. So it's still a robust method,
but the way it was used traditionally was not
appropriate, it's not optimal if you will.
And similarly like price optimization, like there is this
challenge industry, but there are many, many places where this
price optimization is basically a rules based price setting,
right? Like OK, when this happens to
this kind of thing, right. So when I started, what I did
was I started experimenting with data science algorithm.
So basically to get these things done faster and better, so I
developed a new machine learning framework that would quickly
assess a battery of segmentation attributes.
And then we are not just limited by what the business stakeholder
thought, they were important, so we could run data analysis EDA
quickly, faster to understand like OK, these are the
dimensions on which you can slice the data and then also
assess their attributes. Sometimes like the business, the
hypothesis that we get from the business is not visible in the
data and we are coming up with better attributes.
So this machine learning framework can assess a battery
of attributes in a relatively short amount of time.
Similarly for elasticities, I brought in like newer and faster
techniques. I need to talk more about these
like later, like faster sampling algorithms that could converge
and reduce the run times. And scale is a big problem,
right? Like 5-10 years ago, you are
primarily dealing with a small amount of data on transactions,
customers, products. That's about it, right?
And now we have a lot of data, right.
So how do you go from having a method that was suited for a
small amount of data to a large amount of data?
So this is like net net the cultural shift where the
clients, the solution providers and the end users are kind of
not shying away from using data science and analytics and
leaning on machine learning, data science to make faster and
smarter decisions. That's like overall tectonic
change that has happened in the landscape.
OK, awesome. That's cool.
Now, you do have a very extensive background in this,
even though things have changed in the past five to 10 years and
you've been at the forefront of applying innovative data science
and AI techniques in pricing. So, starting with segmentation,
do you mind walking us through some fascinating instances where
you have kind of harnessed AI and data science to create a
more robust segmentation solution?
Yeah, yeah, absolutely. And this is, this is such a
great topic. Actually there are many such
examples of it. And as we speak, like the one
that comes to my mind is a customer where like they have
like a traditional segmentation model.
The customer had implemented a customer segmentation model for
their own customers across 2 dimensions, like how much their
customers spent money over a year and what is the price they
were like paying, right? Like a price index, which
effectively kind of measures like, hey, this person paid
below the average price paid by all customers or above, right.
And if you think about these two attributes, think of it as Skype
plot and these two dimensions will create like 4 quadrants,
right? You break the customer base by
spent and then you further break it by the price index, right?
So you have 4 bad clusters or or or segments, if you will.
Now the salesperson used to come to them and say, like, hey, I
don't really like this pricing because I feel like these two
customers are selling situations are similar.
I feel the price guidance for one is very low and we are
leaving money on the table, whereas others is very high.
And we are kind of like at the risk of not winning the
business, right. And this was a problem that like
kept on repeating and and that sort of also erodes the trust
from the end user, the person who is using the pricing
solution. So the customer like the
business sponsor came to us and asked like, hey, like this is
what we are hearing. And I kind of agree with what
they're saying is in their argument like, can you like make
sense of why it is happening. So again, like looking at the
transaction data, it's very difficult to kind of like have a
scarab water and eyeball and say like, does it make sense?
So that was like one of the first use case of data science,
like years ago where I just like ran a unsupervised crossing
algorithm. So basically instead of telling
the system like how many clusters of segments of data you
need to create, go figure how many should be there.
And the machine came back and said like, OK, there are optimal
5 clusters instead of the four that the traditional
segmentation tree was creating, right?
And then we did some further analysis and we found that there
was a cluster of customers or transactions that were not the
top spenders or not the bottom spenders.
They were right in the middle. And then there was another set
of customers that were like not paying the top dollar but also
not paying the rock bottom. So they were like right in the
middle. And you partition that middle
cluster into four segments, the way the traditional segmentation
was done, 2 customers with a very similar selling situation,
one goes into a high price cluster, other goes in a low
price cluster, right. And that was the source of the
problem. So we did some further analysis
and work with the customer to kind of give them like hey,
instead of like doing this customer segmentation across
these two attributes like in the segmentation tree maybe we have
one attribute that like clusters customers and you have those
five clusters more a data science driven cluster
segmentation and that like low behold the problem was kind of
solved, right. So that was one good example.
