Growth Thresholds & Growing Pains with Marcel Petitpas

Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Marcel Petitpas (00:00):
I can't tell you the number of times that

(00:01):
we're like meeting with teams.
We're starting in a discussionand we're talking about, let's
call it utilization rate.
We'll just pick a random metric,but this happens with all
metrics and we start talkingabout utilization there's a
moment where I have to be like,okay, pause.
Project manager defineutilization for me, and they're
like, oh, you know, billablehours over capacity.
I'm like, great.
What is someone's capacity?
Exactly?
What does it

Harv Nagra (00:20):
include what does it not include?
They give me an answer.
Then I ask the CFO

what's your definition of capacity?
Oh, it's different.
Fascinating.
Then I ask the CEO, what's yourdefinition of capacity?
Oh, it's also different.
Fascinating.
Okay, so we're all talking abouta metric that we think we're
speaking the same language.
We don't understand each other.

Harv Nagra (00:35):
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(01:18):
Now, back to the episode.

(01:52):
Hey all.
Welcome back to the podcast.
Whether you're running a 20person agency, you're creeping
past 50 or surpassing a hundred,at some point you might find
that your ops stop keeping up.
Suddenly, you can't see thenumbers clearly, forecasts start
falling apart.
People are busy, but profit isflatlining.
Your management team isfrustrated at juggling too much.

(02:13):
And the systems that got youhere, they're holding you back
from what's next?
It happens to the best of us.
One of my old bosses, if he'slistening, will remember a
picture I showed him of a leakypipe held together with duct
tape and about to burst.
I was trying to make a point.
So in this episode of thepodcast, we're breaking down the
growing pains that hit agenciesat different thresholds and how

(02:34):
to overcome them before theybecome blockers.
We get into what actually needsto change, whether it's your
data practices, operating model,or how you make decisions and we
bust a few myths along the way.
So if you've ever felt like youragency's ops are built on duct
tape and good intentions.
This one's for you And joiningme for this conversation is
someone who probably knowsagency numbers better than most

(02:57):
of us know our own bank balance.
Can you guess?
It's Marcel Petitpas, CEO, andco-founder of Parakeeto.
Marcel is the go-to expert whenit comes to helping agencies
improve their profitability,pricing, forecasting and
financial systems.
He's worked with hundreds ofagencies all over the world,
from boutique shops to globalteams, and he's got a knack for

(03:19):
making complex financialconcepts feel surprisingly
clear, he's also the host of theAgency Profit Podcast, a killer
resource in its own right.
Let's get into it.
marcel, welcome to the podcast.
Thank you so much for beinghere.
You're the first fellow CanadianI've had on the podcast, so yay
us.

What a time to be coming together like this.

Harv Nagra (03:40):
Exactly, exactly.
Commiserating.
Um, but we'll, we'll do that offthe record.
So Marcel, you talked to clientsall over the world.
are there any trends or shiftsyou're noticing compared to the
past couple of years?

we're recording this podcast on April 3rd,

Harv Nagra (03:55):
mm-hmm.

obviously a lot of stuff changing in the world.
And of course all of this isaffecting agencies, but I think
if we crop out from this momentin time, that feels very acute,
there have been a lot of broadsweeping changes from my
perspective that have beentaking place over a very long
time, at the most macro level,the industry has been maturing
in the same way that everyindustry matures in that margins
have just been getting worseover time.

(04:18):
Competition has been increasingas all of the little barriers to
becoming an agency have gonedown.
opportunities to do digitalthings have proliferated, and
the cost of getting into thosedigital things and educating
oneself on those digital thingshas decreased, what we found,
especially in the last fiveyears or so, is that there have
been some major accelerants tothat margin pressure.

(04:38):
So

Harv Nagra (04:39):
Hmm.

a lot of people are realizing that it's
happening it's happened a lotfaster in these last few years.
And those two things have beenthe pandemic, which I think
leapfrogged remote work and the

Harv Nagra (04:49):
Yeah.

of competition by like a decade, almost
overnight, where

Harv Nagra (04:53):
Mm-hmm.

people were like, wait, why are we hiring
agencies locally?
When our team isn't even localanymore, and now all of a sudden
it's like, oh wait, we can getreally high quality work from
geographies that have a massivecost advantage.
And so that really acceleratedprice pressure in the agency
space while at the same time youmight recall during that
pandemic, the cost of onshorelabor went up.

(05:15):
to 40% across most roles insideof an agency, especially
specialized in technical ones,

Harv Nagra (05:19):
Hmm.

engineers, designers, project managers,
account managers, like therewere salary reports that got
published that showed massiveincreases in staffing costs
throughout that time.
So you were getting squeezedfrom both directions through the
pandemic.
And then just as everyone'srecovering from that tidal wave.
AI shows up and enters the

Harv Nagra (05:35):
Yeah,

and of course we

Harv Nagra (05:36):
I.

still haven't really seen the full breadth of
the disruption, but I think wehave seen some impacts and the
the worst is yet to come and thebest is yet to come.
You you could be on either sideof that equation.

Harv Nagra (05:47):
Mm-hmm.

forces, I think, have really recently put
a lot of focus on much tougherit's becoming to run a
profitable agency.
How much less you can get awaywith not actually being
sophisticated about youroperations, having your data
together.
of course with the economyhaving been challenging, let's
put it that way, the last fewmonths, that has also, I think,
increased the focus on this.

(06:09):
And so the short summation ofthis is, I don't think anything
has changed, but I think thatrecent events have pushed things
to go a lot faster in a shorterperiod of time, and therefore
people are more aware of it.

Harv Nagra (06:19):
Marcel, remind me that salary increases that
you're referring to.
Was that due to that"greatresignation" thing that was
happening where people weremoving around a lot and getting
quite, pushy with theirexpectations?

Yeah, so I, I'm not gonna pretend to have a full
kind of understanding of themacroeconomic factors behind
this, but my high levelunderstanding of why that
happened was twofold.
Number one, there was not a verystrong incentive to work during
Covid because there was so muchrelief fund available, right?

