Part 2 of our episode with Kluster's Rory Brown gets practical: Rory and Jonny Adams walk through a hypothetical joint client and show how Kluster’s quantitative insight pairs with SBR’s enablement to close performance gaps. Rory breaks down leading vs lagging indicators, why “sheep dip” KPIs fail, and how to reverse-engineer every rep’s route to quota. The episode closes with Rory’s grounded view on AI: fix the data context first, then unlock trusted natural-language answers and predictive actions.

Hello and welcome to the Growth Workshop Podcast.

We'll cover all aspects of leadership, sales, account development, and customer

success alongside other critical elements required to build an effective

growth engine for your business.

Welcome to part two of two.

Rory, you're, like, like the best trailer in the world you seem to

tease in with, with, a couple of themes before we get onto the, partnership

and, we're gonna, we're gonna bring together a bit of a use case together.

So we are gonna hypothesize on a mutual client.

We're gonna talk about how Kluster can extract the great insights, and

then we're gonna talk about how SBR supports basically dealing with the

so what, of that insight in a moment.

And, I think, one of the things that I'm super curious about just,

summarizing some of the conversations.

So I love the story about how you and your co-founder got together.

Whenever you say forecasting, I'm thinking about weather forecasting

or something along those lines.

So that's really interesting.

He's the scientist, right?

Yes, he is.

Yeah.

And you are the?

The other guy.

Fantastic, so we've been through a number of things, and how

Kluster can support organizations.

I think one of the anecdotes that I recall is a fantastic chap called

Charlie, who's the Chief Revenue Officer of an organization that we've

worked with for a number of years.

And I remember him sharing with me a couple of years ago was like, I'm

now going to see the board every two weeks, every month really.

And I have to stand up in front of the investment directors, and

everyone else in the PE house and just didn't have the insights that

were being surfaced quick enough.

The 48 hours before the board meeting.

The things that was going through his head, the drain that it gave him that the

impact it had on not just his own feeling, but, the way that he felt probably at

home and, in the job for those 48 hours.

And in doing that exercise wasn't fun.

Yeah.

It wasn't enjoyable.

But if you're able to arm yourself with great insights, you can probably

add a bit of the art onto the other side, which is about using some of

that human capital, being able to build a great narrative that is actually

substantiated by empirical data.

It's absolutely right and, it's, it's incredibly stressful.

Me and Dan

have board meetings ourselves and I always joke, but if there's anyone

in the business, who, doesn't get let off lightly with the forecast, I'm

probably up there given what we do.

Yeah.

It's incredibly stressful time and the biggest area is they will

know that they're gonna go in there, they'll get asked questions, and the

answer will have to be, I'll have to get back to you on that because

I, they don't have the depth of information insight at their fingertips.

And sometimes if they're a bigger organization, it could be days or

weeks before that information's passed back up in process.

And is the question that board member asked even relevant anymore?

Or do they care anymore?

Maybe they don't.

Maybe they wanted an answer straight away.

So they're living under that and then they're also living under

the fact that, if you're a CRO.

You're right front and center of the board movement.

'Cause it's where we're gonna land and tell me why I believe

it and give me evidence that is

so, obvious.

Unarguable, that we can then move on from that conversation.

'Cause it can be quick.

If you can do that, you can finish that conversation quickly in the boardroom.

And if you cannot do that, the conversation lingers there for

an uncomfortable length of time and the CROs under more pressure.

And all of a sudden the questions start going a layer deeper.

Oh, let's, what about pipeline then and what's that and what's moving

here and what's working here?

And all of a sudden you're in, an area of, gosh, I'm get, I'm now getting

peppered with questions that layer deeper and I certainly can't ask, all these.

So the, strength with which you can start can then move everyone on to

the stuff that matters of right, what are the next order of business?

What are strategies, what are we investing in?

What are we doing next?

What's our product roadmap?

All the good stuff.

So we can look forward and we can implement some actions

and initiatives to support that.

Let's move on to a use case.

Let's bring this to life, right?

How do we combine Kluster and SBR, if we think about that, again, it's the

data insights, the technology that drives some of that empirical evidence.

And then you've got the change management, the sales enablement, that,

that real sort of, how do we change and really instill those behaviors.

Organization, 200 million ARR.

They've got a flight path of the next three years that they want to

essentially get to 350 million ARR.

Okay.

That's the use case.

Fine.

We know where we're going.

There's a number of other facets.

They've got go-to market opportunities in new regions, but they've got over

50 sales reps across the organization.

Just to let you know.

So in terms of that set up, what do we do in that sort of diagnostic

consult phase when we work together?

