00:00:00 Dr Genevieve Hayes
Hello and welcome to value driven data science brought to you by Genevieve Hayes Consulting. I'm doctor Genevieve Hayes. And today I'm joined by Lauren Pearl to discuss her work in helping startup founders translate their business ideas into financial models. Lauren is a CEO turned CFO.
00:00:21 Dr Genevieve Hayes
Who now trains CEOs to work better with financial data. She holds an MBA from NYU's Stern School of Business and is the resident startup finance expert at NYU's Berkeley Centre for Entrepreneurship. Lauren, welcome to the show.
00:00:39 Lauren Pearl
Thanks so much for having me, Genevieve. It's great to.
00:00:41 Lauren Pearl
Be with you.
00:00:42 Dr Genevieve Hayes
Startups and data science go hand in hand, but usually when people think about how data science can help startups, it's with regard to product development and enhancement.
00:00:54 Dr Genevieve Hayes
And this is what we've seen in previous episodes of this podcast, where I've interviewed startup founders.
00:01:01 Dr Genevieve Hayes
However, it doesn't matter how great a startup's product is if the financials are a mess, the business is going to struggle, and this is where data science can also help in the form of financial modelling and analysis. That's something that you help startups with, Lauren through your own business, Lauren Pearl Consulting.
00:01:22 Dr Genevieve Hayes
Looking at your LinkedIn profile in preparation for this episode, one thing I found interesting was your reason for why you started your business stemming from your experiences with your own family business to begin with, would you be able to share your origin story with Alex?
00:01:43 Lauren Pearl
Definitely. Yeah. So I did. I grew up in a family business. My father was running a 200 year old shoe company headquartered in Freeport, ME in the.
00:01:54 Lauren Pearl
US and I grew up in.
00:01:56 Lauren Pearl
The family.
00:01:57 Lauren Pearl
Working in the business, I rebelled for a couple of years after college and became a software engineer.
00:02:03 Lauren Pearl
And was a professional musician for a.
00:02:05 Lauren Pearl
Couple of years, but then pretty quickly went straight back to working at the family company and eventually was actually slotted to take it over from my father when he retired. However, I didn't have a formal business education. No one in my family did either, and things seemed to be going wrong with the business. Things were getting kind of wonky, but no one really had the skills to know.
00:02:27 Lauren Pearl
What exactly was happening and and how to fix it so I went?
00:02:30 Lauren Pearl
To Business School, that's.
00:02:31 Lauren Pearl
When I left for my MBA at NYU, and I remember sitting in my first accounting class learning how to read a financial.
00:02:40 Lauren Pearl
Statement and the sort of very dramatic moment discovering holy.
00:02:44
Moly, this business is.
00:02:46 Lauren Pearl
Really in trouble? Just like finally seeing it in the data in the numbers. The hole that we had dug ourselves into as a company, and unfortunately that business actually went bankrupt before I graduated school. And so since then, I've done consulting. I've worked for various startups and now.
00:03:07 Lauren Pearl
My consulting practise, I've started my own is really focused on preventing this issue for other Coms.
00:03:14 Lauren Pearl
And I've noticed that it's common, right? Entrepreneurs who are excellent at managing, building cool tools, selling a lot of those folks don't come from a financial background, don't necessarily have the tools to understand what's going on from a numbers perspective. And it gets them into really big trouble.
00:03:34 Lauren Pearl
And so my mission is essentially to help them avoid the same fate that my family company suffered.
00:03:40 Dr Genevieve Hayes
Firstly, I'd just like to say.
00:03:41 Dr Genevieve Hayes
They that you must be the only person in history, whoever rebelled by doing software engineering.
00:03:47 Lauren Pearl
Well, I graduated in 2009 and I went to Boston University, which is right next door to MIT. My friends at MIT in 2009, the financial crisis weren't able to get jobs. The only ones that could went into software engineering. So I quickly followed through this to the degree I could.
00:04:07 Lauren Pearl
I wasn't a very good software engineer though.
00:04:10 Lauren Pearl
I did really basic things. I did a lot of front end stuff and string web parts together when.
00:04:15 Lauren Pearl
Web parts were a thing.
00:04:16 Dr Genevieve Hayes
The other thing I want to pick up on is your comment that a lot of.
00:04:19 Dr Genevieve Hayes
Startup founders don't understand financials. My background is in insurance. When I was working in the insurance sector because most people in that sector come from a financial background, they do tend to understand financials. And then after I left the insurance sector, I was working in a non insurance organisation.
00:04:41 Dr Genevieve Hayes
With a lot of really smart people who were software engineers and lots of data type people.
00:04:49 Dr Genevieve Hayes
That what was really fascinating was they did not understand even the basics of personal finance.
00:04:56 Lauren Pearl
I'm often fascinated by that same thing you talk about how really smart people who are running businesses don't have an understanding of how financials connect to what's going on. You are. It is completely possible to run a business and completely not understand finance.
00:05:16 Lauren Pearl
People do it all the time.
00:05:18 Lauren Pearl
And I think to give those folks.
00:05:20 Lauren Pearl
A lot of credit.
00:05:21 Lauren Pearl
A well deserved credit.
00:05:22 Lauren Pearl
That finance is kind.
00:05:24 Lauren Pearl
Of made up right when you think about it.
00:05:26 Lauren Pearl
Finance is sort of a system that we created of accounting for the ways in which we've decided to trade goods. It's a system that's been invented, there's no inherent understanding that you're born with of how finance work. It's like you're not.
00:05:42 Lauren Pearl
Or knowing how the Dewey Decimal System works, again because it's created. So I like to think about it as it's sort of just a language, right? It's a system that's been created to make sense of how to run businesses and.
00:05:56 Lauren Pearl
You really do need to be kind of given the terminology you know, shown the different concepts and systems in order to get it. Otherwise, why would you know?
00:06:08 Lauren Pearl
There's no reason.
00:06:10 Dr Genevieve Hayes
What you've described as finance is essentially like a programming language, and you don't inherently know Python or Java or whatever your weapon of choice is. You learn, and there are all these keywords that you learn in order.
00:06:26 Dr Genevieve Hayes
To write whatever your code is and it gets you whatever outcome you want, and that's basically how you've just described finance. It's just another programming language.
00:06:37 Lauren Pearl
Yeah, I think.
00:06:37 Lauren Pearl
That's a brilliant comparison, because I think it's super true. And actually it points to something that happens often that I've noticed working with clients, which is.
