Data, Decisions & Discovery: Katherine Black on Turning Insight into Impact

What happens when data becomes more than a tool and starts to shape how we lead, learn, and listen?

In this episode of Retail Relates, Rich Honiball and Guy Courtin sit down with Katherine Black, Partner at Kearney and one of RETHINK Retail’s Top Retail Experts for 2025. Katherine’s career has taken her from Capital One to dunnhumby, KPMG, and now Kearney - helping global retailers turn customer data into meaningful loyalty and long-term growth.

She explains why curiosity beats coding, how the best loyalty programs are built on trust - not transactions - and why AI is most powerful when guided by human judgment. Katherine also reflects on lessons from her work with Tesco, Goodwill, and other global organizations, and what students and emerging leaders can learn from the next wave of “agent commerce.”

Insightful, practical, and refreshingly human - this episode is a masterclass in transforming data into impact.

Katherine Black Bio

Katherine P. Black is a data-driven strategist and Partner at Kearney, where she helps global retail and consumer companies transform how they grow, compete, and create value through data. With more than twenty years of experience leading commercial and technical teams, she specializes in converting data assets into strategic advantage—building loyalty ecosystems, designing data-driven pricing and personalization, and launching large-scale monetization programs that unlock new revenue streams. Her work consistently delivers measurable results, driving mature businesses to 3–5-point sales growth and launching new lines that achieve double-digit gains in year one.

Prior to Kearney, Katherine held leadership roles at KPMG and dunnhumby, where she led customer analytics partnerships with retailers including Tesco, Kroger, and Macy’s. She is a member of Chief, serves on the Board of Directors for Goodwill NY/NJ, and has been recognized among Retail Leader’s “Top Women to Watch in Retail” and DM News’ “40 Under 40.” Most recently, she was named one of RETHINK Retail’s Top Retail Experts for 2025. A lifelong believer that “data gives a voice to every customer,” Katherine is passionate about helping companies listen better, act smarter, and grow with purpose.

Episode Transcript

Rich (00:12):
Have you ever thought about what happens when data
becomes more than a tool and itstarts to tell a story?
For Katherine Black, the realadvantage isn't in how much data
you have, it's in how youlisten.
Hi, I'm Rich Honiball, and I'mjoined today by one of our
retail relates co-hosts, GuyCourtin, as we sit down with
Katherine to explore howcuriosity, empathy, and purpose

(00:34):
can transform analytics intoaction.
She has spent her careerhelping retailers and consumer
brands unlock the potential ofdata, from building loyalty
ecosystems to driving growththrough smarter pricing,
personalization, andexperiential design.
Our guest today, KatherineBlack, is a data-driven
strategist and partner atKearney, where she helps global

(00:56):
retail and consumer companiestransform how they grow,
compete, and create valuethrough data.
With more than 20 years ofexperience leading commercial
and technical teams, Katherinespecializes in converting data
assets into strategicadvantages, building loyalty
ecosystems, designingdata-driven pricing and
personalization, and launchinglarge-scale monetization

(01:16):
programs that unlock new revenuestreams.
Prior to Kearney, Katherineheld leadership roles at KPMG
and Dunhumbey, where she ledconsumer analytics and
partnerships with retailers likeTesco, Kroger, and Macy's.
She is a member of Chief.
She serves on the board ofdirectors for Goodwill New York,
New Jersey, and has beenrecognized by RETHINK Retail as

(01:39):
the top retail expert for 2025.
In this episode, we talk abouthow loyalty is built on trust,
not transactions, why curiositybeats coding, and how AI can
enhance rather than replacehuman creativity.
Katherine also shares what thenext generation of retail
leaders can learn fromGoodwill's purpose-driven
circular model, and how agentcommerce will soon reshape how

(02:02):
consumers shop and connect.
This is an episode we know thatyou'll enjoy.
Let's welcome Katherine Blackto Welcome to another episode of
Retail Relace.
Today I am joined by myco-host Guy Courtin.
How are you doing today, Guy?
I'm doing great, Rich.
How about yourself?
Doing great.
And then as mentioned in thepreamble, excited to be joined

(02:25):
by Katherine Black, who is apartner at Kearney.
Katherine, welcome to theprogram.

Katherine (02:30):
Thank you.
Great to be here.
Thanks for having me.

Rich (02:33):
Looking forward to jumping right in.
So we do jump right in.
And since we have had thechance to introduce you to our
guests, rather than have you gothrough the same chronology of
events, we take a little bit ofa different twist.
Think about the top three mostpivotal moments from a personal
or from a business perspectivethat have shaped your path and

(02:56):
brought you to where you aretoday with us.

Katherine (02:59):
Well, I guess I'll stick mostly to professional,
although interesting, if if youbring in personal, that's a
whole different, that's a wholedifferent ballgame.
Maybe we'll do a mix.
I mean, I would say one wasearly in my career, long before
I was in retail, I was inbanking for a while.
And one of the places I workedwas Capital One.
And I really like Capital One.
It was very data-driven.
It was a lot of the same stuffthat that I do today in terms of

(03:22):
understanding how customersbehave and whatnot, but it
wasn't a bank.
And I got the feedback that Iwasn't very clear and concise in
my communications.
Hopefully that's not the casetoday.
Hopefully, I've improved.
But one of the things someonerecommended to me is that I go
and work in this internalconsulting group that we had.
And the internal consultinggroup was primarily made up of
outside consultants who werecoming into the company.

(03:44):
So I went there and it wasfantastic because I got to learn
from all these consultants fromall these different firms.
They all gave me their trainingdecks.
So I learned every singlefirm's training method and got
tons of advice.
And it was a huge leap forward.
So that was one pivotal momentbecause it opened me up to
different ways of thinking.
I got new skill sets and I justlearned a ton.
I'd say the other big pivotpoint was uh maybe when I went

(04:07):
to Dunhambi.
Again, I'd been a banker, andthen that was really my shift
into pure retail.
And people would always say,Hey, how did you go from banking
to retail?
That's really weird.
I was like, well, the thing isthe customers that have checking
accounts also buy groceries andclothing and all that good
stuff.
So that's how that makes senseis I was always studying

(04:28):
customer behavior, but it was apivot point in terms of
industry.
And retail is so data rich.
And that was such a big pieceof my career that that was a
great pivot.
And then I guess that therewould be professional, I mean,
personally, um, you know, whenyou have kids, it's a life
changer, as you know.
And so I that has that has tofactor in there.

(04:49):
It forced me to be moreefficient, trust my teams more,
uh, let go of control a lot morethan maybe I would naturally
do.
And so, yeah, that's a that's amassive pivot point.

Rich (05:00):
Kids definitely count as a pivot point.

Katherine (05:03):
Yeah, absolutely.
They might be the biggest one.
You know, I tell I tell youngprofessionals who struggle with
time management that the bestsolution is to have kids.
I was like, okay, but if youbut if you don't want to have
kids, there's some other tipsand tricks, but that'll do it.

Guy (05:18):
Yeah, you get a dog too, that will be a good time
management.

Katherine (05:22):
That's a great, great idea.