Harv Nagra (06:48):
Right.

of that meant that there wasn't a lot of
pressure for people to work.
There was this moments in timewhere the the power dynamic in
the labor market, especially theskilled labor market, shifted in
a big way towards The employees.
We did see

Harv Nagra (07:01):
Absolutely.

resignation, people

Harv Nagra (07:04):
Mm-hmm.

The push to remote work, I think really
opened up a lot of opportunitiesfor people to

Harv Nagra (07:09):
Mm-hmm.

in different geographies.
And so, Yeah, all of thosethings really contributed to
this pretty steep hike in thecost of talent, especially as
the demand for digital talentwent through the roof in that
moment.
cause you

Harv Nagra (07:20):
Right.

there was.
Massive industries that were waybehind on digitization that all
of a sudden needed to catch up.
And so the demand for agencyservices went through the roof.
I know a lot of e-commerceagencies that did very, very
well.
Those two years had multi

Harv Nagra (07:35):
Mm-hmm.

percent year over year growth rates.
So they're trying to hiretalent.
big organizations are trying tohire that talent in-house.
So those things all coalesced ina perfect storm and that shot
the kind of leverage for peoplethat had digital skills very,
very high in that time period.

Harv Nagra (07:51):
you also work with people in all different markets
agencies in the uk, Canada, andthe us, Australia and New
Zealand, can you share anyobservations about differences
that you might see in thesemarkets?

be candid, the, the differences are very minor.
the UK, I would say is sufferinga little bit worse than
everybody else, mostly becauseof how devalued the pound has
become over

Harv Nagra (08:12):
Hmm.

few years.
I.
has kind of been an additionalblow on top of the broad
sweeping economic factors that Ithink are affecting everyone.
Mm-hmm.
major one was the bottom fallingout of the tech industry in late
2023 or late 2024, where youmight recall like Facebook's
valuation, like all these bigtech companies, the MEG seven,
their valuations got cut.
of a sudden, investors wereusing the word profit, which

(08:35):
they'd never really done before.
IPO market dried up.
M&A started to dry up under the,the previous administration
where, they were basicallyshutting down any major
acquisition.
And so the flow of venturecapital into the tech industry
really stopped,

Harv Nagra (08:49):
Hmm.

is what has rebounded that.
But we, we still haven't reallygotten back to m and a.
We still haven't really gottenback to IPO, so there's still a
bit of a, a, tenuous environmentthere.
That was one big blow.
And then the second thing thathappened was a lot of the
economic uncertainty that camewith this most recent election
and that we're still kind ofdealing with now, where there's
essentially a global trade wargoing on.

(09:09):
Nobody really knows what's gonnahappen next, and therefore we're
not really in decision makingmode.
So the thing we're hearingacross every place in the world
is no one's making decisionsbecause no one knows

Harv Nagra (09:20):
Hmm.

next.
Therefore, we have stalledprojects, we have delayed
projects, we have contracts outthat haven't been signed,

Harv Nagra (09:27):
Yeah.

sales cycle has basically slowed to a crawl.

Harv Nagra (09:29):
Mm-hmm.
Yeah, good point.
Because I've been reading acouple of LinkedIn posts that,
you know, don't worry,service-based businesses are
exempt from tariffs and all thatkind of stuff.
But,

Yeah.

Harv Nagra (09:40):
our customers may not be.
So before we get into today'sdiscussion, bringing it back to
kind of agencies andprofitability, can you think of
any mistakes agencies make, ofany size when thinking about
their kind of finops?

I can think of a lot

Harv Nagra (09:52):
Okay.

that they make when they think about their
finops, I'll summarize thebiggest one

Harv Nagra (09:57):
Mm.

thinking of finops in, I'm gonna call it the
old school.
Way, which is this linear way ofthinking,

Harv Nagra (10:04):
Okay.

we have to kind of go back to time and materials
era in services, which was notthat long ago, couple of decades
ago, when the defacto businessmodel was time and materials
billing.
You get a client, you give theman hourly rate.
You work that number of hours,then finance reconciles all of
that.

Harv Nagra (10:21):
I.

Like that billing model is inherently
retroactive.
We don't know how much we'recharging the client until we've
collected the time sheets.
Stacked it up, billed theclient, right?
So made sense at that time forfinance to be seen as sort of
the the primary owner ofmeasuring the performance and
profitability of the businessbecause the finance workflow,

(10:42):
which is inherently retroactiveand is about reconciling data
from the past.
Was well suited to thatfunction, and so you really kind
of had this like sales went outand sold things.
Delivery did things, and.
things flow from sales todelivery and then to finance

Harv Nagra (10:56):
Yeah.

But today, with the proliferation of alternate
billing models, very

Harv Nagra (11:02):
few

firms are exclusively billing on time and
materials.
Most of'em have multipledifferent billing models.
There's also been aproliferation of different
staffing models, so most firmshave full-time employees,
part-time employees,contractors, white

Harv Nagra (11:14):
Mm-hmm.

people that are somewhere in between all of
those things, right?

Harv Nagra (11:18):
Yeah.

of those complexities have made it such
that.
You don't have such a linearflow of information and
timeliness is becoming moreimportant and the complexity of
operations has gotten muchhigher.
And so finance really should nolonger be seen as the place that
we push all of this informationand then have them try to figure
out what's going on.
And I think

Harv Nagra (11:36):
Yeah.

people are recognizing that.
This is not really a fair thingto be asking them to do because
it requires so much context andit requires so much complexity
to really interconnect what'shappening in sales, what's
happening in operations towhat's happening in finance, and
get a real picture of how thebusiness is performing and how
changes to the business affectthe rest of those things.

Harv Nagra (11:57):
Mm-hmm.
Absolutely.
And I think it's just soimportant that that kind of
education trickles down toliterally everyone, reminding
themselves that we're here tomake money and here are the ways
we need to do that, and therules we need to play by in
order to make sure that we're,profitable, right?

Yeah.
Yeah, and I think that thebiggest change there that has
really increased the level ofownership that I think business
development and operations needto start taking over This is
that it's no longer as simple asan hour multiplied by a rate
equals revenue

Harv Nagra (12:26):
Mm-hmm.

an hour multiplied by cost equals cost,

Harv Nagra (12:29):
Mm-hmm.

things have become abstracted from each
other, and so there's a lot morenuance to figuring out like how
are things actually going andhow

Harv Nagra (12:36):
Right.

is our business relative to how healthy it could
be.
Theoretically.