00:06:49 Lauren Pearl
Often when I work with CEOs who have an engineering background, they.
00:06:54 Lauren Pearl
Get really frustrated.
00:06:55 Lauren Pearl
With finance, because a lot of finance feels antiquated and like it doesn't work.
00:07:02 Dr Genevieve Hayes
The way it should.
00:07:04 Lauren Pearl
There's a lot of financial norms, especially in like accounting.
00:07:08 Lauren Pearl
You know, compliant accounting that it doesn't feel like a modern programming language. It feels like an antiquated programming language that could just be so much more efficient if we just changed it. I think it's completely the perfect comparison that it is its own kind of programming language, and sometimes the frustration can even just.
00:07:27 Lauren Pearl
The ohh. I really don't like this language you know.
00:07:30 Lauren Pearl
I don't like these.
00:07:31 Lauren Pearl
Norms, but it's just what everybody uses, and it would be very hard to completely reinvent the entire thing, even if there might be certain more efficient ways that we could do things sometimes.
00:07:42 Dr Genevieve Hayes
Although I'm sure the big accounting consultancies would love it because they'd make massive profits in the transition.
00:07:49 Lauren Pearl
Right, all the updates.
00:07:52 Lauren Pearl
That's so true.
00:07:55 Dr Genevieve Hayes
So I think this is a good segue into today's topic, which is about the benefits of financial modelling for startups.
00:08:02 Dr Genevieve Hayes
When you talk about financial modelling, what do you actually mean? What financials are you modelling?
00:08:09 Lauren Pearl
Sure. So basically when we when we say financial model in the context of startups, we typically mean creating a picture most times in Excel.
00:08:22 Lauren Pearl
Of how money?
00:08:23 Lauren Pearl
Moves in and out of the business. It's like the math behind how the business.
00:08:28 Lauren Pearl
Or a model is a hypothetical forecast where basically you are putting in certain assumptions or inputs around what you think will happen with the business.
00:08:41 Lauren Pearl
And it spits out the hypothetical consequences if those inputs are true in the context of when I teach founders how to build a model, it's often for a business that either doesn't exist yet, or it doesn't really have a lot of good data on what those inputs actually will be, right? So if you're starting a.
00:09:01 Lauren Pearl
A new company that sells you know baseball.
00:09:05 Lauren Pearl
Apps you have to create a bunch of assumptions around, like how many hats you think you're going to sell the first year and how much those hats will cost to make and so on and.
00:09:14 Lauren Pearl
So forth, but showing.
00:09:16 Lauren Pearl
All of that in math and taking some good guesses on what those numbers will be. Let's you then see the consequences of. OK, this is how much money this tax.
00:09:25 Lauren Pearl
Company is probably going to make in its first year and this is the amount of.
00:09:29 Lauren Pearl
Funding I need to.
00:09:30 Lauren Pearl
Buy enough inventory to get to these numbers and.
00:09:33 Lauren Pearl
Things like that.
00:09:34 Lauren Pearl
So for my financial modelling course, again, you're building this model to show you those things, but there's also some context around what a financial model means for a new startup. That's not a full blown business yet.
00:09:48 Lauren Pearl
It's also part of sort of the process that investors expect you to undergo when you're starting a business in order to justify getting investment. So it's this picture, you're not just creating for yourself or you definitely are creating yourself. You get lots of benefits from doing so. You're also creating it for other parties so they can kind of get on board with you.
00:10:10 Lauren Pearl
And they can.
00:10:11 Lauren Pearl
Yeah, have the assumptions. Do the assumptions that this entrepreneur is making match the reality that as an investor, I think is the case or that I might have information about and if I agree with these inputs, am I happy with this potential outcome of this startup? And do I want to give it my money?
00:10:32 Lauren Pearl
Because it seems like it's gonna be.
00:10:33 Lauren Pearl
A a profitable good investment.
00:10:36 Lauren Pearl
So that's sort of the greater.
00:10:37 Lauren Pearl
Context is, it's a tool that's very helpful, but it's also generally part of a process for accessing investment as an early start up that most founders have to go through if they want to take someone else's money to build their startup.
00:10:50 Dr Genevieve Hayes
One thing that struck me while you were talking a few months ago, I interviewed a man called Todd Tressider. Have you ever heard of him?
00:10:58 Lauren Pearl
I've not.
00:10:59 Dr Genevieve Hayes
He wrote a book called How Much Money Do I Need to retire?
00:11:04 Dr Genevieve Hayes
And in that book he talks about how you.
00:11:08 Dr Genevieve Hayes
And model how much money you need to retire, and one of the things I found really interesting about that book is that he's talking about how statistics fall apart when you've got a sample size of one being yourself and how difficult it is to make a lot of the assumptions because.
00:11:28 Dr Genevieve Hayes
An insurance company that's modelling life insurance.
00:11:31 Dr Genevieve Hayes
You can make assumptions based on the average for the population, but when you're dealing with an individual, it's very difficult to make assumptions about yourself because if you get them wrong, you've either saved up massively more money than you need to, and then you die and it goes off to whoever inherits from you.
00:11:51 Dr Genevieve Hayes
Or the worst case scenario, you don't save up enough and you don't die, and then you end up being poor for the last years of your life. I would imagine that there would be a a lot of overlaps between the challenges that are faced in.
00:12:08 Dr Genevieve Hayes
Modelling retirement savings for an individual and the challenges faced with modelling financials for a startup that doesn't have data. Is that right?
00:12:20 Lauren Pearl
For certain businesses, you might run into issues of sort of the not enough data or I think so. In the example that you're talking about, you're also talking about a scenario where you've got to make a whole bunch of decisions about how you're going to spend money and earn money, and then your ability to spend money or earn money goes away and you have to live on those savings. And so it's a pretty big.
00:12:42 Lauren Pearl
Yeah, to make because you can't kind of, you can't learn.
00:12:47 Lauren Pearl
As you can get better data as you go along about how long you're going to live, and potentially maybe you can like that's not quite true because you know, maybe you learn that you're sick or maybe you, you know, at the age at your own age of 60, your grandparents are still alive and you can be like.
00:13:03 Lauren Pearl
Oh, I'm probably gonna live longer than other.
00:13:05 Lauren Pearl
People, I think this points out.
00:13:07 Lauren Pearl
A key thing that is helpful for building models of any kind, that especially true in startups, which is.
00:13:14 Lauren Pearl
You want to build.