Guy (05:24):
Kathy, it's interesting you say that too, because you talk
a lot about the the one of theimpacts with data and all this,
but then you talk about sort ofhaving kids and letting go of
some control.
And isn't that sort ofcounterintuitive?
Like you you talk about a pivotpoint about learning and
appreciate data in retail.
Then you say, but with when youhave the pivot with children,
you have to learn to let go.
Like, how do you consolidatethat sort of as a as a learning
you've had with regards to whendo you need to rely on the data

(05:46):
and be married to it and when doyou need to sort of see control
and sort of let things flow asthey should?

Katherine (05:51):
Yeah, I think there's an interesting, I'll have two
answers to that.
The first one is I don't seethem as totally um discontinuous
because in some ways when youuh allow your decision making to
be data driven, you do give upsort of your personal
preference.
You have to let go of kind ofyour personal preference and and
hopefully your biases andwhatnot as much as possible.

(06:12):
Um, so there's there is somecontinuity in that.
But you're right, you cannotdata drive your kids.
I mean, God bless if you canfigure out the algorithm to
manage, you know, a child of anyage, you know, five years old,
teenager, infant, what have you.
Uh, it's not quite it.
So you learn to observepatterns, I think, in both
cases.

(06:32):
And and that can absolutely beseen in children.
But uh yeah, the sooner you canrealize that sometimes the
behavior you're observing hasnothing to do with the direct
thing that's happening, the thebetter off you are.

Rich (06:45):
Yeah, absolutely.
So is this what you imaginedyou were gonna do?

Katherine (06:49):
I mean, who imagines what they were?
I I think that's such a funnyquestion when people ask at any
stage of your career, you know,where do you see yourself in
five years or 10 years?
I mean, short answer, notreally.
I did always considerconsulting, which is what I'm
doing at the moment.
But if you had said to me whenI was, you know, 21 years old
and I never left the state ofNorth Carolina and you know, I

(07:12):
grew up in a really small townthat I would live in London or I
would work in these types ofbusinesses or do these types of
things.
They didn't even really existin that way back then.
So no, I definitely would nothave imagined, you know, all
those rich experiences.
And so, you know, maybe that'sa point of advice is to not get
overly planned on things.
So there may be some vaguenotions that are still the same,

(07:35):
but a lot of the specifics, no,I wouldn't have imagined.
I don't know if I would havedreamed that big, to tell you
the truth.

Rich (07:41):
Did you have a dream?

Katherine (07:42):
No, not really.
No, not really.
I was sort of, I you know, Iwas sort of the type of young
person that was like, okay, Ijust want to get out.
I don't want to be, I want tosort of be successful.
I want to, you know,self-sustain.
You know, I don't want to gohome and live with my parents
and you know that.
I just want to have a job andyou know be moderately uh
successful and have some fun andmove on with life.

(08:02):
Yeah, I don't know.
I'm sure, I'm sure if you askedmy 21-year-old self, I would
have had a better answer thanthat.

Rich (08:08):
But no, that's actually that's a good answer.
And it's actually one that uh II try to encourage people with.
And I got caught because Ithink it was a couple of years
ago we had an intern group thatcame in, and I was giving the
advice of look for the firstthree to five years of your
career, don't get so wrapped upinto title or marching down a

(08:29):
specific path.
Explore, discover.
And then somebody turned to meand said, Then why do you ask
the question, where do youimagine yourself in five years?
I have not asked that questionagain since getting called on
it.

Katherine (08:43):
That's funny.

Guy (08:44):
Well, Catherine's interesting because I I like
your story about, you know, you21-year-old, you would never
imagine living in London, thingslike that.
But can you talk a little biton what Rich has said too, sort
of being open to these types ofexperiences?
Can you talk a little aboutthat?
Because when I looked at yourCV, it was really interesting.
Like you, I'd love to talk toyou more about this, like
working for Tesco and thingslike that, which I think are
fascinating, especially in yourpath to consulting and retail,

(09:06):
right?
Those are great experiences.
But how did you get there?
Like, how did you make theleap?
I mean, obviously, moving fromnot having left North Carolina
until you're 21, then all of asudden you move to London.
Like, that's not a small jump.

Katherine (09:16):
No, I mean, obviously there was a progression, a
progression along the way.
And there were some comforts.
I think you hear about peoplesaying, like, don't make two
hard pivots, like, don't changeyour industry and your location
at once or what have you.
And I didn't do that.
I was with the same company I'dbeen working with, doing the
same sorts of things I'd beendoing.
And I had some colleagues incommon.
And so there were some comfortzones within that for sure.

(09:40):
But it was just a greatopportunity.
I I got really good at beingable to do tooth in one work
with data.
And I was super passionateabout, you know, formalizing
customer experiences and andloyalty for retailers.
But I also was really good atgoing in and working with
clients who maybe needed somenew relationship help, needed

(10:02):
new, new um ways of the teaminteracting with them and that
sort of thing.
And so the blend of those twoskills is kind of how I ended up
in in London, being able toforge some good relationships as
well as make an impact on thebusiness, hopefully.
And uh, and it was a greatopportunity.
It was a lot of fun.

Guy (10:19):
What would you say, like if if you're talking, if you're
talking to the 21-year-oldCatherine, like what would you
tell her to make that leap?
What is the on the other sideof that bridge, like what are
you gonna gain out of it?
What are the what are the bigpositives?
And also maybe more what's onthe negatives uh in making that
leap.

Katherine (10:36):
Yeah, I would say for that one in particular, some of
the big positives were justgetting to experience a
different culture, getting toactually do something that I was
familiar with, but in a newplace to see how it worked
differently.
There's real value in that.
Living there, honestly, wasfantastic.
It was a fantastic experiencefor my kids.
Uh, I loved seeing how they uhsaw countries as associated with

(11:01):
people versus foreign lands.
And that really, I think, canonly come from being in a really
internationally richexperience, which they were.
We traveled a lot, got to see alot of the world and just hear
and see day to day how thingsoperated in ways that on the
surface look fairly similar, butbut down deep were were pretty

(11:22):
different, was an incredibleexperience.
And I think in terms of beingopen to that, yeah, there's uh I
I love Ina Garden.
I think she's a great, I don'tknow if you know her, she's a
cookback, and she has this book,Be Ready When the Luck Happens.
I think there's some realwisdom in that.
Sometimes you can't engineeryour way into an opportunity.
Sometimes you can, butsometimes you can't.
And I'm not sure I'veengineered my way into a lot of

(11:43):
opportunities, but I think thatidea of being ready and being
willing to take newopportunities when they come up
is super important.

Rich (11:51):
So I'm gonna dive into your background a little bit
with um, and I'm trying todecide whether I want to dive
into the data part of it or theloyalty part of it.
Obviously they both connect,but yeah, what role has data
played and and how is it kind ofreshaped the retail environment
over the last 10 or 20 yearsfrom your experience?