Harv Nagra (12:40):
Yeah, I wonder if you can generalize a bit that
when you start working with anew agency, can you make any
kind of broad observations aboutwhere financial maturity tends
to be?

yeah, so to your point, the level of
financial maturity is quitebroad.
I'm gonna abstract that even andsay profitability management is
quite broad.
'cause you could focus onfinance.
I've run into lots of firms thathave, I.
Incredible finance departmentsand they still have no idea
really what's going on from likea profitability management
perspective because finance isonly one small piece of

Harv Nagra (13:10):
that puzzle

And I would argue the least important piece
of that puzzle, far lessimportant than getting a grip on
operations data.
that varies a lot.
I spent a lot of time in thesoftware industry.
My co-founder, Ben, he spent alot of time in the home services
industry.
And you might find this kind ofalarming, but I would argue that
the average level ofsophistication for like a
plumbing or landscaping companywhen it comes to this stuff.

Harv Nagra (13:33):
is actually

quite a bit higher on average for the same
size of company than what we'veseen in the agency industry.
The margins are a lot worse inhome services than they have
been in agency.
But now

Harv Nagra (13:42):
Hmm.

to get to that same place where it's like, oh,
we really need to take thisseriously, otherwise gonna start
failing as a business.
We're gonna start losing money,and it's becoming increasingly
challenging.
So I think agencies are behinda, because they didn't really
need to pay attention to this,and B, because a lot of
creatives that start firms, thisis not really what they're
interested in.
And so they're gonna put

Harv Nagra (14:00):
Not at all.

long as they can.
Right.
As long as the money's

Harv Nagra (14:03):
Yeah.

they don't have to deal with this stuff, then
that's generally gonna be theirtendency.

Harv Nagra (14:06):
that's probably the trigger that gets'em to kind of
start working with yourselvesand stuff like that is when
things get a bit scary maybe.

That, or they wanna sell, and then all of a

Harv Nagra (14:14):
yeah.

start doing the math on like, oh, every
additional dollar of profit Imake, I can like 10 x that when
I sell.
Well, okay,

Harv Nagra (14:20):
Mm-hmm.

that's a little more interesting.

Harv Nagra (14:22):
Mm-hmm.

to actually rewarded for that discipline.

Harv Nagra (14:27):
Right.
So.
I talk about business maturityhere a lot, and that's not
necessarily tied to headcount,but I'm wondering if we were to
look at headcounts, Marcel, whatthresholds do you notice?
Agencies start to outgrow theirsystems and processes and need
to level up.

the simple answer is every time a layer of
management is getting installedin the agency, that tends to
correlate to one of these sortof"ceilings", and

Harv Nagra (14:52):
there's a lot

of vectors to those ceilings in terms of what
makes them challenging.
Some of them are tactical, someof them are strategic, and some
of them are psychological andemotional, and they have to do
directly with the founder'sability to grow to that next
level of leadership in theorganization.
So

Harv Nagra (15:07):
Mm-hmm.

one.
It It depends on how manyfounders there are, but let's
assume that it's like a, a onefounder company.
The first one's around 10-ishgive or take a handful of
employees.
So you're getting to that likemaximum number of direct reports
and usually at that stage, thefounder's having to install that
first layer between them and theclient work.
So you need some level offinancial acumen at that point
in time to know what's going onbecause you're, you no longer

(15:30):
have your finger on the pulsedirectly with every client.
the next major one, this is theone that we deal with a lot at
Parakeeto, is that first reallevel of middle management,
which tends to

Harv Nagra (15:41):
Hmm.

around 30 ish, give or take, let's say five to
seven employees where you're,you're finally having like.
or an executive suite, and thena layer of people that are kind
of managing each department inthe organization, you probably
have like a person overseeingdelivery, a person overseeing
sales, a person overseeingoperations, and maybe the CEO's
playing one of those roles.

(16:02):
But you really kind of have asophisticated layer at the
middle, and that is usuallywhere.
You really start to feel thepain of not having any way to
measure how everyone's doing andhold them accountable.
And I think more importantlythan holding them accountable,
empower them assess their ownperformance and to be able to
make decisions and make judgmentcalls based on objective

(16:24):
information that everybodyagrees on.
And that last part thateverybody agrees on is nuanced,
but should not be.
cause it tends to be one of thebiggest challenges.
then similarly around 60 ishemployees, give or take 10,
that's when you're installing aC-suite.
So you, at this scale ofbusiness, typically you have
three levels of management.
You have your executive levelmanagement, you can have your

(16:44):
middle level management, andthen you might have some kind of
leadership at the delivery levelof the organization.
And

Harv Nagra (16:50):
Mm-hmm.

there needs to be.
Much more structure, much moreclarity to how not only we think
about measuring the business,but now we have to get into the
systems measuring at thedifferent levels of the
business.
And there's so many traps atthat point that have to do with
actually letting go of trying toconnect every little thing that
happens at the bottom of theorganization to the

Harv Nagra (17:13):
Hmm.

at things at the top of the organization.
'cause those lenses are oftenquite different and require very
different structures of data.

Harv Nagra (17:19):
Mm-hmm.

where we start to get into a lot of sort of
architectural systems levelchallenges that also require a
change of thinking, in terms ofphilosophy around managing data.
So that was a long answer, but30, 50 is, is roughly where we
see those major roadblocks.

Harv Nagra (17:34):
That resonates with me as well, Sometimes I think it
tends to be quite reactive.
These kind of challenges arebeing addressed when It's
already started to cause someissues.
Right.
So, hopefully we're giving somepointers to anybody coming up to
those thresholds today.
I wonder If we can go througheach of those in a little bit
more detail.
Marcel.
I think you were saying 10 to 30size initially.

(17:55):
What are the things thatagencies of this size might be
experiencing and facing, andwhat are some of the problems or
pitfalls that you tend to see?

so agencies need to understand that it's
alarming how often they don'tunderstand this is their
business model is capable of.
And so I ask this question allthe time to agency owners, which
is, if everything wentperfectly.
How profitable would your agencybe?
How much revenue would you make?
What would you spend on deliveryon overhead?
How much net profit would thebusiness generate?