00:13:15 Lauren Pearl
A model to accept.
00:13:17 Lauren Pearl
Changing into.
00:13:19 Lauren Pearl
So if you build a model where you're just going to build the model and OK, I've put in some guesses on my inputs and I have this projection of my.
00:13:29 Lauren Pearl
Output and then you get some funding in your startup and then you never look at it again. That's not a good model. The best models.
00:13:38 Lauren Pearl
Allow you to learn as you execute and then update as you go along so that the output gets increasingly accurate, right? So I'm starting.
00:13:48 Lauren Pearl
This hats business.
00:13:50 Lauren Pearl
I get some money to buy my initial inventory and then I'm actually running the business right. I'm selling hats and I might learn that actually I went to this material supplier to get some of my cloth and it costs two times as much as I thought it was going to. I can put that increased cost in my model.
00:14:10 Lauren Pearl
And then I can make different decisions. Ohh wow. Materials were more expensive than I thought. This means I might not be profitable in the first year. Maybe I need to change something else about the business to.
00:14:22 Lauren Pearl
You course correct for that, like cut down my cost of some other material or change the way that I'm creating these hats or sell them for more money or something like that.
00:14:32 Lauren Pearl
So I think.
00:14:33 Lauren Pearl
The line that's true about both of the scenarios of planning for when you get older and for planning for a startup.
00:14:42 Lauren Pearl
Is more data is better?
00:14:45 Lauren Pearl
But learning is a way to gather that data. Having a model that allows you to continue to put data in overtime as you learn more prevent some of that from happening.
00:14:58 Dr Genevieve Hayes
When you're doing.
00:14:58 Dr Genevieve Hayes
The first iteration of this model I can understand how you can get some of the assumptions pretty accurate right from the beginning, like you can just find that.
00:15:08 Dr Genevieve Hayes
Cereal wholesaler to find out how much it costs for baseball hat fabric. But how do you predict something like demand for your baseball caps when you haven't even made the first baseball cap?
00:15:22 Lauren Pearl
Yeah. So you look for benchmarks is but kind of answer #1 we're lucky to live in a world where lots of people start startups and lots of them either publish, you know, some work in public and we'll release some of this data you'll find, like, blogs where people talk about their experiences. There's also so. So one is like.
00:15:41 Lauren Pearl
Benchmarks of like you or as like you as you can find.
00:15:45 Lauren Pearl
Another way is extrapolation from data that's maybe not similar, but where you can compare yourself to it and ramp the input up or down based on your differences. So a good example here might be to say I know that.
00:16:02 Lauren Pearl
I'm trying to think of our baseball example. I know that this other startup that sells beanies has had sales that grew like this with this percentage. So I think that mine will will grow in that way too. Another way would be to.
00:16:21 Lauren Pearl
Base it on something in the market, like a phenomenon, so you could for example, project that demand might increase based on the increasing preference for some item like your demand is is equivalent to the market.
00:16:36 Lauren Pearl
Growth, I think the best way.
00:16:38 Lauren Pearl
Though if we're doing something like demand and it's really.
00:16:41 Lauren Pearl
Stepping back, I think it's about asking what's the most reasonable way to make an assumption? How can.
00:16:47 Lauren Pearl
I get as.
00:16:48 Lauren Pearl
Close as I can to saying this is not necessarily correct, but it is a reasonable way to make a good guess.
00:16:58 Lauren Pearl
Something like market demand is really hard to.
00:17:02 Lauren Pearl
Know how it's going to play out, but there's certain things that are very accessible about how demand works in a market, right? So or rather grow interest in.
00:17:12 Lauren Pearl
Your product, right?
00:17:13 Lauren Pearl
Customer demand is most times generated through marketing. Customers don't just arrive on your doorstep.
00:17:19 Lauren Pearl
You entice them to join.
00:17:20 Lauren Pearl
Right. So there's activities inherent in that. There's advertising on Facebook, there's doing SEO, there's doing a launch event and having people come to it. And for any one of those activities that you do, there are a certain number of people that will see it, certain number eyeballs and then of those eyeballs, a certain number of people who will.
00:17:41 Lauren Pearl
Show some interest.
00:17:42 Lauren Pearl
And then of that group, there's a certain number of people who will eventually buy from you, right? So in your model, often the most accurate way to get a picture of this is to model that story.
00:17:53 Lauren Pearl
Because it connects the work you will do in marketing to the eventual outcome of that market, right. If I spend $1000 on Facebook ads, I know how much a single click in Facebook costs I can make a guess about how many clicks will convert because there's some good benchmark data around that for similar products and so.
00:18:13 Lauren Pearl
$1000 means 10 new customers, for example, and making that connection, it's also great because then as you move along as a startup, you can spend that $1000 on Facebook. See how many customers come and of course correct that demand generation very easily.
00:18:30 Lauren Pearl
Does that make sense?
00:18:31 Dr Genevieve Hayes
Yeah, that's really cool. One thing I was wondering while you were speaking was you gave the example of how the baseball cap manufacturer might look at the beanie manufacturer and drawer. An analogy from it. But I was wondering, how does the baseball cap manufacturer get the financials of the beanie manufacturer because the beanie.
00:18:51 Dr Genevieve Hayes
Manufacturer is probably not a publicly listed company, so their financials might not be publicly available, but.
00:18:59 Dr Genevieve Hayes
Things like Facebook conversions. I would imagine that data is a lot easier to obtain.
00:19:05 Lauren Pearl
Yeah, when you're working on a model on a hypothetical sort of picture of what you think the business is going to be, you're basically just working with the best data that you can get and.
00:19:16 Lauren Pearl
In fact, it's really.
00:19:17 Lauren Pearl
Good practise for running a a full blown startup because that will always be the case. You're always just working with the data that you can get.
00:19:25 Lauren Pearl
Right. You're gonna have an amazing conversation this one day, cuz.
00:19:28 Lauren Pearl
You meet a.
00:19:30 Lauren Pearl
CEO of some Beanie Company that you look up to and they're going to have a, you know, 15 minute call with.
00:19:36 Lauren Pearl
You and they might rattle off some data. That's really helpful.
00:19:39 Lauren Pearl
OK, you go and you take.
00:19:40 Lauren Pearl
That and you can put it in your model.
00:19:42 Lauren Pearl
There's this interesting transition when I think as data people, we go from working in a bigger company to working in a startup and a bigger company. I think the idea is so often that we have a lot of data to work with, almost too much data and we're kind of digging in to find certain glean certain insights.