How does Kluster start?

What does it look for?

What does it interrogate from a data perspective?

Could you describe that?

Yeah, I think there's a wonderful journey if you look at the SBR,

Kluster, partnership from consultation to embedding and driving behavior and process

and rigor and cadence and operationalizing that growth number that we're looking for.

So I'll come to that second but in the consult phase, obviously SBR going

in there and doing the qualitative research and we're, also mining data.

So for example, let's say we interview the salespeople and we start to get a

sense that, there's deal control problems.

That's the common thing.

If there's sales skills that need to be sharpened, there's

inevitably deal control challenges.

What Kluster will quantify is, what does that mean for us in terms of

our conversions, in terms of how good we are at forecasting, in terms of

actually how we build that problem and the percentage of that problem into our

forecast calls as empirical evidence so we can pass that up the chain.

Another example might be, we interview the salespeople and we start to get

a sense that are there some deals that are working here that don't feel

quite right in the evidence, Kluster's going to, show us that here's profiles

of deals that these sellers are good at, and here's profiles of deals

that these sellers are not good at.

What SBR can understand is, why is that the case?

It could be as simple as, send those profiles to that seller and

send those profiles to that seller and boost your win makes by 10%.

There you go.

There's your growth number.

Sometimes it can be that simple.

It could be.

Okay our A players are good at these higher end deals and actually they're,

we're giving them all our pipeline 'cause they're our best, but they're churning

our lower end deals 'cause they're not putting effort into it and actually these

people here who are yearning for, more pipeline to practice their craft and

work with SBR to, to develop their sales skills should be put in that pocket.

So it could be a piece of moving things around, which can be

an uncomfortable conversation.

But if you're chasing these numbers, you have to be ready to

have uncomfortable conversation.

I think back to uncomfortable truths when you come to SBR and Kluster,

it's exposing the uncomfortable truths from a qualitative perspective,

from a quantitative perspective.

And it's being able to implement that in a way that's data-driven and will

prove to move the needle, but also in a training and coaching and development

way, which will also move the needle.

And how does Kluster, thank you for that, and how does

Kluster analyze target attainment gaps?

So let's just say in this particular organization, we know that the field

force of the sales reps are currently at a certain percentage of attainment.

Evidently we want to move the map as Chief Revenue Officers.

So how do we use Kluster to identify, and analyze some of the data to go well?

That's an interesting theme or thread that's happening with target attainment.

Yeah, it's a really good point.

the first thing is, we reverse everything.

Reverse engineer, sorry, everything from target back to the most, the greatest

leading indicators that we have.

Leading indicators are a really important point because we've gotta understand

what they are and it's as far up the funnel as you can get that's predictive.

Give us an example, of the leading indicator.

Leading indicator is... lagging indicator is obviously revenue.

Okay.

For many people, a lagging indicator is pipeline.

'Cause it's there, it's already happened.

A leading indicator is the pace that we're creating.

A leading indicator is are we creating the right pipeline?

A leading indicator is, are we doing the activities across our demand gen engine

to create pipeline for marketing...

Like sales activities, like meetings and stuff like that.

Yeah.

Campaigns, lead volumes for marketing, for me, pipeline

generation is a lagging indicator.

It's the last thing.

Yeah.

Obviously we want revenue contribution for them, but that's baton

pass to the sales team at that point.

So it's about reverse engineering every single person's journey to quota

attainment because unless you can sit there as an exec or as a CRO and say,

my sales team are mechanically the same.

Behaviorally are the same.

They sell in the same way.

It's a playbook, it's repeatable.

They probably all do it slightly differently and their route

to goal will be different.

They'll find different pockets of success and different people they're

more comfortable speaking to and so for example, these profile of reps

might operate on a 6.4 coverage.

Your A players might operate on a point nine coverage.

'Cause they're so good.

They can start the quarter with slightly less pipeline, build a little

bit and then just hit their number.

And pipeline coverage.

Talking about pipeline coverage, coverage, exactly.

And it's, all individual.

If you shy away from that or if you try and blanket, or I think

you guys use the term sheep dip.

If you sheep dip everyone in the same, KPIs, first of all, they won't trust

them 'cause they'll say I'm different.

So they don't really count that.

That happens immediately.

But then you're not showing them their path to goal.

And I think uncovering that is the first truth of across all your reps, here's

their coverage, here's their activities they need to do to hit their number.

Here's their profile of deals they need to be running to hit their number.

Who's actually on track for that?

Where are the biggest deltas?

Start there.