Another good example actually goes back to customer specific
price, B to B, like as you know, it's very negotiated bilateral
conversations, right. So segmentation will give you a
segment price, right? But again, there are situations
within the segment that there are customers who pay the rock
bottom or below the floor price. Like in typical price guidance,
you have a stock price which is like the best price that you
expect to get. There's a floor price before
below which you don't want to go and then there's a target price
right in the middle, right. And there will always be
transactions that are below floor.
There will always be transactions that are above
stock, right. And we were dealing with a
customer who was like typically into selling raw materials and
stabilizing products for food and beverage industry and they
were like, hey, I want to build customer specific pricing for my
customers because I feel like hey, we are losing business or
we are not increasing prices fast enough.
And a typical implementation of customer specific prices is like
people look at like, OK, what is this customer within this
segment has paid and where are they related to the segment
prices? They are like way below the
floor price, maybe slowly increase them to floor if
they're between floor and target, increase them close to
target, right. So it's again a rules based
pricing, not a super scientific way of doing it, right.
And the customer was like hey, can you come up with a more
scientific way. So like what we implemented was
like a very hyper focused segmentation based on not the
segmentation attributes that we use to segment the business, but
instead of like within the segment itself, what is excuse
me a hyper segmentation that we can implement.
And I used like machine learning methods for that hyper
segmentation that I'm talking about.
And we were able to leverage like their purchase behaviour,
who are their end customer to assess like how much is their
willingness to pay. And in this example we learned
that all things equal you have 3 customer, 1 customers.
End user is like adult food industry like you and I, right?
Eating like packaged food and then similar customer is making
baby food and similar customer is making pet food.
The willingness to pay for baby food manufacturer and the pet
food manufacturer was significantly higher compared to
adult food manufacturer. Oh wow, that's interesting.
And and in that case what we were able to do is like OK,
instead of going by like where they are in the pricing segment,
let's go by their willingness to pay.
So we were able to give higher price increases to the baby and
pet food manufacturers compared to like adult food
manufacturers. So like these are like hidden
patterns that are like not visible or even like the
traditional segmentation methods will kind of like fail at
identifying these trends and patterns in the data which is
like machines are very good as this.
Sure, that's really cool. That's a good point.
You make leaning on machine learning for these algorithms
and for these hyper focus segmentation components to
further study the willingness of customers and how their their
willingness to I guess purchase certain products.
You know there's there's a lot that goes into segmentation.
But can you highlight a few other instances where you
effectively utilize, you know, AI or data science to tackle
complex pricing challenges? Because it kind of would be
great to hear about the diversity of problems you you
can address in this particular space.
Yeah, absolutely. So I mean one thing I alluded to
like customers, end users willingness to lean on data.
So people are harvesting a lot of data and like previously like
I said, you would have some transaction data, you would have
some customer data and then things like that.
And then you build a model Right now with more and more data
coming in and more and more products being available to the
customers, the scope of data has been really exploded, right.
And if you continue using the traditional methods, then you do
take a significantly longer time to find pressing solution or
recommendations. Like there are two challenges
with the advent or or the adoption of the data culture if
you will, right. One is like the scale of the
problem has become much, much, much bigger and the scope has
become much, much, much bigger. So like there was a time when
like I had a customer who had like millions of segments
because they were talking like millions of skews, right.
And with customers like those, what you have is typically you
have 8020 rule pretty much everyone actually where 80% of
your revenue comes from 20% of your products, right.
And if you were leaning on traditional segment, sorry,
elasticity methods, right, let's just use elasticity as an
example. We don't want to talk
segmentation. And if you're trying to compute
elasticity for those millions of segments, you're talking about
running million regressions, right?
And those are really time consuming.
And again, like I talked about, they're very prone to outliers,
80% of your business, like I said, also 20% of products.
Still, that means that almost a large chunk of your business
doesn't have enough transactions to accurately estimate
elasticity. In that case, what happens is
like if you're running the regressions, first of all for a
thin or sparse segment where you do not have enough data, your
results can swing from one way to other with like injection of
newer data, right? Like we can estimate like, hey,
for this particular SKU, oh it's not elastic and then you have
like few months of transaction data coming and all of a sudden
there is few outlier and it goes from inelastic to super elastic.
That is not good. You don't want any of those.
So. And the second thing is like if
you are having like too much products, compute elasticities
like millions of regressions. It's time consuming right?