00:20:02 Lauren Pearl
At a start up your data starved and you're essentially trying to leverage whatever data you have to make it more likely the case that you're making a good decision than a bad one. So you start that at that exercise, really. With that model, knowing that you're simply trying to get closer and closer and closer to reasonable.
00:20:22 Lauren Pearl
And then as you go along, you will have and in finance.
00:20:26 Lauren Pearl
We call these actuals actuals.
00:20:28 Lauren Pearl
Are the financial data that's like logged in your accounting software because it's already?
00:20:34 Lauren Pearl
You'll start to have actuals.
00:20:36 Lauren Pearl
And it will very.
00:20:37 Lauren Pearl
Quickly start to course correct any places where.
00:20:40 Lauren Pearl
You are really widely off in your initial assumptions.
00:20:43 Dr Genevieve Hayes
Another thing that struck me while you're talking is have you ever heard of Fermi questions?
00:20:49 Dr Genevieve Hayes
When I was applying for graduate actuarial positions, everyone got these in their job interviews. They are basically.
00:20:57 Dr Genevieve Hayes
The questions where you had to estimate something that you would have no data on whatsoever.
00:21:03 Lauren Pearl
Ohh yes, yes, I've heard this called.
00:21:06 Lauren Pearl
Market sizing questions they gave them, they gave market sizing questions in case interviews. When you become a consultant like for I was consultant at Deloitte, but they do at McKinsey, they all the big places.
00:21:18 Lauren Pearl
Yeah, yeah. Market sizing based on no data.
00:21:21 Dr Genevieve Hayes
Yeah, I had one.
00:21:22 Dr Genevieve Hayes
My first job was as a consultant and the question I got in my job interview was how many petrol stations are there in Australia?
00:21:30
Yeah, yeah, yeah.
00:21:31 Lauren Pearl
Yeah, I don't recall what my market sizing question is and I don't think I could probably perform it anymore if I tried. But yeah, just it's the same concept. You're totally correct that you're trying to.
00:21:43 Lauren Pearl
Think about, OK.
00:21:44 Lauren Pearl
I obviously I don't know what the actual.
00:21:48 Lauren Pearl
Number is, but there's all this.
00:21:50 Lauren Pearl
Other data in my universe that I have.
00:21:52 Lauren Pearl
Access to.
00:21:53 Lauren Pearl
And can I?
00:21:54 Lauren Pearl
Connect these dots such that I can come up with a really reasonable guess based on data. I do feel more confident.
00:22:02 Dr Genevieve Hayes
So you mentioned that the main tool you use when you're teaching financial modelling is excel. Do you just use your straight Excel spreadsheet with formulae in cells, or do you use macros or anything more fancy than that?
00:22:17 Lauren Pearl
Well, so typically when I'm teaching, I actually like to start outside Excel and listeners might be horrified to learn that I actually encourage my students to start with pen on paper. I know it sounds terrible, but I will caveat this with that.
00:22:38 Lauren Pearl
My course is extremely attended and that there's really high reviews and that I'm not torturing people. The reason we start outside Excel is I find, especially if you don't come from a financial background, but even if you.
00:22:50 Lauren Pearl
Do when you're looking at an Excel document, I look at them all day, every day. I'm a finance person now.
00:22:58 Lauren Pearl
And my first reaction is my eyes glazing over. There's just so much there, right? You can't help it, even for people who look at spreadsheets every day, there is that moment where you're looking at it and you don't know what's.
00:23:09 Lauren Pearl
Going on you.
00:23:10 Lauren Pearl
Kind of have to dig in and click around and look at the.
00:23:12 Lauren Pearl
Formulas to figure out like how does.
00:23:14 Lauren Pearl
This work so when we're.
00:23:16 Lauren Pearl
Trying to learn how to translate.
00:23:19 Lauren Pearl
The idea of your business into math.
00:23:21 Lauren Pearl
I find it's much.
00:23:22 Lauren Pearl
Easier actually to start.
00:23:24 Lauren Pearl
By just looking at the formulas, because what we're really trying to do when we build a financial model is we're starting from like a high level equation. Most financial models are projecting profitability. They'll maybe look at cash too, because those are slightly different, but they're looking at how much money will your company make and is it going.
00:23:41 Lauren Pearl
To make money.
00:23:42 Lauren Pearl
Right. And the equation for that is super simple.
00:23:44 Lauren Pearl
It's just profit is equal to revenue minus costs. What's complicated is.
00:23:50 Lauren Pearl
That revenue and costs are really complicated to calculate and you calculate them differently depending on how your business works. And so the very beginning of building a financial model is really about digging into what comprises my business's revenues, what are all the things I'm.
00:24:11 Lauren Pearl
Going to sell?
00:24:12 Lauren Pearl
How many customers are going to come in every month? What is the price point of the items? Like how what are the building blocks of revenue? Do I have sales people? Are there commissions? Right. There's these different factors.
00:24:25 Lauren Pearl
These different variables.
00:24:27 Lauren Pearl
And the same thing for profit. They're sort of a variable tree that you have to create in order to understand how you're going to get to those numbers. So you can calculate profit. And so this is where we start on paper. Basically, by starting with that profit equation and then digging in. And I I actually have my students.
00:24:47 Lauren Pearl
Write down the formulas that they're planning to use to calculate each of these variables, right? So if I'm this hat manufacturer, I would ask how are you going to calculate revenue? OK, looks like hats, so the hats are a certain price.
00:25:02 Lauren Pearl
And I'm going to have certain customers and they're going to buy a certain number of hats. And so my customers times the number of hats they typically buy times the price point they're paying is my revenue, right. But then we can look at that and say, OK, what like, how am I going to know how many customers I'm going to?
00:25:19 Lauren Pearl
Have more variables, right? So then you circle customers.
00:25:23 Lauren Pearl
Number of customers and you say, OK, like, what's the equation for finding the number of customers and we had already, we already have talked about like oh maybe I can use certain marketing statistics to think through how I might predict how many customers are going to come in based on you know how many people I entice to walk into my store.
00:25:40 Lauren Pearl
And so you go in and and what you end up with in on on.
00:25:43 Lauren Pearl
Your written piece of paper.
00:25:45 Lauren Pearl
Are math formulas written in English or whatever language you're using, right? And then you take those and then you're ready to move to excel because this is a way where you kind of are able to visually see the building blocks. Most people who are comfortable with very, very basic algebra.