Katherine (12:09):
Yeah, you know, it's interesting.
When I first started in in mycareer and really working with
data, I really felt empoweringbecause data was like the dr the
great democratizer.
Suddenly, like the loudestvoice in the room used to make
all the decisions, and suddenlythere was a way to sort of
democratize that.
I would say over the lastdecade or so, data has gone from

(12:30):
being a real differentiator tokind of table stakes.
Now, if you think about it, Ican't think of many businesses
who aren't using uh at leastsome of their data.
I'm also always surprisedsometimes at things that I did
maybe 20 years ago that arestill new to some companies and
whatnot.
So everything old is new again,I suppose, from time to time,
but it's become much morecommonplace and much more table

(12:53):
stakes in how businesses arebeing run today.
And I think that's that's a winfor businesses and the
customer.

Rich (13:00):
So I'll jump in from a loyalty perspective without
divulging anybody that you'veworked with or putting anybody
on the spot.
You've got a wide range ofexperience, both from a global
perspective, from the uh fromthe the client side and the
retailer side.
What has separated those thattruly get loyalty and invest in

(13:22):
it versus those that are justchecking the box because they
know they have to have it andthey feel like it's table
stakes?

Katherine (13:31):
I would say the biggest differentiator and what
would drive return on using yourcustomer data, loyalty, et
cetera, is how much you'retalking and caring about
building loyalty with a customerversus driving sales.
And what I mean by that is inthe first case, I've seen, I'll

(13:52):
give you a great example.
Rich, I've probably shared thiswith you before, but I once
worked with two retailers.
They used the exact samesystems, the exact same
analysis, the exact same typesof data.
It was their own data, but it'sthe same types of data.
They got dramatically differentresults.
And it really came down to oneof them kind of had religion
around using that data andreally putting the customer

(14:12):
first and their thinking.
And they were kind of willingto make trade-offs that
analytically, if you were onlyworried about the short-term
bottom line, you might not make.
But like I say, they kind ofhad religion around it.
They really believed that ifyou did the right thing for the
customer and you saw that in thedata, it was going to pay out
in the long term.
And it and it really did.
They had phenomenal, verysustained results.

(14:35):
In another case, I would saythe company's actually far more
sophisticated in how theyoperated.
But they were sort of superconcerned about the short-term
bottom line.
And that then becomes a salesdriving technique, not really a
loyalty technique.
And you get short-term bumpsthat are hard to sustain in the
long term.

(14:55):
And then the longer term youoperate, the longer term you
operate that play, the moredifficult it is to get results.
And I think that's a realdifference maker that's hard to
stomach, particularly foranalytical people, because you
want there to be a very, youknow, sort of data-driven, but
it is the art and the science.
It's data driven, but you youdo sort of have to believe it
and have some conviction arounddoing the right thing for the

(15:18):
customer in the end and thinkingin a more long-term fashion and
not just the short-term metric,which can really throw people
off.

Guy (15:25):
I really like that answer, Catherine, but can you expound
without obviously really names?
But from the retailperspective, are there different
kind of categories of retailersthat are more open to what you
just said, which is have thatlong-term view of building
loyalty versus justtransactional?
So, for example, you know, ifI'm a fast fashion retailer, I'm
just looking for the quicksale, and maybe I'm not as

(15:47):
concerned as your long-termrelationship, but if I'm a
luxury brand, you know what?
I want to nurture that customerloyalty for the lifetime of the
customer, for lack of a betterterm.
Do you see that, or does itdoes it not matter what the
subvertical retail is that canapply these techniques?

Katherine (16:03):
I would say where folks tend to be more short-term
oriented is when they're apublic company with a difficult
new competitive environment andthey're trying to make their
short-term earnings.
They're in a low marginbusiness.
You know, it's easier in for inyour example of the luxury uh
company.
I think when you are of aprivate company that is higher

(16:23):
margin, it's easier to take along-term view candidly.
And you have a lot ofexecutives who are under very
real pressure to deliver resultson a quarterly basis.
And so being able to navigatethat and do the right thing for
the customer in the long termwhile also managing your
short-term commitments is reallydoable, but it is very
difficult.
And that's where I tend to seepeople fall down.

(16:45):
You know, when the environmentis particularly challenging,
people just sort of fall backon, well, geez, there's been a
strategic shift or a marketshift or a big difference here,
but I got to make my number.
That's when things startfalling apart.

Rich (16:58):
You've had the art, the science, you've been involved
with clients that have had theart and science.
How do you make sure whenyou're leading a project that
you get both sides at the table?

Katherine (17:10):
Yeah, interesting.
Well, look, today is superinteresting because we can train
bots to act like differentpeople, and you can have
literally thousands of personasat the at a given table.
So that's sort of a fun newplace where we're doing some
experimentation.
But I think even with evenwithout the fancy technology,
let's rewind two years ago.

(17:31):
How did we do that?
I think you always have to begrounded in the data.
But I will say common curiosityand common sense will trump
technical acumen any day of theweek.
And, you know, Rich, we'veworked together in the past.
You teams know how totechnically do what they do, but
you do need that experience setto go in and sort of say, like,

(17:51):
that doesn't, that doesn't makesense with all the context that
I know.
Or, you know, when it let meshare it with this other part of
the business and understand howthey're thinking about this
because it really can make adifference.
And you can spot some obviousissues that are nuances to the
data or connections that need tobe made between the data that
you'd otherwise miss.

(18:11):
So there's always a little bitof science and art.

Guy (18:14):
So I I'm gonna jump on this because I love what you just
said.
So talk a little bit.
I'm gonna bring up thetwo-letter word that we all have
to talk about because you talkabout data, and now you talk
about the experience, and thenpeople are saying, Well, now we
have AI engines and they'll doall that.
And can you shed some light onthis?
I think I'm with you 100%,where there has to be the human
element that understands thenuances, understands the

(18:35):
context, understands from fromexperience.
Is AI gonna take over that?
Or is the human where is thehuman AI data play in this?

Katherine (18:44):
Yeah, I mean, it's interesting because I'm sure you
guys have experienced this.
I feel like uh AI is anenormous accelerator.
I mean, it's really incredible.
Uh, but it's certainly notperfect.
Uh, and I do think the abilityto shape the right prompts, the
right thinking, the right uhanalysis still requires some

(19:07):
human intervention.
And certainly evaluating theoutputs uh requires some gut
checks and some some commonsense real-world applications to
things.
So yeah, it's uh I do thinkit's a massive game changer.
Absolutely.
But is it a hundred percent AI?

(19:27):
No, I don't I don't see thatworld in the near future.
Maybe we'll get there uh atsome point, but I don't see that
as immediate for a variety ofreasons, but but predominantly
because we still need you stillneed to kind of guide and shape
the thinking and bring in otherperspectives that aren't
necessarily gonna be thought ofand and gut check it.

Guy (19:49):
No, I I I was leading the witness of things.
I think that's I'm spot on withyou on that.
I I think it's when I look atyour, you know, you just touched
a little bit about yourexperience in London and working
for Tesco as I saw in all this,and it's like you can't
replicate that, right?
That's experience you have, andwhen you see data that I'm sure
in your consultant with acustomer now, you can call upon
that.
And yeah, use AI as a tool anddata.