(18:23):
And

Harv Nagra (18:23):
they often

don't have an answer to that what they respond
with is their goal.
And I'm like, okay, well I canappreciate that that's your
goal, but mathematically, whatis the limit of what your
current business model isdesigned to do?
And it kind of catches them bysurprise.

Harv Nagra (18:36):
Hmm.
This

is actually just a math formula.
You have a certain number ofpeople on your team.
They get paid a certain amountof money, they work a certain
number of hours per year.
There's a

Harv Nagra (18:44):
certain number

of those that could possibly be used to earn
revenue doing client work.
You have a way of pricing thatsets a target for what you
expect to earn from that, thatformula.
It's pretty straightforward.
you how much money you can make,and then you should
theoretically have a model forwhat do you expect to spend on
things overhead, your office,you know, sales and marketing,
et cetera.
So

Harv Nagra (19:05):
Mm-hmm.

be an understanding of there is a best
possible outcome, the way thatthis business is currently
structured, what is that?
and that container is soimportant because without it, do
you interpret any information,any actual measurement of data?
Oh, our utilization rate is 52%.
Is that good or is it bad?

Harv Nagra (19:23):
Yeah.

answer the question unless you know what it
theoretically could be.
Our average billable rate onthis project was$178.
Is that good or is it bad?
Well, you can't answer thatquestion unless you know what
you expected it to be in thefirst place.
And so the first fundamentalthing is, do they understand
their model?
is that model capable ofaccomplishing their goals as an
organization?
And is it based on assumptionsthat are realistic?

(19:46):
that's kind of the first majorthing.
And then it ties into pricing,which is another area that I am.
I'm alarmed at how few firmsactually understand how
profitable they expect to bewhen they've sold something.
They know what their rate is.
They often don't know why theirrate is what it is.
They

Harv Nagra (20:02):
know how

many hours they think it's gonna take.
They multiply it by the rate.
But when I'm like, okay, butwhat's the margin that you
expect to make on that?
And how do you know that that'sthe right amount of margin to
support the rest of thebusiness?
Very few firms have an answer.
So those would be the first twomajor components is make sure
you understand your model.
and

Harv Nagra (20:17):
make sure when

you sell things, you have an
understanding of how much moneyyou expect to make.
we can kind of start to get intomore advanced things like
forecasting and some basicfeedback loops to make sure that
we're on track.

Harv Nagra (20:27):
so at that size, Marcel, what do you think are
the most important things toaddress that can have the
biggest impact?

Yeah, so assuming that you have a basic
understanding of your model andyou're pricing things
intentionally, right, so you

Harv Nagra (20:41):
Mm-hmm.

you're selling things in such a way that you're
setting the business up forsuccess.
The the next three challengesthat we tend to see people face
in that journey from let's say10 to 30 plus, is number one.
are having to step back from theday to day, so they're no longer
gonna be in, be in touch withwhat's going on in the business.
They need to now empower otherpeople to be accountable to and
measure results.

(21:02):
And the timing of hiring andletting people go and, and
really managing staff relativeto demand,

Harv Nagra (21:09):
Yeah.

significantly more important.
So the ability to forecast andin particular forecast at the
executive level, which I thinkis, is very different than
forecasting at the deliverylevel.
And we'll, we'll talk aboutthat.
So.
On that point, the first majorpiece at this point that we like
to install or that we find isvery helpful is something I call
top down forecasting.

Harv Nagra (21:29):
Mm-hmm.

top down forecasting is really the idea
that when you're at theexecutive level in an agency,
the conversation at that levelaround staffing tends to look a
lot like, what if this, that, orthe other thing, right?
What if we close these fourclients that are on in the
pipeline?
What if we don't, what if they

Harv Nagra (21:46):
Yeah.

month instead of this month?
What if this person who seems alittle disgruntled quits, what
if we were to shift thisperson's role and put them over
here instead of over here?
what, if any, combination ofthose things were to happen?
And so it's, it tends to be avery fluid conversation where
we're in evaluating a wholebunch of different potential
outcomes and then makingdecisions based on our analysis

(22:07):
of all of those potentialoutcomes, because we very rarely
have perfect information.
the system for forecasting inorder to facilitate that kind of
conversation and decision makingneeds to be really quick, needs
to be really fluid, and oftenneeds to be abstracted away from
detail, the big thing and theproblem that I see most agencies
face is the way that they'reforecasting is by trying to take

(22:28):
what their project managers aredoing

Harv Nagra (22:30):
mm-hmm.

to decide, you know, who's working on what task
and using that to have this veryfluid conversation.
And it does not work.
It doesn't

Harv Nagra (22:39):
Hmm

because you can't go and update a thousand
task assignments every time youwanna run a different scenario
because it takes.
In some cases, hours to do that.

Harv Nagra (22:48):
mm-hmm.

majority of the time when you get to the
meeting, something's going to beout of date because there's too
much change happening at thatbottom level.
And so it's not to say that thatbottom up resource planning that
project managers do isn'tuseful.
It's extremely useful for thejob that they're doing,

Harv Nagra (23:05):
Right.

very useful for the job the executive team is
trying to do, which is overbroad time horizons.
Highly variable sets ofuncertain data,

Harv Nagra (23:16):
Hmm.

bode well for a system like bottom up resource
planning, which is kind ofinherently based on the
assumption that we have a shorttime horizon.
We have a high degree ofcertainty with the

Harv Nagra (23:25):
Yeah.

that's the first thing is, is installing a
top-down forecasting system thatis radically simplified, and

Harv Nagra (23:32):
Mm-hmm.

kind of mental shifts that we have to work
through with clients is The factthat this is really simple and
lacks detail is the feature.
Not the bug and the

Harv Nagra (23:42):
Hmm.

to a degree from what your project manager
doing again, is the feature, notthe bug.
So it doesn't rely on anunrealistic expectation, which
is their forecast will be superduper up to date quick to update
at any given moment in time.
'cause that's just not reallywhat that system is designed to
do.

