Hello, and welcome to the Professional Pricing Society
podcast. I'm Kevin Mitchell from PBSN.
I'm thrilled to have you with us today.
Our podcast explores the latest pricing strategies, industry
insights, and expert perspectives to help businesses
optimize their pricing for profitability and growth.
Today we have a fantastic guest joining us.
Mr. Fred Quesch is the founder of Kenolytics, a boutique
consultancy specializing in pricing analytics and strategy
for food service and e-commerce companies.
Fred has over 15 years of experience in pricing and he has
worked across a very wide range of industries.
He has been APPS member for well over a decade and has LED
several CPP workshops at our conferences.
He holds a PhD and a Master's in Economics and he's also going to
lead a workshop at our upcoming event, PPS Profitable Dallas in
May from May 6th to May 9th. Our conversation today is all
about pricing analytics and strategies along the customer
journey. We're going to talk about how
businesses can use data-driven pricing strategies to enhance
profitability at every stage of the customer experience.
Brad, let's start by setting the stage from a brand's
perspective. What are the key touchpoints on
the customer journey and what role does price play?
At each of. These key touchpoints.
Yeah. Thank you, Kevin.
So typically what I what I see is if you want to simplify it,
there are mostly three main touchpoints along the the
customer journey and three prices, I would say attached to
those to those touch points. The main price, the number one
is obviously the everyday price. That price is visible to anyone
and everyone, including non customers.
You know, if you think of an e-commerce platform or an
ordering app for a restaurant, even if you decide not to order,
you will see the the everyday price and that everyday price
usually then and that ends up being used in about 50 to 75% of
orders. So it's, it's the main one.
I would say the second one you will have a number of promoted
or let's call them discounted prices, limited time offers,
coupons, you name it. And these prices, they should be
primarily used for new customer acquisition.
And by that we mean the first time a customer ever orders with
the brand and also to get the 2nd order, getting the 2nd order
and we'll get to that maybe a bit later, getting the 2nd order
is, is crucial, especially getting the 2nd order as quickly
as possible after the first one. It's the best way to build the
loyalty. The 3rd and and last type of or
set of prices that you can use as as another touch point is
precisely everything that pertains to your loyalty program
and more targeted offers. And that's usually what is what
is used primarily after that second order.
So once a customer you know is placing a 3rd, 4th, 5th, 10th
order, they're more familiar with your brand and it's
obviously a different type of relationship as when it's a
brand new customer. Fred, that's a great breakdown.
With these different touch points in mind, where should a
company start? Which pricing element is the
most critical? Yeah.
So, you know, I'll be preaching to our own church here with
Pricers listening to us. The the everyday price, sort of
the traditional everyday price is it remains the most important
one and for a couple of reasons. The the first one is that it
ensures that the company remains profitable.
And you know, everybody who's listening to us here at, at PPS
knows that, you know, that's what price is for first and
foremost is to maintain and grow your, your business's
profitability. And what it does here is that
you, you can think of that everyday price as the reference
price for all your transactions. So all the discounted prices,
loyalty prices, targeted offers that I just talked about, in a
sense they will be set off of that, that everyday price, which
is sort of the the baseline, OK. And that baseline should be set
in a way that that your business is profitable.
The, the second reason why it's, you know, the everyday price
remains the the most important one and the one where any brand
really the one that any brand should tackle first is that even
with at most aggressive brands, you know, the brands that are
going to do a lot of discounting, a lot of promotions
with a heavy loyalty program, usually that everyday price
remains used in about half of the transactions, sometimes in
about 75% of them. So obviously that's that's the
one that you you don't want to, you don't want to set wrong,
wrong. Thank you very much, Fred.
Now let's talk about the analytical tools that are
available to us as pricing professionals.
What are the main types of analytics that businesses should
use when setting prices? Sure.
So there's there's a lot you can do and especially with the the
type of businesses that we're talking about here with a heavy
component of digital sales, whether it's e-commerce or
restaurant brands now selling more and more online.