(20:10):
But I think I guess the nextfor me, it's it's interesting
you also mentioned talking abouttalking to different parties.
Let's go back again justquickly, like the 21-year-old
Catherine, like what would yousuggest to her?
How do you develop that tribalknowledge, that experience?
Right.
And and for you know, I have a17-year-old son, like, what do I
tell him?
Like, as he goes in the worldwhere he's using AI, and that's

(20:31):
AI to him is you know, like whatwriting a turn paper was to me
in college, right?
Like using a word processor ofdating myself.
But what would you tell them interms of how do you build some
of that those calluses, thatexperience that then you can
apply the tools, but your brain,your experience is is is what's
valuable that can't bereplicated.

Katherine (20:49):
Yeah, I think I think particularly for younger folks
who may not have as much manyyears of experience, I do think
there's a certain amount ofcuriosity.
I I think curiosity is one ofthe most critical skill sets,
particularly when working withdata, but just in general.
And so I think that ability tobe curious and not just take a
pat answer and continue to shapethinking and wonder about where

(21:13):
things can go and sort ofexperiment with it, and then to
borrow experience.
So by that I mean build buildthe network of people who do
have more credible experienceand make sure that as you're
thinking through, you know,using AI, you're being curious
about it, you're really drillingdown and asking them like,
well, what's the downside ofthat and what's the other
perspective on it?

(21:33):
But then also using someoneelse's gut to gut check things,
I think is important.
Absolutely.
The experience will come.
I don't think there's any wayto shortcut experience.
You almost have to borrow it inthe in the in the short term.

Guy (21:46):
That but I love the the notion of curiosity, right?
Because I think that's that'sthe part I think at least I see
when I when I get to talk tostudents like college students,
grad students, it's always likebe curious.
Like there's nothing thatsomething that might seem
unrelevant to your workpotentially could be super
relevant because you're gonnalearn something from it.
Take a class in you know,ancient philosophy, even if you

(22:07):
want to be an account.
Maybe you'll there's somethingyou'll learn from it.
And if you enjoy it, then evenbetter.
Yeah.
So I think that's I love thatpoint about curiosity.

Katherine (22:14):
Yeah, and I think making those connections to your
point, I think the mostinteresting insights come from
making connections that otherpeople aren't seeing.
And so, yeah, the more curious,the more diverse your inquiries
are, whether that's a class orjust reading an article or
studying something, I thinkthat'll make everyone everyone's

(22:35):
perspective richer.

Guy (22:36):
So following that to think back to what Rich was asking
earlier about your work and thespace you're seeing with data,
like how do you talk to yourcustomers who potentially, well,
the data tells me to do thisand that's what I'm gonna do?
And the AI engine told me thisbecause I put it through Grok
and it told me blah, blah, blah.
How do you force them to say,well, wait a minute, have you
thought about this?
Have you thought from thisperspective?

(22:58):
Have you looked at a differentdata set maybe?
Or have you maybe looked at thedata and said, you know what?
I don't trust, I'm gonna lookover here.
Like, how do you train?
And I know it's an art ofscience, but from your
perspective, how do you get thepeople to see the vision or the
light to get there?

Katherine (23:13):
Yeah, I think um, I mean, I haven't had a lot of
those interactions with clients.
They're still pretty sober,honestly.
About I say I will say withteams, I've sometimes I've more
frequently over the years havegotten you know data or
information back, and I'm like,that doesn't, that doesn't
check.
And I can always then sort ofsay, well, here's what here's
why it doesn't check for me.
I need you to go back andresearch that.

(23:33):
And it is always someperspective that they were
missing.
So yeah, I think the best thingto do is just be really
thorough in testing ideas withdifferent people.
And obviously, testing ingeneral, I think is incredibly
underutilized and underrated.
I would hope that's somethingthat AI will bring about is much

(23:55):
more robust testing, uh, eitherthrough synthetic means like
digital twins or it actually inmarket, but in more limited uh
basis, because very fewretailers do real testing.
Um, they do a lot of pilots,but in in my experience, not
true testing.

Rich (24:13):
And in doing the testing, you can fall prey to the
dystopian novels that say we'reall going to just give into the
machine and and go about ourbusiness.
But it is also, I think, aboutaccepting imperfection.
I I I sometimes wonder, wouldyou have Formula 409 if AI was
creating it?
And you weren't going throughthe 408 mistakes if if you buy

(24:35):
that.
And that oftentimes it's themistake, testing it, learning
from it, that never say neverwill artificial intelligence get
there, but it is curiosity, butbeing able to be okay with
making mistakes andimperfection.

Katherine (24:52):
Yeah, that's a real skill.
Uh that that being being notafraid to fail.
And honestly, I'm not very goodat that, but what I'm okay at
is breaking it down to be asmall failure.
I can be okay with that.
That's why I like testing somuch.
You never really have a massivefail if you've constructed a
good test.
You're only going to have asmall fail, and then then you

(25:13):
can kind of move on.
That's easier to accept.

Rich (25:16):
So, from a retail perspective, I want to lean on
two experiences that you've hadand how that would shape your
advice for US retail.
Again, without putting anybodyunder the microscope.
And you've had this richexperience from a global
perspective and seeing the wayretail operates differently.

(25:37):
And you also are part of theBoard of Goodwill, which is
retail, but a completelydifferent perspective.
What are lessons that you'vetaken from a global perspective
of retail and the goodwillaspect of retail that US
retailers should be looking at?

Katherine (25:55):
That's a good question.
Those are very differentuniverses.
I'll start with internationaland the global players.
I think global retailers aresecond to none on private label
on a very specific thing, and USis very underpenetrated in
that.
And I've never quite underthere's some very obvious things
that they're doing really wellin Europe that I'm surprised
that more US retailers haven'texperimented with over the

(26:18):
years.
They're not, they're not brandnew ideas.
So that's one.
I would also say the Europeanretailers are very efficient and
and frankly, I'd say willing togive the consumer what they
want.
If you think about somethinglike e-com, and I'm I'm thinking
specifically in grocery becausethat's where I spend a lot of
time.
Europe had a well-developed,sort of 20-year head start on

(26:43):
grocery e-com ahead of USretailers.
US retailers sort of said, no,thanks.
That's looks super unprofitableto us.
And they said that until Amazonacquired Whole Foods.
And then they were sort oflike, oh, geez, I guess we
better do it now because losingmarket share is more detrimental
to us than the e-com, you know,lack of profitability.
We'll do that.
And all good news.
They've also figured out retailmedia will do that too.

(27:06):
But, you know, Europe sort ofleaned in on it and just said,
well, this is a betterexperience for the customer.
Of course, this is where thevalue proposition is going.
We'll innovate ahead of that.
And it forced them to have muchmore efficient supply chains,
much more efficient operations.
And so I think there's somelessons to be learned there for
US retailers.
I think goodwill is interestingbecause it's super dependent on

(27:27):
two things.
One is donations in.
So they literally need thecommunity to donate items in.
There's no sort of pickingshoe, there's no merchant
prints.
It's a donation based business.
And so much of that businessrevolves around how much
donation volume is coming in.
And so making sure peoplereally understand and thrive on

(27:49):
the purpose of the organizationand what that organization is
going from, which is in a lot ofcases widely misunderstood, is
so important for buildingloyalty, not just for sales, but
also for donations.
So they're incredibly dependenton the donor base.
And it's an incrediblyprice-sensitive business.
I don't think I've, it's themost price-sensitive business

(28:10):
I've ever seen.
I mean, down to the penny.
I think if you can measure itin fractions of a penny, you
would.
So it's incredibly pricesensitive.
And so I think there's a lot tolearn about cultivating real
brand loyalty around purpose andcommunity that a lot of
retailers, many retailers dosome aspect of, but their
business actually lives andbreathes on it.