So to address these challenges we brought in like 2 methods,
right? One method was to be able to
work with less amount of data. So most of the data science
algorithms are data hungry. They need a lot of data to give
good protections. But there are certain algorithms
that like deal with like less amount of data.
So for example a segment doesn't have enough data.
Instead of like collecting data or climbing above the product
hierarchy and computer regressions or elasticity at a
product, some are higher in the product hierarchy, right?
You could like look at the closest queues that are like
similar to this queue we are dealing with which doesn't have
enough data. So there are scientific
hierarchical methods which will go up and up until it reaches a
certain data density and is able to compute LSCS accurately.
The good thing about these hierarchical regressions which
are like Bayesian in nature, you can have robust results which
doesn't change with like advent of a few data points, sorry,
injection of few data points in the model.
So stable robust results. So that was one set of
algorithms that we implemented like hey algorithms that are
like less data hungry and still have robust results.
The other question or problem was about scale, right.
So previously what you you would do in traditional methods is
just rely on accurately estimating elasticity of top or
faster in skews and just like give some elasticity number at a
higher level for the remaining skews, right.
So for you to be able to run that regression, you need a
method. So like traditional older
methods that and this elasticity is relied on algorithms that
used to take a long amount of time.
Let's just put like in some cases 50 hours for a million
regressions. Yeah yeah.
And we implemented newer algorithms sampling techniques
like. I mean, the new robot sampling
technique is called like a new no U-turn sampling NUTS, NUTS is
able to converge that same 50 hours of work in less than five
hours. So it's almost like you'll see
90% reductions in runtime. And it's basically data science
and machine learning algorithms that learn things faster and
converge to a solution faster. So you can solve the problem of
scale. You can solve the problem of
accuracy being affected by few data points using these
techniques. I can imagine that comes in.
That's very convenient in today's today's world as far as
the amount of data we have in certain industries.
Now you've been instrumental in assisting various companies
including Fortune 500 companies and building and refining their
pricing programs. How do you even measure the
tangible value of your solution? Like how do you measure what
that, what that brings to the table?
Are there any specific metrics or analytical frameworks you can
share with us? Yeah, absolutely.
I mean and honestly like in my experience, not much attention
has been paid to this topic in the bTB space.
Typically companies implement pricing strategies and don't
really have a scientific method to quantify what is the benefit
I'm getting out of this pricing program, right.
So, and if you don't measure what what you're, what you're
working with, what is the benefit?
There is no way, no good way to improve the that process, right,
The pricing strategy and in essence of which you are just
like you cannot continuously improve, right.
The continuous improvement relies on effectively measuring
it. And this is a problem I had to
deal with multiple times. So I leaned on my operations
research background to and to this background to solve for
this problem, right. So I leaned on traditional
statistical techniques to develop store tests.
So basically what you could do is, the crux of this method is,
is that you can select a relatively small amount of
products and a relatively small amount of stores to be
statistically representative of the whole fleet or the whole
business that we're talking about, right?
And and then implement a pricing program or a pricing strategy in
that narrow set of products and store combination.
Look at the benefit against another similar set of stores
and products to see how much of A lift that you are getting out
of it. And what happens from this is
the key objective of this design is to achieving a kind of
balance where the selected products and the selected stores
are representative like I said and small enough so that if
something goes S, they're not affecting the whole business,
right. And how you do it is you want to
select products that are and needed to ensure equivalence
across like multiple dimensions, like business divisions.
Like if you have like multiple business divisions, you want to
have equivalence. You don't want to select
products that are top revenue across and a business division
is not represented. Similarly, you want to select
products across product categories, different product
velocities, product profitabilities and metrics like
margin markup, even seasonality. So you can like slice the
products based on those dimensions.
Similarly stores you can slice the stores and select stores
from based on region, traffic, profitability and and
performance metrics like store labor, size of the store
location, volume that that that store deals with.
And then you can kind of form with those pieces of
information, you can formulate the optimization problem like,
hey, with these dimensions of products and excuse me stores.
Find me a combination that is representative.
Why fleet in terms of volume, in terms of revenue, in terms of
representativeness across the dimensions that I talked about
and and and and it will not be a perfect match.