There's a lot of data available and a lot of, a lot of things
that you can do to help you set those different prices the, the
right way. Let's start with the everyday
price, which again is the most important of the, the Trinity
that we're talking about here. Here number one thing you you
ought to do is, you know, use price elasticity and quantify
price elasticity. A lot of businesses now,
especially online, do they run a lot of experiments.
So they try different prices and different, different price
points for for their product. So they actually have a lot of
data, experimental data, but real life data, you know, with
actual orders from their customers to use to build
predictive models of demands that can then help you quantify
the price elasticity of your product.
Price elasticity. Just a quick reminder, what it's
going to tell you is whether your product is very sensitive
to a change in price or not. Obviously if you are, if your
goal is to take to run a price increase, you're going to focus
on the items that are not price sensitive.
So with a low price elasticity, the ones with a very high price
elasticity, you will stay away from for your price increases,
but they will be good candidates for promotions though, right.
And so Speaking of which sort of second type of prices, we, we
talked about promotional prices and, and discounted prices here.
Typically you're going to focus on promotional effectiveness.
What I've noticed is that there's actually a lot of
companies out there that are not very good at measuring the true
impact of, of their promotions. It's a lot of just very simple
top line before and after analysis where all look volume
went up. Our promotion is successful.
It's not that simple. You need to go a lot deeper than
that to really understand whether a promotion worked or
not. One way to do this is to build
what statisticians and economists called a
counterfactual. So a counterfactual will tell
you what would have happened without the promotion.
There are fairly simple ways to do it.
There are also more advanced ways to do it where you can
build again, a a predictive model that will tell you, all
right, had the promotion not happened, this is what we would
have observed at a very high level of detail.
And by comparing that kind of actual with your actual
performance, this is where you can really get a good read on
the effectiveness of of your promotions.
Last but not least, for everything related to loyalty
programs, targeted offers a good long in depth analysis of your
order patterns and customer behaviour on the customer sides.
Typically I start by analysing cohorts and that's something
very common in e-commerce, right?
Cohort analysis, so you look at all the customers acquired in
the same month or the same quarter and you track them over
time and see how they behave and then eventually you're going to
build more advanced customer segmentation.
You know, Kevin, the customer segmentation is key to pricing,
whether it's B2B consumer, you know that that never goes away
again. Now with the the B to C digital
businesses that we're talking about here, you can do a lot of
that customer segmentation because now every time a
customer places an order online or through your app, you
actually have the customer ID attached to the transaction,
which opens up a lot of possibilities obviously for for
analytics and, and, and customer segmentation and, and customer
analysis. Thank you.
Fred, you mentioned how important it is for companies to
clearly define the purpose of each pricing touchpoint.
What are some of the common mistakes you see businesses
making in this area? The the main mistake is not to
take advantage of it actually and to try to have either A1,
let's call it A1 price fits all, you know.
So for every transaction, regardless of who your customer
is, where they are in their, their journey with your brand
using the same price or sometimes using the wrong price
for the wrong purpose. And what I mean by that is
typically, so again, if you go back to the, the three types of
prices I, I, I described here, so your everyday price, your
promotions, Lt. OS discounts and then your, your
loyalty programme and targeted offer.
Well, again, your everyday prize is here for profitability.
And that's what that's what pricing is for.
I don't think anybody who listens to us here will disagree
with with that statement promotions and that's where I
see a lot of mistakes being made.
Sometimes people use promotions to try and drive volume from
existing customers and to drive loyalty etcetera.
That's a losing gain. Usually promotions are most
effective when they are used for customer acquisition and by that
I mean customers or consumers who have not purchased from your
brand yet or who are very early in their customer journey with
you. So some sometime around the
first or second order with your brand and if you tailor your
your promotions and your discounts to really attract
these new customers to your brand, this is where your
promotions become effective and your marketing dollars become
more more productive. Loyalty programme and
personalisation. These are great tools and great
sort of prices or pricing programmes to use for retention.