And this is the beautiful thing I think about how Kluster and SBR

are working together because we hear and have heard that amazing insight.

Typically it's gut feel, right?

Yeah.

People can see target attainment and the miss, and they're going, oh,

Jonny's just not up to the races, and Rory, he's just a superstar.

What a rock star.

And, then we typically then ask the same question, what makes up your A player and

D player, what are the characteristics that helps them achieve that attainment?

And what, we're saying is Kluster's able to give us those

data points, those insights around the leading and lag indicators.

Now, when we think about this consult phase and diagnostic, what we're

doing is we're pairing that insight, that data from Kluster, and then the

Go To Market Capability Accelerator from SBR, which essentially enables

us from an SBR perspective to take that population of 50 leaders and

then start to, survey their insights.

And what we mean by that is we're able to triangulate their psychometric insights,

so their traits and motivations connected with a proven capability framework.

And then using the Kluster data, whether that's performance leading and lagging

indicators or target attainment, we're able to triangulate those data points.

Not just turn around and say, oh, Jonny needs to do more meetings to hit coverage,

which is what Kluster will give us what SBRs insight we'll be able to do is

going well what are the capabilities that directly align to target attainment?

And then to your point, not doing sheep dip, Jonny then can have a pathway

and Rory can then have a pathway.

And then throughout the period of seeing these capabilities level up.

We can leverage the Kluster data to start analyzing those leading

lagging indicators, and we can use the psychometric and the capability

insights to go if we're able to move Jonny's capability within curiosity

and questioning to the right hand side by one level, we should see an

increase of attainment by X. Yeah.

And that combination is just gold.

Yeah.

Fantastic.

And, I was just thinking of a, an even more specific example

as you run through that.

So let's say your psychometric reveals that, someone has a, as a difficulty with

uncomfortable conversations with combat.

Now as a, as we're probably thinking as salespeople should love combat, but

actually a lot of people get into sales.

A lot of people don't like combat and they shy away from it.

I was certainly like that in my younger years.

And, we can see in the data that due to this, they struggle with getting

deals to a certain point 'cause they're not having difficult conversations.

So we see more pipeline slippage.

So we see the fact that their coverage needs to change.

So we see the fact they need to book more meetings.

We reverse engineer the whole thing from, behavioral profile to leading indicator.

And that's where I think things get really, powerful.

Oh, what a great insight.

And I guess then what we've got.

Two things that come together, Rory, and this has just come to mind, so it

may land or may not, but fundamentally, if we think about forecasting, we're

basically forecasting number through Kluster, and then what we're doing

is we're forecasting the capability that needs to change, right?

Yeah.

We cross those together then we've basically got a winning formula.

So when we're in the board room, we are sharing these insights.

The narrative is I can now segment my 50 sales base people, and then

I'm able to segment them to who do we need to retain, who do we need to

develop, who do we need to support, and then who do we need to hire?

Yeah.

To help us hit that number.

Yeah.

Which could well be another uncomfortable truth.

These reps aren't right that is a thing.

We always wanna save people, but sometimes that's a conversation.

I think when we come back to closing the loop, we get to the embed phase

when we are operationalizing this and we want people to turn up to

an operational cadence of meetings.

We want them to discuss specific things for that person or in that meeting that

move the needle after all this analysis.

Where do I do that?

Where do I get the information?

How do I see if I'm pacing, how do I see if I'm on track?

That's where, again, the SBR program of embedding the platform

of Kluster producing the data and the insight come together.

Then you're well on your way if you've done that to closing that

performance gap to closing that number that you're chasing as a business.

Amazing.

the last question from me, and it'll be remiss of me not to bring this up: AI.

Ah, do you know, I've got a bit of a, bit of a problem.

Not problem.

I've got many problems, but in the evening I -the true serums

on me.

I think the, the thing I keep doing is just looking at so many news outlets

at the moment, and I, in, in some evenings, I'm like, whoa, AI is amazing.

And then in some of the days I'm like, wow, what's gonna be going on?

But when we think about Kluster and AI.

Think about the people that you're supporting, whether it's the CRO,

the CFO, any type of persona.

Like how are you gonna be leveraging AI a Kluster moving forward?

How are you gonna help those people?

Yeah, we are beyond excited about our AI roadmap,

what we've already developed, what we're going to be developing.

I think we need to start with some fundamentals and I'm gonna

start with a very old phrase.

Garbage and garbage out.

It's resurfaced.

It's the new black again.

And the reason people are talking about it is because they're sticking LLMs on

top of their databases or on top of their CRMs, and they're getting out a bit of

garbage and they're not trusting it.