00:26:05 Lauren Pearl
Addition, multiplication, subtraction doesn't tend to go much more than that.
00:26:09 Lauren Pearl
Right. Can look at a piece of paper with.
00:26:12 Lauren Pearl
Those English language formulas written on them and.
00:26:15 Lauren Pearl
Their eyes don't glaze over because they.
00:26:18 Lauren Pearl
Look like how we'd speak about it.
00:26:21 Lauren Pearl
And so it's very easy to understand the math behind the business without getting all hung up on like what's in each of these cells, what are the formulas here? How are we manipulating this? And that's the well when you have that, you're then ready right to move into Excel and then.
00:26:38 Lauren Pearl
Excel becomes an exercise in OK, I know what I'm calculating. How do I organise it right? How do I just make this like a tool I can use as opposed to like a theoretical breakdown of how I think about this?
00:26:52 Lauren Pearl
So that's what I find is the better process for doing it.
00:26:55 Lauren Pearl
And leads to better models. In the end. Hopefully it's not too painful.
00:27:00 Dr Genevieve Hayes
But from the software engineering background, you know I mean that same approach would apply really well to developing software.
00:27:07 Lauren Pearl
And honestly, maybe that's where it comes from.
00:27:12 Dr Genevieve Hayes
Cause that's how I write software. I just come up with a math of it and it makes everything so much easier in the end.
00:27:19 Lauren Pearl
That's really interesting.
00:27:20 Lauren Pearl
I think I didn't get deep enough into.
00:27:23 Lauren Pearl
Working with software to realise that was the case, but given the background that I had that led me to doing software that makes some sense. It's like logic puzzles. Kind of, yeah. Yeah, exactly. I took logic as an elective when I was an undergrad and it was really interesting because logic was offered by the philosophy department.
00:27:43 Dr Genevieve Hayes
So half of my class were philosophy students and half of them were all maths and computer science students.
00:27:50 Lauren Pearl
Oh, that's really funny. Yeah. I was a philosophy major, so maybe this is not making sense. You're doing some psycho analysis on me that I did not realise.
00:28:01 Dr Genevieve Hayes
So what's the typical background of your clients?
00:28:04 Lauren Pearl
Typically I work with clients who most of them don't have a finance package, so I work most times with founders who were previously an engineer. I worked with one client who previously held amazing positions in government and is now building a private company for the first time. I work with one client who was an.
00:28:25 Lauren Pearl
Amazing audio engineer and now has a thriving podcasting company with with lots of employees, so it tends to be folks.
00:28:33 Lauren Pearl
Who? They grew a business because they were able to do something really amazing, and now they're kind of playing catch up, working on the financial aspects because they've been running the business kind of without that piece on gut instinct. And that business has now grown to the point where that's starting to break down very similar.
00:28:53 Lauren Pearl
Honestly to the way that that my business, my family was run for so many years.
00:29:00 Dr Genevieve Hayes
I would imagine that.
00:29:01 Dr Genevieve Hayes
A lot of them would have the attitude that they're doing this business, but the financials are the responsibility of the accountant that they've hired or.
00:29:11 Dr Genevieve Hayes
If they've hired a CFO, that person.
00:29:14 Lauren Pearl
Yeah, totally. How?
00:29:17 Dr Genevieve Hayes
Do you make them understand the importance of them understanding financials and not just saying go do this accountant?
00:29:24 Lauren Pearl
Yeah, it is a working process for getting that exactly right. I really focus on.
00:29:32 Lauren Pearl
And starting with what is most valuable for the client. So I think in in any technical field, I feel like this is true where it's work to understand a new technical topic and the work has to be worth it, especially if the person doing the work is a super busy CEO who has 15 things that are pulling their attention.
00:29:54 Lauren Pearl
And so we normally start with the parts of the business where the CEO is searching for answers. They really need to know more. For example, you know, they're selling and selling and selling, and they don't see it. They there's no cash in the bank and they don't know what's going on or you know, they.
00:30:15 Lauren Pearl
Are trying to hire. They want to hire and they have no idea how much money they'll be making next month, and they don't.
00:30:21 Lauren Pearl
Know how to figure that out?
00:30:23 Lauren Pearl
So starting with a problem that they have that to them doesn't feel like finance, it's not accounting, it's not technical, it's not.
00:30:31 Lauren Pearl
Nerdy, it's very practical.
00:30:34 Lauren Pearl
Giving them those answers and showing them how to think about those things, using math and using financial concepts and tools. They probably already have, and data they probably already have.
00:30:45 Lauren Pearl
To be a real.
00:30:45 Lauren Pearl
Unlock and once you get that unlock, I think.
00:30:50 Lauren Pearl
You're starting kind of that campaign that it internal sort of idea that, oh, this is something useful that can really help me run my business when finance is an answer to a question they already have, it's a lot more enticing. So for the most part, as a fractional CFO coming in, I'm looking for those opportunities.
00:31:11 Lauren Pearl
And then it tends to be kind of like a ball rolling down a hill once you know.
00:31:15 Lauren Pearl
A little bit it.
00:31:17 Lauren Pearl
Makes you want to learn more. It starts to change the way you think about the.
00:31:20 Lauren Pearl
Business it starts.
00:31:21 Lauren Pearl
To get you thinking when you hit another problem.
00:31:24 Lauren Pearl
Oh, maybe it could be useful here too. And when they're pulling that information from you, that's the good thing. Like, you want that desire to come from them rather than kind of force feeding it. If you force feed it, it always is a chore and it will.
00:31:37 Lauren Pearl
Always continue to be a chore.
00:31:39 Dr Genevieve Hayes
I'd imagine, given you say that a lot of your clients are from an engineering background that have above average maths and tech skills so.
00:31:48 Dr Genevieve Hayes
I would imagine that once they get the need for this, they would catch on to how to do all the calculations very quickly. Is that right?
00:31:57 Lauren Pearl
Oh yes, definitely. And in fact, I think a lot of them are already doing certain calculations. And when I come to them, I'm simply giving them kind of supplemental information to connect it to financial data, so.
00:32:10 Lauren Pearl
Often engineers CEOs will be excellent at tracking things. Like all the customers they've spoken with, they track a lot of stuff, right? It naturally like numbers. If I can connect those customers to the revenue number on their profit and loss statement, that's really powerful. Yeah. So it really just is about filling in those pictures and showing them why their financial data.