(28:31):
So I think there's someinteresting dimensions to that.
And figuring out how you createcircularity in that business is
uh isn't interesting and couldbecome more relevant for more
retailers going forward.

Guy (28:43):
You talked about European retailers, Kathy.
I want to, and you just talkabout circularity.
Can you talk a little bitabout, you know, because I've
seen this right where Europeanretailers seem so far ahead of
American retailers when it comesto sustainability, you know,
being more green, moreenvironmentally friendly, and
circularity.
And then you talk aboutgoodwill.
Can you talk a little bit aboutthat, like what your experience
is, you know, comparing the twosides of the pond?

Katherine (29:03):
Yeah, I mean, I think it's exactly what you've seen.
And honestly, I think it'sreally driven by legislation.
You know, Europe legislatesdifferently on that topic.
They legislate differently onprivacy.
And uh that may happen withoutlegislation, but I think at the
moment uh it's largely thelegislation driving it and big
differences in in how companiesare operating there.

(29:25):
I have seen some instances morerecently where US-based
companies who are not driven bylegislation are sort of being
more forward-thinking on that.
And I think that will that willreally only come from the
consumer.
There's certainly segments ofthe consumer that are
increasingly, uh, even in theUS, increasingly more interested
in that.
Certainly in Europe, that's apredominant population.

(29:45):
Um, but I think we'll see arise that's consumer and market
driven, uh, maybe evenefficiency driven, not
necessarily legislative drivenin in the US.
And that's been the primarydriver to date.

Guy (29:58):
Do you think that some of this Consumer drive driven on
the US side?
I mean, I I always cite theexample of like made wealth,
right?
With their genes and theyrecycle and create linings
inside houses, right, for for uhinsulation.
Do you think that you would goback to customer loyalty?
Like, do you think that's a asort of a hidden way of driving
more customer loyalty?
That hey, if consumers are moresensitive to sustainability, to

(30:21):
be more aware of as a retailer,to then drive more customer
loyalty.
And back to your earlier point,right?
It's not just transactional,it's about the long-term brand
loyalty.
Do you think we're seeing moreof that in the US?
Like those it those thingsintertwine?

Katherine (30:34):
I definitely think there's more and more um
experimentation going on.
I mean, the brilliant partabout that is it drives a trip.
And so it's it is actually insome ways even a short-term play
because returning the item to astore.
And um, if you look at, forinstance, Lulu Lemon's resale
business, uh, you can't do thatthrough the mail.
You have to go into a store.

(30:55):
So it's driving a physical tripas well, which is an
interesting, um, you know, asyou know, a key sales driver and
sometimes difficult to do incertain environments.
And so it's a great, greatshort-term strategy as well.

Rich (31:08):
So I don't know whether I want to ask you your insights on
how the Gen Z consumer mayimpact retail from a long-term
perspective, or for or if I wantto ask you how economic
uncertainty may challenge theretail environment.
I'll I'll let you pick whichone.

Katherine (31:27):
Okay.
I'll maybe be talking abouteconomic and uncertainty and try
and touch the other in theprocess, but because we're
spending a lot of time on thatright now.
And I think the consumer todayis interesting.
I was talking to a colleague ofmine earlier today, um, KD
Thomas, who runs our consumerinstitute, and we were talking
about how consumers will saythey're batting down.

(31:48):
They absolutely do.
They're comparing more prices.
They tell us that they'reholding off on purchasing,
they're changing stores, they'rechanging brands that they buy,
they're waiting for coupons andsales.
But consumers also get fatiguedand reach, you can never forget
retail therapy.
I think, particularly when itcomes to, you know, apparel and
certain items, uh, maybe less soin groceries and whatnot.

(32:10):
But I do think consumers arestill looking for a little bit
of a release valve on the chaos,and they do that through
shopping.
So the patterns that we'reseeing in the environment are
kind of all over the board.
I did an interview last week,and and the guy asked, you know,
he's like, I can't make senseof this.
The, you know, the economicnumbers say this, but then
consumers say that.
And I'm like, yeah, it's reallyconfusing.

(32:30):
And I think you always have totalk about to a particular
consumer on a particular dayabout a particular item to get
clarity because they're thinkingabout it in different ways uh
on at different points in timeand in different categories.
I think medium to long term,there's gonna be a lot of
economic impact on the consumer.
And I think if you couple thatwith how shopping will change

(32:54):
with agenda commerce, how thejob market is gonna change for
younger consumers and the factthat they grew up in a sort of
30-second sound bite mentality,I think that could create a
really interesting shoppingexperience of the future that is
very price-driven, is veryoptimized.
And that optimization maychange, you know, day to day,

(33:17):
week to week, category tocategory.
But I think the consumer isgonna be looking to optimize.
And so companies are gonna needto be able to cater and be the
best in certain ways for certainconsumers at the time when they
need it.
I think that's gonna be areally big shift.
And they'll I don't know theexact timing of how that's gonna
happen.

(33:37):
But if I had to guess, I wouldsay it's over the next three to
five years.

Guy (33:42):
So, do you think, Ketherin, that we're gonna have, you
know, the holy grail of youknow, variable pricing?
So, like, you know, we alwayshear example Coca-Cola machine.
You know, if it's super hot,I'm gonna raise the price of a
Coke bottle from three bucks tofive bucks.
Then it gets cold, I'm gonnadrop my price.
And we're we're seeing thingsright where Delta's come out
with this.
They're gonna use AI todetermine the price of a seat

(34:02):
based on what you can pay, whichkind of makes me feel weird.
But do you think we're gonnaget to that?
Is that where you think we'regetting to?

Katherine (34:09):
I don't think that's gonna work.
I think I think where we'regonna get to, though, is
optimized pricing with ceilingson it.
I actually think it's you see alot of press about how, you
know, oh, there's this fear thatgrocers are gonna use digital
shelf tags to raise prices.
That's not really how thatworks.
They're mostly working to lowerprices to move inventory to

(34:33):
prevent spoilage and waste.
That would be the first leverthat you would pull.
You know, they're trying tomove through as many goods as
possible.
And in price-sensitivebusinesses, like let's just take
grocery as an example, thattraffic and the repeat traffic
and the price perception is somuch more important than the
single margin on a singleproduct at a given time.
So I think people aremisreading that signal, to be

(34:57):
honest with you.
I think mostly it will be pricedeclines and price offers for
more price-sensitive customersat particular times or to move
inventory or to manage, youknow, supply, that type of
thing.
Would there be surge pricing?
I if it happened, I would hopethere would be a ceiling on it.
Certainly you can imaginemanaging demand where you say,

(35:19):
hey, uh, if there was like a runon toilet paper, let's use
that, that, uh, that oddexample.
You know, maybe the firstproduct is a certain price point
and it goes up to manage supplyfor a given consumer, but uh,
or smart techniques, but I Ican't imagine that being an
unfettered, primarily priceraising technique for most

(35:40):
retailers unless it's in aluxury category or a very
obsolete, you know, um scarce,scarce good.