Like for example, let's just say your PROC Division One is 50% of
your business. These methods can get you to
like 48 percent 49% representativeness right and and
and once you have these you're able to get like a near
representativeness. And typically with these kind of
tests you can simultaneously test like multiple pricing
strategies and like in statistical speak they're like
AB test. But the design of it is like a
little more nuanced to achieve equivalence.
And yeah. And once you have these design,
these tests, you can like come up with multiple pricing
strategies, implement those at a smaller scale, understand the
benefit out of it and then like depending on which ones are
working versus not, you scale up right and quantify the benefit
of it. And if it's not working, you can
continuously assess like, OK, does my pricing program kind of
needs a refinement because the benefit that I was seeing over a
period of time has kind of stagnated or like going down.
So it just gives you a continuous read into how your
strategy is doing and and then and you can continuously keep on
refining it. OK.
Now let me ask you this as well in correlation to what you just
said, the selecting products that don't aren't too heavily
involved with the revenue. You know if it doesn't sell too
much, you know it's not going to make make the company go belly
up. How often do you run these type
of tests? Actually you can run it once in
a while like for example if you have a new strategy you can run
this test like as a one go see like hey one time six weeks, 12
weeks test and you need to have like the product store and the
duration and it's a statistical formula that feeds that and
you'll tell, it will tell you like hey if you run these tests
for these long number of days with these number of products
and stores and you expect to observe X percentage of lift.
Here is what how long it's going to take.
You can run these tests as a one off or you can have a continuous
like a as they call it like a control study where you always
keep a small set where you're not putting any of your current
pricing strategy and then compare it and continuously keep
on checking. That is what I was talking
about. Like you can have these tests
that can be one off to test the strategy or these tests.
This test could be like continuously running over a time
horizon and you can continuously refine it.
If there is like a product that is not included or is a new in
your assortment, you can refine the assortment mix and then keep
going. OK, OK.
And that's more of like a controlled testing that makes
sense now as it pertains to adoption, you know, driving
adoption especially within sales teams, it's often a challenge
when introducing a new pricing strategy as many of us may may
already know are aware of what are some strategies or
experiences you have successfully navigated this
hurdle and ensured seamless integration within teams.
Yeah, this is just a good example actually.
Good question actually and a few that come to my mind and some of
my customers have extensively spoken about it on public
forums. So basically if you don't ask
anyone, I mean there is always a balance between trying to get
the highest price or not losing the business, right.
So you have to incentivize those instead of incentivizing volume.
For example, if your incentives or adopting the price is like,
hey, how many transactions you can close, it's a race to
bottom. Everyone will go to the lowest
price that you're recommending and try to close as many deals
as possible. However, if you're able to
incentivize like, hey, if you are like selling at a stock
price, your incentive is going to be widely different than if
you're selling at a floor price, right?
So we had some customers who were trying to optimize gross
margin dollars while incentivizing number of deals
done and that was like counterintuitive defining your
sales compensation program is a good way to kind of Dr.
adoption. The other is simply asking
people for like why do they need a price exception.
So for example, we had a customer where like they were
seeing a lot of price override. And what they did was they added
a simple widget where you have to select from like 5 or 6
regions. Why you think the price is not
right and why you need the exception And you cannot leave
others that you cannot leave blank.
You have to give a reason. And those are concrete reasons.
And all of a sudden the adoption increased because if you people
can select others or not give a reason, they will always like oh
right, right. And yeah, And I had one more
customer actually. They were like pretty good in
the sense like what they did was basically when you have a price
that is displayed for the salesperson and if you are not
feeling confident about, there is a widget to explain, you hit
that explain button. It's just like hey, your
transaction is similar to these transactions that have happened
in the last six months or a year and this is the price they have
realized. So it and that kind of gave
confidence to the salesperson, like hey, the price that I'm
going to offer is not going to end up in the lost business.
So it was more like building confidence.
So the multiple things people can do, but what I've seen is
rewarding the right things and not rewarding the wrong things
like other things that are not aligned with your business or
pricing strategy is the most effective way.
That's interesting, that's good to know.
Now I want to move briefly aside from adoption and just looking
at beyond you know things as aspects beyond pricing.
Are there other areas within the realm of B to B commerce where
you foresee the integration of data science and AI playing a
pivotal role? Any emerging trends or
opportunities that may be catching your eye?