00:32:33 Lauren Pearl
Is relevant to them. I will say oftentimes the disconnect for someone who really loves numbers but doesn't love financial data.
00:32:41 Lauren Pearl
It's often that the financial data most businesses have most access to, which is historical financial statements. They're all in the past.
00:32:53
So it's hard.
00:32:53 Lauren Pearl
To do anything with them right? Like if you have financial statements are something that a lot of businesses just think about at the end of the year.
00:33:01 Lauren Pearl
Because you need.
00:33:02 Lauren Pearl
To to do them for taxes or for reporting if.
00:33:05 Lauren Pearl
You have a board or something.
00:33:06 Lauren Pearl
Like this, so they're not.
00:33:07 Lauren Pearl
You. They're like a way for other people to check up on you, but they're not really something that you can use as CEO to run your business because you care about data for the future. You care about what's going to.
00:33:18 Lauren Pearl
And and you know, for accounting, it's worse than it just being in the past. It's months in the past these times because the accounting data is high assurance record that takes time to complete your for some businesses, this is not true, but for most businesses your financial data.
00:33:39 Lauren Pearl
Is like lagging by a month or two because your accounting team needs.
00:33:43 Lauren Pearl
Some days to.
00:33:44 Lauren Pearl
Go through all of your transactions and log them.
00:33:47 Lauren Pearl
And so it's not just historical, it's late. So in order to really make that data useful, you really need to understand how those past data can be input to things that will then help you forecast and make decisions about the future. So sometimes it's that.
00:34:08 Dr Genevieve Hayes
Yeah, well, it's the data analytics versus data science split. So with data analytics, a lot of that is business intelligence type reporting where you're.
00:34:17 Dr Genevieve Hayes
Reporting on.
00:34:18 Dr Genevieve Hayes
The average number of sales by month for the last two years or something? Or is data science is all about taking that data and using that to predict the number of sales by month for the next two years that are coming.
00:34:34 Lauren Pearl
Yeah, yeah, yeah, same distinction exactly.
00:34:37 Dr Genevieve Hayes
And the data analytics side, it's useful, but the data science side is what allows you to make decisions about how you're going to behave in the future and give given that what you're.
00:34:52 Dr Genevieve Hayes
Clients would need is and this is coming back to what you said earlier. They need to be able to have data right away because data about the future that comes two years into the future is going to.
00:35:06 Dr Genevieve Hayes
Be out of date.
00:35:07 Lauren Pearl
Yeah, that's right. And actually I think this concept of data and timing is really, really helpful to thinking about your startup and kind of navigating the the data you have to work with and the, the the numbers behind your business timing often can get confusing in finance.
00:35:27 Lauren Pearl
Because there's so many kinds of.
00:35:30 Lauren Pearl
The data can change over time, right? And that also, as a CEO can get really frustrating, like, oh, my sales number for March was, you know, 100,000 and now it's 150 or 90 like.
00:35:41 Lauren Pearl
What the heck?
00:35:42 Lauren Pearl
Happened and it's like well it cause it depends on when you pulled that number and maybe there was some revenue that wasn't recognised that got added.
00:35:50 Lauren Pearl
And it can also cause a lot of, like, mistrust for a CEO, especially one that's not comfortable with financial data in the way it works. So these numbers are wrong. Someone's making a mistake.
00:36:00 Lauren Pearl
I can't trust it, right? So that's something that definitely happens. So in order to navigate that, you need to have some familiarity with the context around the number that you're pulling, right?
00:36:11 Lauren Pearl
And you might.
00:36:12 Lauren Pearl
Need data that points to the future, potentially like a forecast, and if you have one you need to know the context in which it was forecasted and what data it's based off of.
00:36:22 Lauren Pearl
So that you know how seriously to take it, you need to know what data in your organisation is simply real time. Because of the nature of what it is, right, certain things just update automatically, like certain marketing analytics might update every night and you might be able to use those to predict.
00:36:39 Lauren Pearl
Other data that doesn't happen in real time and then kind of square them once it comes together. The other thing you can look for are data that are like leading indicators, right? So data that isn't the data that you're looking for, but is highly correlated with data that you would love to know. But the leading indicator is.
00:36:59 Lauren Pearl
Going to tell you a couple of months in advance what is likely to happen in in two months, so building kind of a universe of different data points that can give you the key metrics that you need to know as quickly as you need to know them as sort of part of that system, you need to build as a star.
00:37:17 Dr Genevieve Hayes
Suppose I'm a client who's attending one of your workshops. I've attended your workshop. I've built this nice shiny Excel spreadsheet, and now because I'm a startup CEO, I save that on my computer and then I go back to doing all those 15 other things that keep me busy. How do I maintain this?
00:37:38 Dr Genevieve Hayes
Spreadsheet over time so that it doesn't become out of date. Am I expected to do that or do I delegate that to one of?
00:37:44 Dr Genevieve Hayes
My stuff.
00:37:45 Lauren Pearl
I think that if you are familiar with the way that the model works, it's perfectly fine to have somebody else be in charge of continuing to put the data in, but it's really about.
00:37:57 Lauren Pearl
You just need to make sure to look.
00:37:58 Lauren Pearl
At it typically.
00:37:59 Lauren Pearl
The way I advise startups to build the model would be that all of your kind of.
00:38:06 Lauren Pearl
Inputs that are static over time, so like benchmarking type input go in one tab of your Excel. So they're like all in one place and you know if you're editing anything, that's where the edits go.
00:38:17 Lauren Pearl
And then you have a bunch of kind of Excel acrobatics in in some of the sheets where all of those.
00:38:23 Lauren Pearl
Data churn into outputs and then you have your financial forecasts essentially in financial statements. So your profit and loss statement, your balance sheet.
00:38:36 Lauren Pearl
And your cash flow statement and those are outputs?
00:38:39 Lauren Pearl
The way that I like.
00:38:41 Lauren Pearl
To update these models is essentially. Those outputs should match the same format of your financial statements that, like your real financial statements as you operate as a business and every month you update the columns that represent sort of months in those financial statements with your.
00:39:00 Lauren Pearl
Actuals. OK, so you have your income statement, your balance sheet and your cash flow statement and they have columns for the months in your first iteration of your model. Most of those months are calculations based on your inputs, but.
00:39:18 Lauren Pearl
If you build your model in a smart way, you can copy paste values over those formulas with actual data from your financial statement and then the projections of how you're going to do over the course of the year and this sort of thing update automatically because your model will pull from those values.