Guy (35:48):
That's interesting.
I I because I give you anexample.
Like I was I took my son to SanFrancisco, we went to a
baseball game, and you go out,and there's all these hot dog
vendors, there's no price.
So they're just charging you,you know, oh, you're gonna pay
five bucks, ten bucks, twelvebucks, six bucks.
It's all you know, I know thegame, but I was just curious to
see like I think it's a greatpoint.
It's like, yeah, it's a movinginventory, not necessarily
catching, capturing another twopercent of margin on an item.

(36:11):
Yeah, I think that's that's agood point.

Katherine (36:13):
Because if you think about it, a lot of shopping will
move to a gentic.
Okay, well, if I have my ownagent, I might train it to say,
hey, I really want to buyproduct A, but I never want to
spend more than you know $5 forproduct A.
And by the way, if you can getproduct A at another retailer
for cheap, you know, I likeretailer, you know, B, but if
you can get it somewhere forcheaper and I get it at the same

(36:35):
time, you know, it's if it'sthis much cheaper, I think
that's the kind of dialoguewe're headed towards.
And so retailers are only goingto be incentive to be
competitive.
This was probably 10 or 15years ago.
There was a CIO who said hereally wanted to build an eBay
like marketplace basically toget rid of inventory.
So they would sell seasonalinventory and whatnot.
So he was like, I wish at theend of the season we could just

(36:56):
create like a bidding system tolet consumers bidded.
Well, I think that's the worldwe're moving to, is more like
bidding to see what they can getfor things, you know, more like
define rule engines and to playconsumer-driven rule engines,
to play in that world, retailershave to be really sharp.
It's not gonna generally be aprice gouging mechanism.

Rich (37:17):
Well, to take that, so if you're left with product at the
end of the at the end of theseason, rather than having
pre-programmed set dates thatyou're gonna mark down by
location, you're looking at thesales velocity, and there's a
trigger that says when thevelocity falls below a certain
amount, lower the price.
So you're managing yourdownside rather than trying to

(37:40):
escalate your upside.

Katherine (37:41):
That's right.

Rich (37:42):
I think that's absolutely right.
I can't say that I've heard alot of conversation from that
perspective.
So it'll be, I think that's init's a very interesting
perspective.
I want to go a little bit moregeneral and then start to pivot
into you've given some greatadvice, but I want to pivot a
little bit more into advice forstudents.
But with all of the experiencethat you've had, are there a

(38:02):
couple of core beliefs orapproaches that have served you
well throughout your career thatyou either had from the
beginning or that you'vedeveloped over time?

Katherine (38:11):
I'm not sure I had any from the beginning.
No, just kidding.
I think over time.
You know what?
A really pivotal one for me.
I read a book that someonerecommended.
I'm a big reader, and uh and anerd and whatnot.
And uh there were two, I thinkit's by the same author, uh,
Leadership and the Art ofSelf-Deception.
And the other one was called umsomething about war.

(38:33):
Oh, I can't remember now.
Anyway, the essence of thisbook really was about how
conflict happens.
And if you think about in theworkplace, there are lots of you
know, conflicts all the time.
They're competing points ofview, they're competing
priorities.
Organizations, largeorganizations are just set up
that way.
Sometimes they're competitionbetween internal teams and

(38:53):
external consulting teams.
And it really changed my viewon how to evaluate those
situations.
Um, and the essence of thisbook basically said a couple of
things.
One is when conflict happens,is that you dehumanize, you
know, it's instead of being, youknow, rich, it's uh it's Macy's
or it's a you know, it's a bigorganization.
And then the other being uhjust not empathizing with a

(39:15):
group and not takingaccountability for what you own.
That was another big piece ofadvice I got that really changed
the game on how I manage my owntime and energy on things is if
a situation is not going wellor there is conflict, is
recognizing what you can do tomake it better.
And you don't have to own ahundred percent of the problem.
You know, in any relationship,there are two people,

(39:38):
organization relationships, etcetera.
You don't have to own 100% ofit, but you have to own 100% of
your 50%.
And so you have to really ownwhat you can own in it and make
sure you're doing what you canto de-escalate things, to lean
in where you need to, to smoothover, to understand more
perspectives, all that goodstuff.
That really was a pivot pointfor me.
And it probably sounds simpleand super obvious, but I think

(39:59):
it made a big difference in howI approached some teams, how we
approach clients, how I how Iempowered teams to do some
things differently that havemassive impact on results.
It really did turn around a fewclient relationships in a big
way within weeks, honestly, justadapting some of that and
building it into how the teamsoperated.

Rich (40:20):
Do you find that there's an abdication of owned
responsibility at times, eitherin the people that you may work
with or in the projects or inwho you come up with?
Are we abdicating decisions todata or AI and not taking
ownership in the results?

Katherine (40:39):
I haven't seen a lot of that.
I've seen more finger pointinguh across particularly large
organizations than I've seensaying, well, the data said
this.
Geez, what's what's happening?
I've definitely seen more ofsort of the like, well, you
know, I would do this, but youknow, this organization, this
silo, this person is doingsomething different.

(41:01):
So I guess, you know, we'llhave to see how that goes.
See, more of that type ofdialogue than abdicating to the
data.
And a very, I guess a goodhealth check when that does
happen with data is to add, youknow, the data's never really
wrong.
The decisions we might makefrom the data might be wrong.
We might not have looked at thefull picture of data, the right

(41:22):
set of data.
But so I think that's then thequestion is like, okay, well, if
we we end up in a bad result,it's not so much the data was
right.
It's like, how what do we missin our data set and how we
thought about it and learn fromit?
And honestly, if you'relearning from that, then it's
it's still generallyrecoverable.

Rich (41:40):
So take that from a student perspective as we as we
talk a little bit more about theadvice.
How do students take moreownership in their development?
Because they're one of thethings that when when we were in
school and we were growing up,I think we were told that many
of the jobs that we were goingto be going for hadn't been
created yet.
Well, if we were told that, Ican just imagine what it's like

(42:00):
to be in college today.
Yeah.
And many of the companies thatthey may go work for haven't
even been started yet, or theyhaven't yet started them.
To what extent do you see thisgeneration having to take more
ownership and control over theirpath?

Katherine (42:16):
Yeah, that's an interesting question.
So I have a daughter, as youknow, Rich, a daughter in
college, and I was chatting withher the other day, and she she
sort of has this idea in mind ofgoing to grad school.
And I was like, look, my bestadvice to you right now is to
have a lot of options on thetable because not only is your
market changing really rapidly,but that's gonna have a knock-on

(42:36):
impact.
So she was interested inapplying to grad school.
I was like, she and she in hermind at the time, she's like, I
don't think that's gonna beimpacted as much.
I'm like, no, but you knowwhat, it's gonna be impacted.
I would expect applications totriple or quadruple because all
those folks who thought they hada career path that's a little
bit soft now are gonna apply tograd school.
That's sort of a well-worncycle.