Harv Nagra (23:59):
correct me if I'm wrong and I am probably wrong,
but the way your kind ofapproach to this works is based
on trends with past projects ofa similar size and scope.
Is that kind of the idea?

So There is a point, there is a level of
sophistication where that canstart to happen.
We, that typically is somethingthat we'll work on later.
So, and when we talk about thisnext stage of growth, the kind
of 50 plus,

Harv Nagra (24:21):
Okay.

scale you have enough maturity and enough data
to be able to start using Yeah.
Time tracking data that's

Harv Nagra (24:29):
Okay.

well structured

Harv Nagra (24:30):
Hmm.

your own algorithms to say, oh, we're
selling a website.

Harv Nagra (24:33):
Hmm.

is roughly $400,000.
Well, if you have enough data,you can literally just build
linear algebra that says, okay,well that's roughly this many
hours of design, this many hoursof engineering, this many hours
of project management, becausethat's what

Harv Nagra (24:44):
Yeah.

in the past.
then,, dial that up or down alittle bit.
so you can start to, with wellstructured data, do that
algorithmically in the early

Harv Nagra (24:52):
Okay.

It's really as simple as saying, let's take the
scope of work that sales puttogether and just simplify it
down to.
Three to six buckets, how many

Harv Nagra (25:02):
Hmm.

hours, engineering hours, project
management hours and strategyhours is this going to take?
Then let's also resource planlike bucket all of our team
members into one of those threeto six buckets and

Harv Nagra (25:13):
Mm-hmm.

the same.
And so now we have this reallysimple system where everything
that sales produces.
It's going to eventually getbroken down into a gazillion
tasks.
Don't worry, PMs like you stillget to do that

Harv Nagra (25:23):
Yeah.

right?
Great.

Harv Nagra (25:24):
Mm-hmm.

it all has to ladder up to this much simpler
data schema, which is thisabstraction layer of what we
call these role categories.
What role category.
Is this time estimated to.
And then similarly, all of ourresourcing, we can break it down
and assign individuals to tasks,but everybody has to ladder up
to, again, one of these rolecategories.
So we have this much simplerspace where if we wanna update a

(25:46):
project and say, oh, what ifthis project closed next month
and the scope increased by 20%?
Well we can update a date fieldand five estimates of hours.

Harv Nagra (25:56):
Hmm.

of updating a date field and an estimate of
hours for 300 tasks,

Harv Nagra (26:02):
Yeah.
Yeah,

the

Harv Nagra (26:03):
yeah.

that, that, and you multiply that across 50
projects you have a, anexponential decrease in the cost
and complexity of maintaining a,not precise, but an accurate
forecast for the purpose, whichis trying to navigate and
simulate outcomes with a lot ofuncertainty that is

Harv Nagra (26:21):
Right.

those outcomes.

Harv Nagra (26:22):
I was talking to, somebody a couple of weeks ago
that was trying this approach.
I think people have thattendency to want to go bottom up
when they just don't know anybetter and, they were struggling
with this, saying it's not superprecise.
and the point was that itdoesn't need to be that precise
at this stage and theapproximation is better than not
having it at all.

A hundred percent.
This is kind of the fundamentalidea behind not just this, but a
lot of the other ways in whichwe approach things, which is
that

Harv Nagra (26:49):
hmm.

and accuracy are not only not the same thing,
they're often inverselycorrelated with one another.
Right?

Harv Nagra (26:53):
Hmm.

so a good example of that is you could try
to get to this high levelinsight of how does our design
demand change over the next sixmonths if we do or don't get
these, you know, projects.
if.
There are 5,000 task assignmentsthat make up that insight, and
each of those task assignmentshas a 1% chance of being out of
date.
Well, there's a 5000% chancethat that system is fucked at

(27:15):
any given moment,

Harv Nagra (27:16):
Mm-hmm.

my French, right.
that is a perfect example of theprecision of trying to build
that castle out of granules ofsand

Harv Nagra (27:23):
Hmm.

exactly the reason that you'll never have an
accurate output.
the precision is the problem.
And so the goal there is to say,okay, well let's round the
edges, let's broaden these timebuckets.
They won't be as precise, butfor the type of insight that
we're trying to look at, whichis inherently uncertain, we have
a much more accurate lens onthat because the context is
different than, you know, whatour project manager's often
trying to do, which is like,what does next week look like

(27:46):
for Harv?
Are we gonna be overworked ornot?
You know, are we

Harv Nagra (27:49):
Yeah.

resources to do the design thing?
That's such a different questionand a different context, and
that's why the tool should be sodifferent for that.

Harv Nagra (27:56):
Really, really good point.
Let's jump forward to that kindof next threshold, which was I
think 30 plus is what you weresaying.
What kinds of things do agenciesof this size, experience, and
face, and what are some of theproblems or pitfalls that you
kind of see?

this is where we start to get into installing,
real tight framework within theorganization.
There's two big problems that Isee when you get past 30,
you're, you're starting toapproach C-suite land,

Harv Nagra (28:20):
Mm-hmm.

gonna have C-level executives.
So problem number one is thereisn't a common language within
the organization.
So I can't tell you the numberof times that we're like meeting
with teams.
We're starting in a discussionand we're talking about, let's
call it utilization rate.
We'll just pick a random metric,but this happens with all
metrics and we start talkingabout utilization there's a
moment where I have to be like,okay, pause.
Project manager defineutilization for me, and they're

(28:43):
like, oh, you know, billablehours over capacity.
I'm like, great.
What is someone's capacity?
Exactly?
What does it

Harv Nagra (28:48):
include what does it not include?
They give me an answer.
Then I ask the CFO

what's your definition of capacity?
Oh, it's different.
Fascinating.
Then I ask the CEO, what's yourdefinition of capacity?
Oh, it's also different.
Fascinating.
Okay, so we're all talking abouta metric that we think we're
speaking the same language.
We don't understand each other.