00:39:38 Lauren Pearl
Rather than the formula that's generated.
00:39:40 Lauren Pearl
Your inputs and so then you get kind of that updated yearly projection and at the same time you can look at the value you're values you're getting in comparison to the values you calculated to the OR the. The numbers you calculated based on inputs and you can say were these numbers similar or were these numbers really different and if they were really different?
00:40:00 Lauren Pearl
You can go back to your inputs tab and say oh OK, why were they different? Were some of these assumptions a little off and dig into. Oh, yeah, my churn, which is the percentage of customers that leave you every month turned out that was a lot higher. So maybe you raised that percentage or what have.
00:40:19 Lauren Pearl
And it basically will change the projection for all of the future months to be more in line with the data you're already seeing, so you can outsource that to somebody else. But I do think especially at the beginning, it's really helpful for a founder to do that themselves, especially if they're not.
00:40:39 Lauren Pearl
Used to thinking about their business in math because it reinstalls those lessons of thinking around.
00:40:45 Lauren Pearl
On the churn is connected to the financial statement, right? You're basically building this really complex web of formulas in your head and the best end result that you want is some of those things start to help you make like quick decisions because you almost have a sense of these numbers instinctually.
00:41:08 Lauren Pearl
And you get to kind of fold them into the business genius that you already have when you manipulate the model this way, it reinforces those must.
00:41:16 Dr Genevieve Hayes
Calls. It's like if you're building software to automate something, it helps to have actually physically done it manually yourself, because then you understand what you're trying to automate.
00:41:27 Lauren Pearl
Yeah. Yeah, yeah, yeah, it's like that, but in reverse, almost like you're trying to review the automation again and again. So that when you do perform it manually or you're asked about the process, you know the process super well.
00:41:42 Dr Genevieve Hayes
Yeah, exactly.
00:41:43 Dr Genevieve Hayes
Yeah. In addition to teaching your clients how to build these financial models, do you teach how to statistically analyse the outputs or is that a bit too advanced?
00:41:54 Lauren Pearl
Typically we don't go into a lot of analysis on my financial modelling course. We do walk into marketing analytics and talking about projecting things first based on conversions in order to kind of get the likely outcome.
00:42:10 Lauren Pearl
But not a lot of analysis in the.
00:42:13 Lauren Pearl
Financial modelling course.
00:42:14 Dr Genevieve Hayes
What do you mean by marketing analytics?
00:42:17 Lauren Pearl
Yeah. So marketing analytics and we talked about how to make a good guess.
00:42:22 Lauren Pearl
On customer demand.
00:42:24 Lauren Pearl
Marketing analytics is the way you do that. Marketing analytics basically studies how customers move through the process of.
00:42:36 Lauren Pearl
Discovering you to buying your product those steps, and we call that a pipeline or a funnel, right where where customers happen to know about your company and then some of them will go to learn more and then some of them will demonstrate buying behaviours and some of them will actually buy something from you.
00:42:56 Lauren Pearl
And the more you know about how those numbers breakdown, the more accurately you can forecast how many customers are going to come in the door based on how many kind of eyeballs you can buy with your marketing activity.
00:43:10 Dr Genevieve Hayes
In addition to the work you're currently doing, you've spent a lot of your career working in and around technology.
00:43:16 Dr Genevieve Hayes
And how do you see technology as being able to help startups and businesses?
00:43:22 Lauren Pearl
Yeah, technology has.
00:43:23 Lauren Pearl
Totally changed the game for starting new companies. Things that used to take an entire team of engineers. You can now just go get a no code tool and and throw together in far less time and that sort of efficiency is happening all over the place from you know how you do your benefits.
00:43:43 Lauren Pearl
Even incorporating your business, lots of things where you used to hire either a full time person or maybe a third party to do it for you. You can now go buy a tool. What's great about that is it's lower the barrier to entry to creating a company and like what is required to actually put together the business and start building.
00:44:03 Lauren Pearl
Like the you know, MVP version of your product, the minimum viable product to start getting data about if customers even like it, that part can happen much more cheaply.
00:44:14 Lauren Pearl
So I think that's kind of the way that I see technology evolving the most from where I sit in in supporting founders.
00:44:21 Dr Genevieve Hayes
Do you have any thoughts around generative AI tools such as ChatGPT?
00:44:26 Lauren Pearl
To be honest, most of my work goes.
00:44:28 Lauren Pearl
Around like the.
00:44:29 Lauren Pearl
Education piece behind starting a business and.
00:44:34 Lauren Pearl
I see lots of ways that AI has made certain things easier and kind of continued to collapse the requirements for starting a company you know.
00:44:45 Lauren Pearl
All of those.
00:44:46 Lauren Pearl
The no code tools and technologies replacing humans and other aspects that didn't necessarily use AI.
00:44:54 Lauren Pearl
If you put AI into the mix, those capabilities or even greater, so I expect that that will just continue to shrink what's necessary to just get.
00:45:02 Lauren Pearl
It one of the things I think a lot about with AI in the area of startups, not AI startups, because that's a whole thing. AI startups just it's a great place to get funded right now. But as a non AI startup trying to make things happen and what AI means to you while the barrier to entry.
00:45:22 Lauren Pearl
Of doing all the things you need to do to create a business can go down.
00:45:26 Lauren Pearl
Because of AI.
00:45:27 Lauren Pearl
I also see a hardship happening, which is that.
00:45:32 Lauren Pearl
Any areas where you're directly competing with big companies where those big companies can leverage AI, the advantage those big companies have will be even greater, making the AI better and better is something that big companies are always going to have bigger budget.
00:45:47 Lauren Pearl
To do.
00:45:48 Lauren Pearl
And so I think about especially things like marketing.
00:45:52 Lauren Pearl
And I speak about marketing a lot, maybe for a person who's a CFO, but I think marketing just is something that startups really, really have to focus on like customers are really the number one thing that if you're starting a company need to be a big area of focus. So thinking about marketing a lot is a very effective way to spend your time.
00:46:10 Lauren Pearl
And in the marketing realm specifically, it's highly, highly competitive to get attention and this has already been a problem for non AI reasons because there's, you know, monopolies that own some of the marketing channels that can kind of price the advertising as and whatever price they want. And we're so inundated with ads these days.
00:46:30 Lauren Pearl
That humans were really trying to get rid of as much advertising and have the sort of bad taste in their mouth.