(42:57):
That's my best advice is yeah,you do have to take ownership.
Nobody owes you a job and it'son you to be curious and kind of
scrappy, I think, and have alot of alternatives.
You know, I think that idea wewere we started out with of um
not having such a set path isreally important right now
because that path could change,it could go away, it could

(43:19):
become oversaturated, I feellike in the blink of an eye
these days.
And so, you know, buildingenduring skills and being
flexible is going to be superimportant.

Guy (43:30):
And to that light too, Kathleen, do you would you
advise college students?
So, like I mentioned, I have a17-year-old to be a college
student in over in over a year,so it's kind of frightening, but
is it important like toobviously be curious, but is
there an undertone where youneed to have at least some
rudimentary AI or dataknowledge, regardless of what
industry you want to go into?
Or how would you advise someonewho asks you that?

(43:52):
Like, oh, I do I need to takeAI classes, even if I want to be
a you know philosopher?

Katherine (43:57):
Yeah, that's good.
That's an interesting question.
My brother actually is aphilosopher, a philosophy
professor.
I think that's good.
You definitely do not need IAI.
No, I'm just kidding.
Maybe you do.
Um it's hard to imagine.
I I don't know what an AIcourse looks like, if I'm honest
with you, because in my mind,the thing about AI is that it it

(44:17):
is sort of a greatdemocratizer.
You know, it's sort of like uhsort of making it less
technical.
I've always thought actually,this is I would say one of the
big misconceptions about um thefield of data and analytics
historically.
A lot of people think it's allabout technical acumen.
I I think five or ten years agothat question would have been
do I need to know how to code inR or Python or something?

(44:41):
And yes, that that in that day,that was a great advantage.
But I honestly think beyondentry level, the most important
skills are kind of curiosity,the ability to structure
thinking, the ability to findconnections and have hypotheses,
the ability to try things thatI think is a big differentiator.

(45:04):
The ability to hear somethingthat sounds a little impossible
and think about and think, yeah,I know how I could do that.
I I'm gonna go try that.
That's a big differentiator, Ithink, in the marketplace and
any data environment.
So uh, do I think they need todouble down on AI courses?
Probably not.
It's probably gonna change aton.
I mean, if they want to, that'sgreat.
But uh is it a requirement?

(45:24):
Probably not, as long as you'reable to do some of those other
things.
Connect dots, be curious,leverage technology.
I think it's it'd be silly notto be leveraging technology, but
I don't know that you have tobe, you know, coding LLMs to do
that.

Guy (45:40):
No, it's good because it's it's I was just on a couple of
college tours with my son, andsome of the questions from the
other kids on the tour wasalways like, well, what's the AI
classes here?
Well, what are you?
And I I don't mean to be, Idon't mean to be snarky, but I
heard the question.
I was like, you you don'tunderstand what AI is, you just
you know you just gravitatetoward this.
But I was interested, I thinkit's a really good answer about
this curiosity.
And I it sounds like in a wayit's what we all did when we

(46:01):
went to college, which is learnto think, learn to make
connections and hypotheses andand push your boundaries.
Don't worry about the toolsbecause you'll use those.
And I think that's interesting.
That's that to me is isrefreshing to hear because
otherwise it's just it feelslike to AI.
And it's like, what does thatmean?

Katherine (46:17):
Yeah, the tools will change.

Rich (46:19):
And I was having a conversation with somebody close
to me who was talking about thecheating that's going on with
AI, because there's obviouslysomebody who's saying, Hey, I
need to do a two-page essay onCatcher in the Rye, and and they
they get something spit out.
But that was happening in the1960s.
You were just paying someone towrite it for you, or you were
just copying cliff notes orwhatever they had back then.

(46:42):
But I'm fascinated with theexample that you have where
students will go into aclassroom, will record the
lecture, transcribe it, use AI,notebook LM, whatever to turn it
into a podcast so they have anaudio version of it to create a
quiz, a tutor that is teachingyou.
I think that's fascinating.

(47:03):
And it's it's basicallydemocratizing information.

Katherine (47:06):
Yeah, it's innovative, it's basically
personalizing a learning style.
You know, that is taking agencyover your own learning and
personalizing the learning styleto something that works for
you.
So I think it's great.

Rich (47:18):
You mentioned earlier about the democratization of
data and that it's table stakes.
And I've heard you throughoutour conversation.
You mentioned earlier about thedemocratization of data and
that it's table stakes.
And I've heard you throughoutour conversation go back to
curiosity and and diggingthrough it.

(47:40):
Are we entering an age where itwill make data available to
everybody who, in a way, thatthey don't have to know how to
connect the dots betweensystems, they don't have to
learn how to code, they justhave to be curious.
And does that free up morepeople to think creatively and

(48:02):
innovatively and push theenvelope?

Katherine (48:04):
Yeah, I mean, absolutely.
I think the curious will win inthe future.
Because if you can, if you cansort of create the right uh
connections, if you can ask theright questions, well, how does
this relate to this?
How does, you know, how do Ithink about you know this in the
context of that?
It's amazing.
Plus, I think there will be awhole population of people that
today don't have access to a lotof data or information or tools

(48:26):
or techniques that this isfairly inexpensive and widely
available.
And so there are whole parts ofour world that are gonna
participate in innovation thathaven't in the past.
So yeah, I think it's gonna bea real game changer.

Rich (48:40):
So as we get ready for the rapid fire round, that's my
cue, Gee, to uh I'll let youtake first and third so you can
you can start thinking about it.
Nice.
Yep.
I was having a conversationwith somebody today, and this is
the first time that I've askedthis question on the podcast.
If you could ask students todaya question, what question would

(49:02):
you ask?

Katherine (49:03):
Gosh, that's a that's a fascinating question.
Just full stop.
I don't know.
I would ask them maybe becauseI'm a parent, I'm always kind of
curious about parenting andwhatnot.
I'd probably ask them what partof their childhood was uh sort
of the most positive in theirmind?
Like, you know, what was theinflection point for them that

(49:24):
was the most positive?
And maybe the flip side ofthat, what do they think they
did that they wish they hadn'tdone, or what do their parents
do um they wish they hadn'tdone?
That's probably what I'd ask.
But that's just pure personalcuriosity.

Rich (49:36):
I'm not gonna turn that question on you.
Um I'll I'll be fair.
But how about in terms of ifyou have somebody in front of
you who is a who's emergingtalent, a college student, maybe
not a college student, but isemerging talent.
What question would you askthem to kind of challenge their
thought process and get themthinking about the future?