Yeah, Yeah. I mean, certainly and then like
I have seen that happen with some of my customers and like a
good example is just staying with the segmentation team, like
you could use machine learning to cluster your customers into
different profiles, right. And I'll give you a more
relatable example. So think about it like your
customer has like builders, manufacturers kind of thing and
then one of your customer base, you're able to identify like,
hey, these guys seem to be doing transactions that make it look
like they are floating contractors, right.
So you're able to create personas of different customers
that you have and machine learning algorithms.
There are a bunch of those that can accurately classify your
cluster, those set of customers. Now think about just one
customer, like floating contractors.
Sure. Now in the floating contractor,
what would a floating contractor buy?
Will buy tile, will buy you reducers, transition pieces,
stair noses, all the good things, right?
So you have a persona that hey, of these customers within my
flooring contractor, a customer base, pretty much everyone buys
40 to 50% of their sales is, is on flooring type, 20% is
reducers, stair noses, accessories, whatever you will.
And then once you have that you can use again use machine
learning algorithms, sort of even simple analytics really to
say like, OK, are there any anomalies in these clusters?
Like is there a customer who is buying pretty much the same
number of floor tiles or same percentage of floor tiles that
other contractors are buying, but is not buying reduced
transition pieces, accessories, right.
And you can identify areas of growth there.
So that's that's like a very good use case of like clustering
similar customers together and then within that cluster
identifying anomaly to find areas where you can grow your
business that's. Cool.
Yeah, yeah. And then there's another
example. Actually, I worked on this one
with another customer. So I had a client who stocked
like standard parts and also built custom parts like
electrical panels and and things like that, right.
And many times like customers would come to them and say like,
hey, I'm looking for this part XYZ part right.
And that would often be the case that they didn't have the exact
same part but they had like a perfectly good substitute of it
in the stock and it was very hard for them, manually
cumbersome and error prone to have that mapping.
Like hey, I have this product, here are the 15 other products
that are a perfectly good substitute and can be used in
its place. But for them to manually
maintain that mapping across multiple products and as their
assortment grew or the product offerings increased, it was
becoming harder and hard for them to keep that mapping or
like maintain the list of substitute products that could
go right. So like I worked on like
building a machine learning solution for them that will
create this mapping by kind of identifying product attributes.
Like you have a free form text description like hey, this is X
world, switchboard, whatever. And then you have product
attributes, descriptions like 3 phone texts like I talked about.
And it would create a similarity score based on like, OK, these
products are similar to this product and this is how, like
you'd create manually create a mapping but give that rule to
machine to be able to faster and accurately figure it out.
And that would create like a good list of substitute products
and in this case, what they have to do.
I mean of course you cannot take human in the loop, like human
out of this loop. Sure.
But the task reduced from like I define products to like looking
at, hey, here is my machine's recommendation.
Yeah, 90% of these are fine. These percent are not right.
They introduced their workload and it was like a good use case
of machine learning and making your product offerings more
robust, right. OK.
Yeah. That's good.
Now Mr. Vivek Anand with Gap, I want to, I want to close out and
thank you so much for your time today.
Before I let you go, you you did provide a lot of key insights, a
lot of knowledgeable wisdom if you will in the AI and pricing
and data science realm as regards to bTB pricing lease for
those who are listening and are are one are interested in
learning more about you and you know your resources, what you
stand for maybe where can they go to learn more about you.
That's a good question. I mean, like, I'm available on
LinkedIn, so like a good place is to just connect with me on
LinkedIn and like research to me with your questions that you
have. And I'm more than happy to
engage. But yeah, I mean I also am
looking to like create a forum for myself.
Like I like I mentioned previously, I not only deal with
pricing, I also deal with inventory like buying and like
Internet issues which are again like pretty prevalent in the B
to B space. And I'm going to like I'm
planning to actually. So I don't have a name yet.
I have a a domain of myself where I will be like sharing
these ideas, but LinkedIn is probably the best way to reach
out to me or communicate with me.
LinkedIn and and kind of keep up with you on the latest updates
about this project you're you're working on personally that that
might be really cool. So staying, staying to know, I'm
sorry, go ahead. Yeah, I think that, yeah,
absolutely I can. LinkedIn will be the first place
where I'll like talk about this project where I have like these
things like codes and and use cases and things like that.
OK, OK, cool. Well, for those who are
listening, stay in tune because he has great things in store for
you all in the times to come. Until next time.
We'll see you all later. Bye, Bye.