00:46:35 Lauren Pearl
When we see an ad.
00:46:37 Lauren Pearl
So all of that is happening.
00:46:38 Lauren Pearl
Thing and when you add AI's capability to help people create marketing content or be even better at capturing someone's attention or affecting someone's buying behaviour using that AI, you could see where those big companies are just going to have so much advantage.
00:46:58 Lauren Pearl
In that kind of 0 sum competition game of marketing.
00:47:01 Lauren Pearl
And startups have to figure out.
00:47:04 Lauren Pearl
How they can compete, how they can continue to have customers, be aware of them in an environment where AI is, you know, really, really given bigger companies that advantage. Have you seen the movie paranormal activity? No, I haven't. It's one of those really low.
00:47:23 Dr Genevieve Hayes
Budget horror movies it costs $50,000 to make, apparently, and made over $100 million, which gave it.
00:47:28 Dr Genevieve Hayes
Yeah. The bigger return on investment than Avengers end.
00:47:34 Dr Genevieve Hayes
Game and you know it's not a great film, but it's an incredible film to watch in that, seeing how the film makers managed to make a effective horror movie with the budget, that's probably the cost of a car. And yeah, so I think that's the attitude.
00:47:55 Dr Genevieve Hayes
Startups need to have the they're not the Marvel Cinematic Universe that how can they produce the paranormal activity version of what they're doing?
00:48:06 Lauren Pearl
Yeah, that and and I think you know that's that startups needing to be scrappy around these things and doing like quote unquote we call it growth hacking right is that parts nothing new. It's always been the case that corporations have had more marketing dollars to spend than startups. That's always been true. And I think stars will continue to be scrappy.
00:48:27 Lauren Pearl
And at the same time you will find like individual startups bucking the norm, but on an overall basis, thinking about what it does to the landscape of companies, I think is where it gets interesting. I think often in startups we we think about exceptionalism a lot. You know you're going to be the one in 100.
00:48:44 Lauren Pearl
Makes it, but sometimes that attributes kind of too much like ohh you can do it. Attitude to the founder, where as a phenomenon if there's something in the landscape that just gives bigger companies an inherent disproportionate advantage, it's going to affect the landscape, right. One of the things that.
00:49:02 Lauren Pearl
Happens if you've got.
00:49:04 Lauren Pearl
You know, if you're going to give big corporations.
00:49:07 Lauren Pearl
A really disproportionate advantage, more so than they already have, it changes.
00:49:11 Lauren Pearl
Is the shape of the market right and we're already seeing this a little bit with startups, with advertising is really challenging right now and you're seeing fewer business to consumer startups get funded these days, right? Business to consumer means that your customers are average people like you and me and it's meant to be to separate.
00:49:31 Lauren Pearl
Those kinds of businesses, from B2B business to business where your customers are other business.
00:49:37 Lauren Pearl
B to C, businesses are fewer now in terms of funded companies because investors have realised it's just so hard to make money doing them because marketing costs have made it prohibitively expensive to pursue that kind of business. And they'd much rather you do a business to business company where you can use a different sales technique that kind of gets around the high cost of.
00:49:58 Lauren Pearl
Of advertising. So something like AI if it gives a disproportionate advantage to corporations, I could see it changing the landscape even more where investment dollars tend not to go towards those small companies that need to compete against the bigger ones.
00:50:13 Dr Genevieve Hayes
That's very interesting. I never thought of it.
00:50:15 Dr Genevieve Hayes
That one, what final advice would you give to data scientists looking to create business value from financial data and data in general?
00:50:25 Lauren Pearl
So in general, I think as a data scientist and I would say just in general, as someone who really loves the numbers, my number one advice would be start with the business question first, bring in the data, 2nd and it's an instinct that can be really challenging when you're a numbers person.
00:50:45 Lauren Pearl
Is I don't know.
00:50:46 Lauren Pearl
About you. But I get really excited, like digging into an Excel spreadsheet and discovering like a new way to calculate something or analysing some data and discovering A trend that I didn't know existed or like did that the the the numbers itself are interesting to me.
00:51:04 Lauren Pearl
But that's not necessarily interesting to the business owner, and it might not be important to the business and so.
00:51:13 Lauren Pearl
I would say that if you're trying to create business value, starting first, having really deep conversations with business leaders who have those questions right, we talked about this a little bit in terms of convincing a CEO who doesn't like numbers that finance is useful. You want them to.
00:51:33 Lauren Pearl
Ask the questions.
00:51:34 Lauren Pearl
So and you want you want your data to be an answer to a question they already have, because that's what's gonna be the most interesting. And the CEO tends to also have the priorities of the business in mind. So if you're answering one of their questions, chances are you're doing something really.
00:51:48 Lauren Pearl
Useful for the business.
00:51:49 Lauren Pearl
Yes. So that's the best, biggest advice I would get is before you go into the rabbit hole of what's really cool and interesting and fascinating because there's a lot of stuff there, start with a business question and then figure out what data and what analysis is really necessary to get after that. And honestly say you're working alongside a CEO. If you give them that.
00:52:11 Lauren Pearl
Answer and it's great and it's helpful. There will be the opportunity to dig in deeper because if it's useful, more data in that area will be useful too.
00:52:22 Dr Genevieve Hayes
The listeners who want to learn more about you or get in contact. What can they do?
00:52:27 Lauren Pearl
The best way to find me would either be on LinkedIn or on my website. So I'm Lauren Pearl on LinkedIn and my website is Lauren Pearl consulting.com.
00:52:38 Lauren Pearl
And for anyone who's just looking for more tips from a start up CFO, a great place to get that is my newsletter, which you can join either on LinkedIn or.
00:52:47 Lauren Pearl
On the website.
00:52:48 Dr Genevieve Hayes
And I've signed up to that and I think it's a very interesting newsletter and I encourage people to.
00:52:54 Dr Genevieve Hayes
Sign up for it.
00:52:55 Lauren Pearl
Ohh, I'm so glad you're liking it. That's very encouraging.
00:52:59 Dr Genevieve Hayes
Anyway, thank you for joining me today, Lauren.
00:53:02 Lauren Pearl
Thank you so much. It's been a real pleasure.
00:53:04 Dr Genevieve Hayes
And for those in the audience, thank you for listening. I'm doctor Genevieve Hayes, and this has been value driven data science brought to you by Genevieve Hayes Consulting.