Harv Nagra (29:03):
Right.

problem number one is you often have.
Just you don't have a commonlanguage across the
organization, and that isextremely problematic because
people are interpreting thingsdifferently.
You end up with these verymaterial skews.
You have these very differentexpectations.
The CEO thinks we should be at95% utilization, whereas the CFO
thinks we should be at 60%, butthat's not because they have

(29:24):
difference of opinion.
They're just calculating itdifferently.
Those implicit problems need tobecome explicit.
And we do that by saying, let'sdecide on a framework.
What are the

Harv Nagra (29:32):
Hmm

track?
What are we going to call them,

Harv Nagra (29:35):
mm-hmm.

how are they going to be defined, and exactly

Harv Nagra (29:39):
Hmm.

are they going to be measured?
And all the data's gonna becollected.
So when we sit down to have aconversation, it's productive
because we're all speaking thesame language.
that's problem number one.
because so much of theimportance when you get to that
level is you have these layersof management.
You have to work through people.
And so you need some rails forthat communication to live on.
And when it comes

Harv Nagra (29:57):
Mm-hmm.

that is like how we define the actual,
metrics.
And the other piece of that isI.
How we understand therelationship between the metrics
and the business, right?
So if this number goes up ordown, what does that actually
mean?
Why

Harv Nagra (30:11):
Hmm.

happen?
How do

Harv Nagra (30:13):
Mm-hmm.

influence it?
And of course, the way we definethe nuances of someone's
capacity or billable hourchanges the answers to those
questions, right?
So it's so important tounderstand and agree on those
things.
So that to

Harv Nagra (30:24):
Hmm.

the first problem and the first major
thing to make sure is reallycrystal clear.

Harv Nagra (30:28):
It sounds so obvious when you're saying it, but it's
so true that we all just gothrough the motions of kind of
growing up through agencies andlearning these terms.
And everybody has a slightlydifferent definition and every
agency has a slightly differentapproach.
What kinds of things do youthink beyond the language and
the stuff that you just spokeabout, should agencies be
prioritizing at this size thatcould have the biggest impact?

so once they agree on the language and stuff,
and, the last thing I'll say onthis is I think one of the
reasons that this happens isgenerally at that scale, you
start bringing experts in thathave a lot of experience from
other firms also, they were anMD at another firm.
You hire them and then everybodywants to bring their
institutional knowledge to theorganization.
Oh, well we used to do it thisway at my firm but the CFO did
it different way at their, theirfirm.
And that's

Harv Nagra (31:11):
Yeah.

But like, we gotta get on the same page.
We can't have everybody bringingtheir own playbook, from there,
once we've agreed on theframework, then I think this is
where we have to be reallyconsiderate of system
architecture.
And there's some

Harv Nagra (31:22):
Mm-hmm.

kind of ideas that are important.
Precision versus accuracy is thefirst the executive level
visibility into the business isoften gonna be very, very
different than what middlemanagers and kind of direct
delivery managers are payingattention to and how they're
thinking.
And so the next step is saying,what are the questions we need
to answer or the decisions thatwe need to make?

Harv Nagra (31:43):
Yeah.

do we need to make them?
that should start to inform whatdoes the report look like for
the different departments at theexecutive level, at the middle
level, what's the time incrementthat we need to be able to
calculate things at?
And what's the level of sort offidelity of the data and where
is it going to come from?
And what I found is that absentthat discussion, what you end up

(32:06):
with often is a system where wefall into a whole bunch of
precision traps.
So I'll give you a few examplesof this.
somebody on the executive teamis like, oh, we should be
measuring this on a daily basis.
But they have that meeting twicea month,

Harv Nagra (32:19):
Right.

right?
So you spend 10 times more onthe operational execution, the
tooling, like it's so much morecomplex and expensive to measure
that metric on a daily basis.
It's all waste, it's allcomplete waste, and in most
cases it's not even feasiblebecause let's say like you're
having to do something based offof time tracking data.
Well, everybody on the team'snot gonna put their time in

(32:41):
every single day If you build asystem and a report that relies
on that kind of behavior, youare setting that

Harv Nagra (32:47):
Mm-hmm.

and system up for failure because that
behavior is gonna be almostimpossible to police and
actually

Harv Nagra (32:52):
Yeah.

to do on a consistent basis.
So it really starts to become aquestion of saying.
What is the actual requirementfor this dataset?
Then what would need to be truefor us to meet that requirement,
and is it operationallyfeasible?
And really trying to avoid thetrap of building systems that
are based on assumptions thatare never true.
So

Harv Nagra (33:10):
Mm-hmm.

we're going to take data straight from time
tracking and put it into areport.

Harv Nagra (33:14):
Yeah.

that does that, in my opinion, is at risk
because people make mistakes intheir time tracking data.

Harv Nagra (33:20):
Mm.

you have a middle step where somebody's
reviewing that, cleaning up thedata, making sure it's accurate,
you're

Harv Nagra (33:26):
Yeah.

end up with reports that you learn to not
trust.
Because it's constantly full ofthe 99 hour time entry that
Johnny logged while he was onvacation and left his

Harv Nagra (33:34):
Yeah,

running

Harv Nagra (33:35):
exactly.

the 17 variations of the client's name
that are being misspelled allover the place, the engineer
that accidentally logged time tostrategy, right?
Because I don't know some,something was going on, so I.
that's the other big thing isyou really have to start to be
considerate about how are wearchitecting our data systems
and start to become intentionalabout it and really start
adopting what I would call dataoperations,

Harv Nagra (33:56):
Mm-hmm.

of the, the formal language for this job
that is common in a lot of otherindustries, but we're just
learning about it in the agencyindustry, which is the team that
is responsible for managing datain the organization and creating
accurate reports for everybodyin the organization, all the
different stakeholders that

Harv Nagra (34:12):
Mm-hmm a role we had in my past agency was project
management officer.
this woman was absolutelybrilliant, but she had a monthly
process where she would dip intoeach account manager's projects
and just do a spot check with,several things that she would
check and correct if things wereoff.
So I think a really good pointthat's important.
I wanna bring it back to thatkinda top down forecasting.

(34:34):
So you were alluding to like howthose agencies that were 10 to
30, and now we were talkingabout the 30 to 50.
how does it differ?

