Austin Gardner-Smith Podcast Transcript
Austin Gardner-Smith joins host Brian Thomas on The Digital Executive Podcast.
Welcome to Coruzant Technologies, Home of The Digital Executive podcast.
Brian Thomas: Welcome to The Digital Executive. Today’s guest is Austin Gardner-Smith. Austin Gardner-Smith is a seasoned entrepreneur and executive with deep expertise in consumer brands, finance, and technology. As the CEO of Drivepoint, he leads a team dedicated to providing scaling consumer brands with cutting edge financial tools that enable smarter capital allocation, accelerated growth, and increased profitability.
Under his leadership, DrivePoint has become the go-to-to-strategic finance platform for top brands like Oats Overnight, Simple Modern, Rasa, and many others. Before founding DrivePoint, Austin built a diverse career spanning performance marketing, product development, and strategic finance. He held key leadership roles at Hill Holiday, where he was responsible for driving revenue growth and led the go to market strategy for new product offerings.
At Nannigan’s, an advertising technology company, he oversaw major platform overhaul, collaborated with platform partners like Facebook and Google, and worked with top tier clients, including Wayfair, Zappos, and Uber. His ability to blend financial acumen with operational excellence has made him a trusted advisor to consumer companies navigating growth.
Well, good afternoon, Austin. Welcome to the show!
Austin Gardner-Smith: Thanks very much. I appreciate you having me. It’s great to be here.
Brian Thomas: Awesome. I appreciate it. I appreciate you hailing out of the great city of Boston or that area. Anyway, I’m in Kansas City traversing time zones today a little bit, but not a big deal.
Austin Gardner-Smith: Just getting into wintertime here in Boston, but making it okay. Fall leaves are a little bit of a salve for the cold coming in.
Brian Thomas: Yeah, I get it. We have like crazy 80-degree weather, which is weird for this time of year, but don’t worry We’ll get that cold snap coming in. I’m sure Austin jumping into your first question here with your background in finance and strategic growth How do you approach capital allocation for scaling consumer brands differently than traditional methods?
What common financial pitfalls do growing brands face and how does dry point help avoid them?
Austin Gardner-Smith: Yeah, it’s a great question. I think as I sort of approached this world and started learning more about financing and consumer brands over the course of my career, one thing that always struck me was, you know, when you have a consumer business, you kind of have to be good at forecasting and good about thinking into the future because you actually have to buy The inventory that you’re going to sell before, you know, exactly what the demand for that inventory will be.
And so, it’s always been a game of trying to make plans and hoping that you get the forecast right in retail from, you know, the 1900s all the way up through today. I think the toolkit, unfortunately for most brands, hasn’t changed that much over that time either. So, most folks are sort of used to putting their finger in the wind or doing a little bit of trend analysis on their historical data and trying to figure out what’s going to happen in the future.
And then they’re making pretty big capital allocation decisions based on, you know, relatively uneducated guesses in many cases about what might happen in the future. And so, I think the biggest thing that we’re trying to unlock or trying to help CEOs with as they go to make those capital allocation decisions is to have some numbers or some data on the other side of those decisions.
So that as they’re thinking about, hey, do I place a bigger inventory by And maybe try and shoot for the moon. Do I place a smaller inventory? Do I think about spending more on marketing? We want them to be able to put dollar signs next to those decisions so that they can actually evaluate the impact of what’s going on.
And with big data and technology and all of the sorts of tools that we have at our disposal. It’s possible to do that for these brands. So that’s a lot of what we’re focused on is trying to help those CEOs and management teams actually use a data driven framework, be able to run out scenarios for all those different calls that they have to make and be able to put dollar signs next to those decisions that they can make the best capital allocation choices that they can.
Brian Thomas: Thank you. I appreciate that. And in today’s world, there’s just so much uncertainty and having those tools again using big data, predictive modeling, some of that large language models will obviously help customers really make the right decisions as they move forward to tackle the upcoming certainty anyway.