Again, you know you're not going to get someone who is totally
new to your brand. They're just placing their first
order. They've never tried your
product. You're not going to going them
to sign up for a loyalty program right away.
Whereas once a customer has placed 123 orders, they've tried
several of your products, that's where they, they will start
liking your brand, liking the value that they're getting from
your product and they will, they will sign up for the, for the
loyalty program. And one, one comment here,
loyalty doesn't mean deep discount necessarily, right?
And that's where we we're sort of at the intersection between
pricing and other marketing initiatives as well.
Sometimes, you know, loyal customers, they enjoy having
access to new products before everybody else, for example,
right, or special bundles or you know, things like that.
They of course do welcome a discount, we all do.
But it's not necessarily about pricing as low as possible.
It's more about recognising the value that you're giving to, to
your customers. My last comment on this, Kevin,
is, you know, it may sound familiar to you because actually
we're getting closer and closer to what B2B companies have been
doing or trying to do for, for yours and, and decades, right,
Especially with trying to create loyalty with their customers and
customer segmentation, etcetera. So this I think a lot of
convergence happening here between the the the two spaces
B2B and PSC. Thank you, Fred.
That makes a lot of sense. So if a company wants to
implement this pricing framework, what kind of data do
they need to get started? Yeah.
So that's, that's the beauty of it.
You, you actually don't need that much fundamentally.
You, you need a single table of data, which is your order
history at the order line level. And that's something I feel very
strongly about, you know, and us as a consultants, we are
sometimes guilty of pushing companies towards, you know,
you, you need a data warehouse, you need a data leak, you need a
tech stack and, and whatnot. But actually there's a lot that
you can do with just that one table of data with your order
history at a very granular level.
So really at the product line, the product line level, so that
one table that will have your order ID, customer ID, product
ID and then quantity and either price or revenue.
There's already a lot that you can do with that.
Now the more the better. Obviously a lot of companies
actually they have a lot more data than than that.
If you are an e-commerce company, you're using Shopify or
you are an Amazon seller and you can pull your data from Amazon
Seller Central, the tables that you get actually have a lot more
columns than that. It gives you all the details
about the discounts that that you gave at the order level and
at the item level. So there's a lot of things that
you can do. You also obviously have more
data available in terms of additional tables.
You a lot of companies have ACRM, right, even in B to C, So
they have a list of all their customers and they collect a lot
of information on those customers that they that they
can use for segmentation. You have a lot of data about
your products that that you can use as well.
And then obviously you have external data, web scraping,
we're at the point where it's almost table states now,
unbelievable to say that 10 years ago it was sort of the,
the frontier in competitive pricing and competitive analysis
was very hard to do. And the data you would get was
so, so nowadays it looks like everybody's creeping everybody
else and everybody has access to their competitors prices and you
know, it's, it's all out there. And so that, but that's another
data point that you can use when, when making decision.
I've also had several clients using third party consumer data,
so complementing their, their order and their customer data
with, you know, credit history or credit data from companies
like Experian. And that adds a lot of
dimensions to your customer segmentation because as you
know, those those credit companies or credit card
companies collect a lot of data on consumers as well.
Thank you, Fred. For brands that are new to this
approach, where do they start? What's your best advice for
getting started with pricing analytics?
Sure. So again, step #1 pull your
order data. You already have the data, it's
available. It's usually pretty clean,
already in, you know when you pull it out of your system
again, if you are an e-commerce company, you're using Shopify or
you are an Amazon seller, you can pull the data from Amazon
Seller Central. It's very easy to do.
If you're a restaurant brand, you know, you have your POS
system, you have your app system that allows you to pull these
these orders very easily. So I feel that getting access to
the data now is, is not the challenge anymore.
The data is there. It's fairly easy to pull, you
know, for someone who knows what they're doing now, once you have
the data, usually what I advise clients is to start with the
basics. And in this case, the basics
would be cohort analysis. It's not even pricing at first.