So this kind of gets us into a little bit more
tactically when you're goingfrom like 30 to 50, the feedback
loops become really important.
from, the 10 to 30, we talkedabout the model, we talked about
pricing, we talked aboutforecasting.
All of that is really abouthaving an understanding.
And a way to structure dataaround our assumptions,

Harv Nagra (34:59):
Mm-hmm.

being really explicit about these are the
assumptions that we make aboutour people, how much they get
paid, how much time they work,when we sell a project, how much
time we're gonna invest, howmuch money we're going to make,
and having a way to projectthose assumptions that into the
future.
And basically just validatelike, are we playing to win
here?

Harv Nagra (35:14):
Mm-hmm.

decisions and assumptions that theoretically
should lead us to success?
When you're below 30, managementaccounting, and a couple of
simple, operational numbers areenough to kind of help you
identify if there's a major gapbetween your expectations and
reality.
But as you start to scale,

Harv Nagra (35:30):
That becomes

increasingly important.
And so that's, I.
Really about like threeoperational numbers.

Harv Nagra (35:35):
Mm-hmm.

utilization rate.
How busy did we

Harv Nagra (35:37):
think the

team was going to be?
How busy were they actually?

Harv Nagra (35:39):
Right.

how that's defined becomes really
important.
The second is average billablerate.
How efficient did we expect tobe at earning revenue?
How efficient were we actually?

Harv Nagra (35:46):
Mm-hmm.
Third

is average cost per hour.
How much did we expect to spendon labor?
How much do we actually spend onlabor?
And those can all be measuredoperationally without.
The need for finance.
And if you have a good grip onthose three metrics, you will
never be surprised by your P&Lagain.
So I find

Harv Nagra (36:01):
Hmm.

really important.
and those are mostly just aboutmatching up sets of data to time
tracking for utilization andaverage cost per hour.
That's people data, who are thepeople?
How much do they get paid andhow much time did we expect them
to have available and to spendon client work versus what
actually happened.
then the second for averagebillable rate is projects.
what is the project?
How much did we sell it for?

(36:21):
How much time did we expect tospend on it versus what actually
got spent?
And by who?
That's really the, the math,right?
It's, I'm radically simplifyingthat because as we just said,
the systems to get all this datatogether and measure it is
that's non-trivial, but.
are kind of the key feedbackloops there.
And then at the finance level,it's quite simple.
It's like how much money did wecollect from clients?
How much of that is actuallyours?
That would be what we callagency gross income.

(36:44):
did it cost for us to earn thatmoney?
So that's all of the money thatgot spent on delivery.
I.
Which is mostly gonna be payrolland softwares and that
ultimately gives you what wecall delivery margin, what
should be called a gross margin.
But I don't like to call itgross margin'cause I'm sick of
arguing with accountants over

Harv Nagra (36:57):
Mm-hmm.

issues.
That's really important.
And is that really above 50%?
Are we keeping more than 50% ofevery dollar that a client.
Gives us after we've

Harv Nagra (37:05):
Hmm.

the work, and then what are we spending on
overhead and are those ratioshealthy?
That's like really kind of thesimple set of feedback loops
that we want to have

Harv Nagra (37:13):
Hmm.

when we're doing all the, the first things
we talked about, understandingour model and forecasting, we
should know where we expect toland, and then we're able to
identify where we're actuallylanding.
And then that really allows usto identify what is actually the
problem, right?
If we're not reaching ourpotential, what is that related
to?
And it's pretty much alwaysrelated to.
One of two things.
Either our utilization's not ashigh as we expect it to be,

Harv Nagra (37:33):
Mm-hmm.

our average billable rate is lower than we
expected it to be, or a

Harv Nagra (37:37):
Mm-hmm.

of those two things.
And then it becomes very, veryclear what we need to focus on.
And that at that scale is soimportant because you have so
many people, you've gotta beclear and focused because it

Harv Nagra (37:47):
Yeah.

time for that initiative to make its way all.
The way down to the execution ofthe business,

Harv Nagra (37:53):
Mm-hmm.

needs to be fairly consistent for it to
actually start to take effect.

Harv Nagra (37:58):
Really good advice, Marcel.
we're coming towards the end,but quickly time tracking.
It sounds like you're anadvocate.
Tell me good I important or notso important.

I'm a huge advocate for time tracking.
I'll Go as far as to say that Ithink it's irresponsible to run
a service-based business withoutit.
To me it would be the same asrunning a restaurant and not
really having any idea what yourfood costs are.
the misconception, I think,around time tracking is that
that has to be time sheets, Iwould abstract the definition of
time tracking to be having arecord or at least some kind of
a model of where time is beinginvested.

(38:29):
Because it is your biggestvariable cost.
You can't understand grossmargins, you can't understand
client, Contribution margins.
you can't really understandanything just your raw P&L
without time trackinginformation.
And there are ways to collectand analyze that data that don't
involve time sheets.
but they're not available toeveryone.
So, for example, if you are anorganization that doesn't spread
your team across many projectsat a time, most people

Harv Nagra (38:51):
Mm-hmm.

on one, two, maybe three things at a time.
your resource plan, if it's keptup to date by your project
management team.
It is probably a pretty accuratesummation of where your time is
going, right?
It might not

Harv Nagra (39:00):
Absolutely.
Mm-hmm.

export that to a CSV.
What does it look like?
Well, it looks a lot like a timesheet project,

Harv Nagra (39:06):
person

time, date, right?
It's all the same metadata.
your qualm is with time sheetsand you just don't like the
experience of filling in timesheets, well, there's ways to
create models and records ofwhere time is going to be used
for this internal analysis andempowering yourself and your
team that

Harv Nagra (39:21):
Mm-hmm.

you to necessarily be filling in time
sheets and the technology forthat's getting.
Better all the time.
We've

Harv Nagra (39:26):
Absolutely.

tools that help us fill in

Harv Nagra (39:29):
Yeah.

sheets.
We have resource, plan informed,calendar informed, kind of time
sheets.
So there's a lot of hybrids,there's a lot

Harv Nagra (39:35):
Yeah.

right, to start to take the friction out.
But fundamental belief, Harv, isthat if you made a team 100%
accountable to the financialoutcomes, if they only got paid
based on the profit everyproject that they worked on,

Harv Nagra (39:48):
Mm.

said to them, you could choose to track time
or not.
Most of those teams after a longenough period of time, I
believe, would organicallygravitate towards tracking their
time.