So, I appreciate that. Austin, DrivePoint has quickly become the go-to-date finance platform for consumer brands. Can you share what sets DrivePoint apart in the crowded field of financial tools? And what do you believe resonates most with clients like Oats Overnight and Simple Modern?
Austin Gardner-Smith: Yeah, it’s a great question.
I think the number one thing that sets us apart from other tools or players in the marketplace is that specific focus on consumer brands. So from the ground up, we’ve been focused on this one specific type of customer, this one specific type of business. And to me, that makes all the difference. I think there’s a couple of reasons why that’s critical.
A lot of times when you’re installing a piece of software or you’re making a system switch, you’re spending lots of time and money on implementation and set up, and it takes a long time to get value out of that product. And so the fact that drive point is already set up for a consumer brand just shortcuts that whole amount of pain and drudgery that customers are doing when they go to set up something that’s kind of a toolkit, but it hasn’t really been customized for your business.
And so, I think that focusing on consumer brands makes it super easy to get started with us. It makes it super-fast to get value and really gives consumer brands the confidence that, hey, we’ve seen this before. We’ve seen other customers who are in your shoes, and we know how to solve these challenges that you might have.
I think the other big piece that that gives us is the ability to build features that are really specific to the needs of these customers. So, you mentioned. It’s overnight and simple modern for both of those customers, you know, very large e commerce and consumer brands. The couple of big things that really have resonated with them are our ability, for example, to predictively model what they’re returning customers are going to do.
So not that hard to understand what a customer will do when you 1st. Acquire them. You’re going to spend some money on marketing, usually Facebook ads. They’re going to come to your website. They’re going to make a purchase for a certain amount of money. You can kind of do the math on that, but much more difficult to understand when that customer is going to come back, make their subsequent purchases.
And as you grow, Oats and Simple Modern have, that returning customer forecast becomes a huge part of the revenue mix. And so being able to dial in accuracy using some of our machine learning models has really been a game changer for those brands. So that’s just one example of the type of features that we’ve been able to build again, because of that kind of laser focus on consumer and retail brands.
So, I think, you know, lots of sort of specific points of difference, but it pretty much all emanates from that core of being specifically built for this vertical and these types of businesses.
Brian Thomas: Thank you. That’s helpful. And sometimes when you niche down a little bit, it’s very helpful. You get laser focused and solving a particular issue and get really good at that.
I appreciate you highlighting some of those examples of those companies and how your product is obviously easy to engage with and use. So that’s very helpful. And Austin, you’re passionate about using data to drive successful outcomes. Could you provide examples of how data has helped inform pivotal decisions for DrivePoint or for your client brands?
How do you envision the role of data evolving in the future of consumer brand strategy?
Austin Gardner-Smith: Yeah. So, I think, again, a lot of the decisions that these brands have had to make aren’t new, right? We’ve been making these types of decisions for hundreds of years in retail, but the toolkit really hasn’t been there to make optimal decisions.
So, I’ll give you a brief example. We had a customer who knew that they wanted to invest in building a new facility. They wanted to bring their manufacturing in house and be able to save quite a bit of money on their cost of goods and therefore increase their gross margin. And so this was something that they knew that they wanted to do.
And the management team was very sharp, extremely analytical. And so they were thinking about, okay, well, when do we want to invest in the real estate to build out this new facility? Real estate prices were coming down where they were. So, in their head, they were being good, responsible operators of the business and said, okay, well, let’s wait 3 or 4 or 5 months and see if these real estate prices come down.
Maybe we can save a 1, 000, 000 dollars on the facility. And using dry point, they were able to actually model out the different scenarios of saying, okay, well, what happens if we purchase the facility today at a higher price? What happens if we purchase the facility far down the road at a much lower price?
And then kind of a whole bunch of scenarios in between those 2 options. And what they found was that the gross margin impact of getting that facility online was actually worth almost any price. And so they actually made the decision to pull that purchase forward. Make that investment in the facility.