And as soon as you start to deploy something as big as change the

entire way you interact with your information, oh, and I don't trust it.

You've got a huge problem.

You've moved too soon- you've got it wrong.

The importance is, and where Kluster's data engine comes in, is

it's producing this contextual layer.

Of how your business behaves, how that data change and moves over time.

All the things we talked about with the sales rep, that's slipping that, pushing

that, winning these deals, winning those deals, we've gotta profile that.

We've gotta amplify that data set and term ERP data and CRM data and any

other data relevant we can get our hands on into a profile of who the

business is, how it performs, where we're strong, what metrics we trust.

One global taxonomy of those metrics.

We've gotta do that first and only then we can think about something fancy.

Once we've done that, we then enter this new world, which is incredibly

interesting because for years, years and years we've looked at charts, bar

charts, pie charts, line graphs, and maybe we'll never get away from those.

Maybe we'll always just be drawn back to, and the closed loop is the end of

the story and the journey I wrote to talk about is the line graph, but there's

an interesting thing that's happening.

When we give a dashboard to anyone in business, literally anyone,

there's just a varying skillset of interpreting that dashboard.

Some sales leaders love it.

They're analytical.

They can infere, they can create narrative, they can drive action off

the back of it if they're really good.

A lot of sales people or commercial people aren't analytical people.

That's other people and so how do you like close that loop and really get

everyone using a dashboard, a visual to drive action, to change, manage

and that's always been difficult.

It's continually difficult the opportunity with AI, when we have

our contextual behavioral data that we trust, it's one taxonomy is the

ability to shortcut that and say, I want to know what my biggest risks are.

I want to know what my coverage should be.

I want to know the profiles of deals that I win.

I want to know which region is behind that's gonna let me down in the boardroom.

I wanna ask that question in natural language.

I wanna get an answer back that I trust.

I want to be able to turn that into specific, you know what we talked

earlier about the specificity.

The acuity of the things that we're producing is getting heightened and

heightened until it's literally just, it's obvious we need to do that.

Have we got the right people?

So we get to that very, quickly.

We can push those memos out to different people in our organization and we can hold

them to account because we can see in the data, we don't have to ask them anymore.

We can see in the data if they're actioning these action plans that

are now really specific and easy to follow and then we might say,

could you take that in a line graph for me so I can show the board?

Yeah, that's really the, journey that we're gonna be able to take people on.

Super exciting.

There'll be businesses that are on that journey.

There are businesses that have tried and they've got to that

first layer where it's failed.

I don't know if you've tried like Copilot and HubSpot, like there's

all sorts of issues with that.

So we've really gotta get the fundamentals right, but if we do, we can

take people on a journey of accessing information that matters, actioning

the information that matters, like literally in, in text that we can read.

If we wanna visualize it, let visualize that for me now.

And that's the AI insight and intelligence back to visualization

layer that we're developing.

And in, in the middle of that journey, we're creating a predictive

layer, which will say to any user, a rep, a leader, a CEO, this is

what you need to do this week.

Here's the greatest gaps in your business tactically, strategically.

Regionally product wise.

and here's the predicted commercial value of acting on that

decision that you need to make.

When we get to a world where that's the case, you're gonna have a bunch

of executors executing information, not spending time trying to infer

information from dashboards and driving the business forward.

Wow.

Do you know what that is just so interesting.

I think you've absolutely nailed that for the analytics, for the revenue

officers, for those individuals that are thinking about how can they

use AI moving forward to expedite some of that sort of challenging

obstacles to get really rich insights.

And as you say, it might be able to make you a line graph afterwards, right?

So you could present it.

I think, as a partner, Rory, as a founder, as a really decent guy, I've

really enjoyed today's conversation.

Me too.

We've gone from one side of the, the conversation to

talk about Kluster, the challenges that people face, the solutions that

we can provide, the partnership.

I'm gonna bookmark this episode and say, in 12 months time, let's come back

on, let's have a chat and let's talk about not a hypothetical situation.

Let's talk about a client that we've worked with throughout this year and

think about how have we supported them through the science, the art and think

about how have we helped them, achieve their growth ambitions moving forward.

So thank you so much for joining The Growth Workshop Podcast.

Thank you for having me.

I have no doubt it, is happening and will happen.

So I look forward to reconvening.

I'll accept the invite for 12 months.

It's been very enjoyable.

Thank you so much for listening.

For more insights.

Make sure you subscribe, and if you enjoy the journey, don't

forget to leave us a review.

Your feedback fuels our growth.

Until next time, keep up that forward thinking mindset.

Goodbye.