Harv Nagra (39:56):
Mm-hmm.

Right.
So if you're struggling withthis culturally it has to do
with how it's being discussed,how it's being

Harv Nagra (40:02):
absolutely.

it's being enforced.
There's a deeper problem becauseinherently time tracking is only
healthy for the organization

Harv Nagra (40:09):
Mm-hmm.

done.
That's my belief.

Harv Nagra (40:11):
Absolutely, completely agree with you,
couple of examples that you gavethat I think are just so
effective.
The resource plan informed itmakes it so easy for somebody
that has their schedule plannedto just say.
Was that accurate or does itneed to be adjusted slightly and
there's no timers involved.
There's no time sheet tablesinvolved.
It's just a tweak to theschedule.

(40:32):
And I think for those of us thatkind of plan our calendars,
that's another great way if youcan, just tag it to a project
or, a task, something like that.

The thing I'll say on time tracking is this is
another place that I see peoplefall into a precision trap all
the

Harv Nagra (40:46):
Mm-hmm.

I go in and I'm like, Hey, how's your time
tracking?
They're like, oh man, ourcompliance is so bad.
You know, people

Harv Nagra (40:51):
Hmm.

in.
And then I go and look and it'slike, okay, well I.
You're asking your team to tracktime against the subtask, within
the task, within the milestone,within the deliverable, within
the phase, within the project.
It's too much detail

Harv Nagra (41:02):
Yeah.

to make 19 decisions just to log a time
entry.

Harv Nagra (41:06):
Hmm.

them like, Hey, what's the most important thing
you're trying to measure withthis data?
They're like, oh, we just wannaknow how profitable our clients
are.
And it's like, okay, great.
Right.
This comes back to the thing wewere discussing earlier, like
what's the actual questionthat's being answered?
And then I'm like, oh, wellthat's great.
out of the 18 pieces ofinformation you're asking for,
you actually only need likefour.

Harv Nagra (41:22):
Yeah.

we could radically simplify this.
And so I, I think part of theproblem too that people
experience is they make it waytoo hard for their team to track
time.

Harv Nagra (41:29):
Mm-hmm.

you really double click on it, the vast
majority of the metadata thatthey're going after isn't
necessary.
They're like tracking it just incase.

Harv Nagra (41:37):
Yeah.

to tracking on a purpose.
So that's another like exampleof trying to be too precise and
it

Harv Nagra (41:42):
Yeah.

the cost of accuracy because now you don't
have enough information toanswer the

Harv Nagra (41:45):
Hmm.

in the first place,

Harv Nagra (41:46):
we've covered a lot today.
So if an ops person is listeningto this and feel like they've
got a load to learn or a lot tofix and they want some more
advice, where can they reach outto you or your team, and, and
get some more information?

Yeah.
Well the first thing that Iwould encourage everyone to do,
in particular if that segmentabout the framework really
resonated with you of like,

Harv Nagra (42:06):
Hmm.

know, we don't agree on metrics to track or how
to calculate them.
We have a

Harv Nagra (42:10):
Yeah.

called the Agency Profitability Toolkit,
and it just basically walks youthrough our framework at no
cost.
it's very important for me tohave this out in the world at no
cost'cause I remember how hardit was to get clear

Harv Nagra (42:21):
Yeah.

to these questions when I was, in that
seat.
So you can get that atparakeeto.com/toolkit.
And if you wanna connect withme, you can find me on LinkedIn.
I'm very active there.
I can't help but Chat withpeople in the dms.
I think that's maybe where weended up, deciding we were gonna
do this podcast together.
So connect with me there, reachout to me, and if you wanna
learn more about our services atPer Keto or just get more free

(42:41):
content, like this than head toper keto.com.
I also want to give a shout outto the team at S Coro, great
people at Koro.
So thank you for having me.
This is a lot of fun.

Harv Nagra (42:49):
Absolutely.
Thank you very much.
I've just noticed,, you'rewearing a hat that says data as
well, which is amazing.
I love it.
Marcel, it's been an absolutepleasure.
Thank you so much for coming ontoday and looking forward to
speaking to you again.

Thank you Harv.
All right.
That's a wrap with Marcel.
If your brain is spinning alittle, a good opportunity for
me to remind you that you cansign up for the Handbook
newsletter to get the keytakeaways in your inbox.
The link is in the episodenotes, but here are three things
that stuck with me.
Number one, most agenciesstruggle not because they're
messy, but because they're toodetailed in the wrong places.

(43:24):
Precision isn't the goal.
Accuracy is number two.
Forecasting doesn't need to beperfect.
To be powerful.
Even a simple top-down model cangive you the clarity you need to
make smart decisions.
And number three, as you grow,alignment matters more than
ambition.
You can't scale decision makingif your team's not speaking the

(43:45):
same language when it comes tothe numbers.
Now we can't resist mentioningthat some of the stuff that
Marcel was talking about thereis doable in Scoro.
For example, top-down bookingsversus granular, bottom-up
resourcing.
Next simplified time tracking byjust confirming or adjusting
what's in the resource plan forthe day, or assigning meetings

(44:06):
in your calendar to projects ortasks.
And finally, financialvisibility at a glance so you
can see how much capacity youhave left next month, or how
you're tracking against yourtarget margins.
A huge thank you to Marcel fordropping so much wisdom.
You can check out his agencyProfitability Toolkit on
Parakeeto's website.
It's a free resource that walksyou through the frameworks we

(44:28):
talked about today and make sureyou give the Agency Profit
Podcast a listen as well.
We'll put links to both in theepisode notes.
If there's a question you wantme to put to Marcel to share in
a follow up, please email me atpodcast@score.com.
And if you've enjoyed today'sepisode, please share the
episode with someone who wouldenjoy it.
Join the conversation when yousee Marcel or I posting about it

(44:51):
on LinkedIn.
if you've not done so yet, thenplease rate the podcast on Apple
or Spotify.
That's it for me for this week.
Thanks so much.

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