Katherine (49:57):
Yeah, I'd probably come up with some sort of look,
I cheat a lot in interviews.
I'll ask people about problemsI'm noodling on at the moment to
see how they think.
And if they come come, frankly,if they can come up with a
better answer, uh, you know,what they would do.
But I think I would asksomething that would get to
their nature of creativity andcuriosity and uh, you know, ask

(50:18):
them maybe how they think agendacommerce will change how
consumers behave and why, youknow, what that would mean for
their generation, how is beingraised on kind of digital short
sound bites, how does thatimpact their decision making?
How do they think that willchange commerce of the future
and just see how the thinkingprogresses?

(50:38):
So it's not really like a um abehavioral question, it's just a
a little bit of a case, but Ilike to see how people think and
how they structure problems andwalk through them more than you
know, set the answer.
You can kind of say anythingyou want about how you are, but
how you think is reallyinteresting.

Rich (50:55):
I think sometimes the reason I teach is because it
gives me a semester-long focusgroup, and I learn how to
communicate with very divergentpersonalities.

Katherine (51:06):
Yeah, how people are thinking.

Rich (51:08):
And so whether they paid me or not, those two things are
very valuable to me.
I bet.

Katherine (51:14):
I mean, it's fascinating because every
generation grows up in aslightly different context that
influences how they think andhow they perceive the world and
move through it.
And so, I mean, that's a supervaluable input, Rich.
It's a whole new data set.

Rich (51:28):
All right, so rapid fire.
We gave you a few questions.
We're probably gonna pull oneout of the air.
If I know Ghee, he's going to.
Why don't you go ahead and uhfirst thing that pops into the
mind no science needed, justpure art?
Gee, go for it.

Katherine (51:43):
I'm awful at these, by the way.
One time I was doing I'm reallybad.
One time I was nominated for anaward and they did one of these
things, and then and theirquestion was if you were a
cheese, what cheese would yoube?
And I swear to God, I couldn'tthink of a single cheese.
And you know what I said wasVelveeta.
And I went back and I told myteam, and they're like, Have you
even eaten Velveeta?
I was like, No, I don't, Idon't know why I said that.

Guy (52:06):
Velveeta cheese is delicious.
I as Richard, I'm gonna pullone out of the air.
And and since you mentioned youlived in London, I want to ask
you this question.
Are you blue or are you red?
And see if you get thequestion.

Katherine (52:17):
Oh my gosh, I don't know what that means.
Is that a soccer team orsomething?

Guy (52:21):
Yeah, are you Arsenal or are you Chelsea?

Katherine (52:24):
Oh, I don't know.
I didn't, I didn't take upsoccer while I was there.

Rich (52:27):
Oh, dude, I'm gonna I'm you're gonna have to ask another
one because I'm Fulham, I'mblack and white.
So seriously, I came fromLondon and that's just in the
Premier League.
You got um uh I'm gonna leavethis in just to just uh you know
follow your reputation.

Guy (52:41):
I've been to Craven, I don't yeah, I've been to Craven
Cottage.
It's a great it's a greatstate.
It is all right.
Um, if you could pick one cityyou've never been to, which
would it be and why?

Katherine (52:54):
Probably a city in well, actually, you know, this
is embarrassing.
I've never been to Hong Kong.
And so I would I definitelywould love to go.
I would definitely love to goto Hong Kong.
I don't know why I haven't beenyet.
It feels that everyone in myorbit has been and raves about
it, and it's their favoritecity, and I've never been.

Rich (53:11):
So it's a great city.
One of my best experiences inHong Kong, and I may cut this
out depending on how it comesout, but was with uh our
sourcing and design team in HongKong in an Italian restaurant
with a Mexican mariachi bandmade up of Filipinos singing
Madonna's like a virgin.

Katherine (53:32):
That sounds amazing.
Sounds about right for HongKong.

Rich (53:36):
And it was just absolutely, yeah, and and and I
know one of the personslistening to this is gonna laugh
right now.
I I have to ask, you've you'vedone a lot of speaking.
Do you have a hype song or awalk-on song that you default
to?

Katherine (53:52):
I really don't.
I really don't, but I do likehype songs, and there'd probably
be things like I lovesoundtracks, I like um like the
greatest showman, you know, kindof like real hype hype you up
kind of stuff.
Is that yours, Rich?
Do you have one?

Rich (54:06):
So no, I I listen to all kinds of music, but I will say,
and I was gonna memorialize thefact that my daughter just got
her first A plus in hercollegiate career.
And no guesses, the course wasprinciples of marketing.
So dad's really proud, and no,dad didn't do the work for her,

(54:27):
this was on her own.
But the greatest showman forher, and I'm a huge, huge Ackman
fan.
The greatest showman for her isthat's her lift up.
If she's having a bad day ormom's having a bad day, you
listen to the you watch andlisten to the greatest showman
from now on.

Katherine (54:43):
You just I mean that's it.
I'm getting like imaginable.
Oh, and I'm also a big Swifty.
I I still I mean it's like acouple years since her tour and
all, but it's that's fun.

Guy (54:53):
All right.
I want to ask one morequestion.
So I had the first so this oneis also a little bit off
training.
If you could time travel, wouldyou go to the future or to the
past?

Katherine (55:04):
I might.
Oh, that's a tough one.
I think I might.
I there was a time where I'dsay to the future.
I think now we'd say to thepast.
I had this bizarre obsessionwhen I was a child with Little
House on the prairie.
I just thought it was aninteresting time period.
It was something about peopleexploring and you know,
uncharted territory and pushingthrough all the you know quite

(55:26):
physical challenges there that Ifound fascinating.
I don't know if I would chooseto go back, but I think I might
go back.
I also think the future itfeels, you know, not to be a
daunting, but it feels veryuncertain at the moment.
So the past.

Rich (55:38):
I would go to the past, yeah.
I think we have to ask that.
I think we have to add thatquestion.

Guy (55:43):
It's actually a really I've never heard that question
asked, and it's uh kind of aperfect question to I've spent
many hours debating this withpeople about well, which what
why would you go the in like howfar in the past or how far in
the future?
Would you go like in yourlifetime future, like or would
you go like a thousand years inthe future?
Or would you go now?
You can't impact history.
You know, you can't do the backto the future, go back in time,

(56:05):
give your you, you, you as a20-year-old the the sports
almanac and then come back.
No, but you can observe.

Rich (56:10):
Yeah, and Catherine, I think I I would, I don't I would
have struggled whether I wouldhave said past or future, but
I'm with you.
I don't know that I would wantto see the future right now.

Katherine (56:21):
Plus, then you see all the impacts of things you've
done, which in the context youdid them in made sense.
But you just kind of know whenif you think of all the things
that have happened before usthat at the time made complete
sense, but now you're like,well, geez.
Now we're here because of that.
I don't know if I want to livewith that uh burden.

Rich (56:41):
Well, and the reverse is I'd like to go back and
understand what truly happenedin history because it's always
told in different ways.
Yeah.

Guy (56:49):
Yeah.

Rich (56:50):
All right.
Well, Catherine, thank you verymuch.
This is fantastic.
We will have to have you onagain.
We'll we'll pick a topic andhave a round table, but love the
uh love the discovery of yourjourney and the advice for our
audience and and emergingtalent.
Uh, thank you very much fortoday.

Katherine (57:07):
Thank you guys.
Always fun.