Just break down your business by quarterly cohorts of customer
acquisition and start looking at your main KP is average order
value, average discount. You can look at average price by
by product and by cohort. Just by doing that, you'll be
surprised by what you see. You you will learn something by
just. Doing a simple average price by
product, by cohort over time, right, If you do it on a
quarterly basis, you'll, you'll see that there's, there's a lot
of insights that you can gather there.
Then as I mentioned earlier, the I would say the first step
beyond the basics would be to make sure that you set your,
your everyday price optimally from a margin standpoint.
And now we get back to the price elasticity modelling the
predictive models of of demand that I talked about earlier.
Again, at the end of the day, we, we are pricers here and that
everyday price is, is the most important one in your business.
All the other prices we we talked about today, your
promotions, your Lt. OS, your coupons, your loyalty
programs and your personalized offers will sort of revolve
around your, your everyday price.
So get that one right. That would be my my advice.
Thank you, Fred. This has been such an insightful
conversation. Before we wrap up, I want to
remind our listeners that you'll be leading a full day CPP
workshop coming up at PPS Profitable in Dallas.
Fred's workshop is pricing analytics and strategy for
optimal growth in B to C, So make sure to take advantage of
that. But Fred, can you give us a
quick preview of what your attendees can expect from your
full day CPP? Workshop, sure, yeah, Thank you,
Kevin. I'm really, really excited to to
get there and looking forward to it.
So the workshop will actually I'll, I'll be talking about a
lot of the things that I talked about today.
We obviously will have the full day.
So we will go in a lot more depth than than what we covered
today. And so I will talk about the
customer journey. I will talk about the different
touch points and the different prices available.
But more than that, you know, as you mentioned, my, my bread and
butter is pricing and I did it. So I will share with the
attendees how you can use, you know, the, the transactional
data I just talked about, you know, so once you've pooled that
order data, how you can use the data to set your everyday price
to analyse your promotions and promotional effectiveness.
So you can set your, your discounts and promotions
properly and how to use the data also to help you develop a
loyalty program and personalized offers.
We will go into a fair amount of details because we will have the
time to do so. We'll, we'll have the entire day
together. So I will be able to share even
for each one of those type of analytics that I just described
the, the different levels that are available, right?
Not everybody can be, you know, PhD level or Nobel Prize level
modeller of, of price elasticity.
And I know there's typically a lot of beginners who attend
these workshops and they may be a little bit overwhelmed at
first. By all my goodness, I, I've
never run a regression model. How do I do price elasticity?
So what I do is that I usually share different levels of
techniques or technicality as to how to run these analytics from,
you know, the beginner, you've never done it where here is
where you can start. Keep it simple, just get it done
all the way to. Well, yeah, if you are a little
bit more advanced, these are sort of the state-of-the-art
techniques that are available to you.
And so I'll, I'll share a lot of examples from project work that
I've done as well. So a lot of, a lot of data to be
expected and very, as you know, Kevin and I'm very keen on
keeping things practical, right? And making sure that at the end
of the workshop, the attendees can go back to their their desks
or their their offices and actually use what I've taught
them in the workshop. Thanks so much, Fred.
That sounds like a must attend session for anyone looking to
refine their pricing strategies. And of course, if you want to
learn more from Fred and other top pricing professionals, make
sure to join us at PPS Profitable.
We'll be in Dallas from May 6th to May 9th.
Lots of strategies and tactics and best practices for you and
your team. You can find
allthedetailsandregister@pricingsociety.com or you can always reach out to
me if you have any questions. Fred, thank you so much for
sharing your expertise with us today and also thank you to all
of our listeners. Fred, of course, thanks very
much to you. Thank you for being a part of
the PPS podcast. And everyone, if you found this
episode valuable, make sure to subscribe to us on Apple Podcast
or Spotify to stay updated on the latest in pricing.
Until next time, take care.