Get gross margin online. And the difference between, you know, doing what they thought was the right thing and doing what the actual right thing was based on the numbers was about 4 million of EBIT or sort of profit straight to the bottom line for the business in that year. And so I think that’s a great example and sort of a big one of where scenario planning and having a tool like drive point can really help you make better decisions that can really impact the business.
There’s tons of other ones. I think we’re working with a ton of companies right now on optimizing their subscription programs about thinking about, okay, well, how much do you trade as a discount on a 1st purchase in order to entice somebody to take a subscription versus a 1-time purchase? There’s a ton of sensitivity modeling around that.
That can be very difficult to do by hand that we can do in just a few seconds and help companies do that. We recently had a company that sort of immediately get the profitability over the course of about 60 days by making a change to that part of their business. So, I think those are the types of things that you can expect.
I pride ourselves on not just being able to save our customers time and money, but actually being able to help them increase enterprise value, expand margins, increase growth. Those sorts of things are really where we hang our hat.
Brian Thomas: Thank you, Austin. And I like the fact that you’re highlighting some of these examples of wins for your customers.
And I really like that. A lot of times we’ll dive into some of the technology behind it, but today you’re sharing some real-world examples of really getting to the bottom line for your customers. And that’s awesome. Austin, last question of the day, what trends do you see shaping the future of consumer brand finance, especially with the growing importance of technology and data driven insights?
How is DrivePoint adapting to these trends to stay ahead?
Austin Gardner-Smith: Yeah, I think a couple of the big things that are happening in consumers are obviously happening across other brands. But when you think about consumer finance, I think there’s a ton of opportunity for continuing to automate more and more of the forecast so we can call it a I or machine learning or predictive analytics, whatever label that you want to put on it right now, even with a great tool kit.
Humans are generally required to think about. Okay, well, what do I think is going to be the forecast for the next couple of months, what do I think this driver of my financial model should be? Whether that’s advertising spend or average order value or things that might be relevant for a consumer brand.
And I think more and more, we’re going to find places where we could actually use both the historical trend data as well as some forecasting algorithms to start to potentially get even more accurate than humans might be and be able to allow them to explore a range of outcomes in a way that.
Doing things manually or filling out Excel sheets by hand just won’t allow you to do. So, I think that that’s huge. And one thing that we talk about a lot is this idea of generative finance or generative financial planning. And what I mean by that is that, you know, today, basically, if you’re a CEO and you want to understand, well, what happens if I change this part of my business?
Or what happens if I change this other part of my business? Typically, what you’re doing is sending an email or a text message to your finance person, and they’re opening their Excel model and monkeying around with some numbers. And maybe they get you 1 or 2 kind of half-baked answers. And that takes 2 or 3 days.
And I think what we’re really looking at and what we’re starting to build around, Is this idea of picture that CEO waking up every morning and getting an email from a system like drive point that says, Hey, you know, we ran 652 scenarios last night to try and find areas of opportunity for your business.
And here are 3 that we think are are super interesting, and you should go and take a look at those in more depth and see if you can action on those in the next couple of weeks. And so I think that that’s really where the puck is going, where you’re going to have. Finance people who become, you know, great stewards of sort of defining the parameters of what’s possible and then starting to let some of the machines do the work in terms of optimizing and finding the best combination of factors that can create, you know, a better outcome for the brand on both growth and EBIT.
And so, it’s super interesting. We’ve got a new feature called experiments that we’re launching. We’ve got some features that we’re calling DrivePoint Intelligence. Around trying to do trend optimization and more forecasting, so lots more to come here, but a very interesting time to be operating in finance and consumer.
Brian Thomas: That’s awesome. You know, using machines to do all this analysis and obviously you could do that in minutes. Maybe a little longer depends, but I like your example of where people can come in in the morning and, and they’ve got a lot of insights and things that they can actually act on versus we’ve all done pivot tables and that sort of thing, forecasting and spreadsheets, and it just doesn’t work.
So, I really appreciate you highlighting that and Austin, it was such a pleasure having you on today and I look forward to speaking with you real soon.
Austin Gardner-Smith: Thanks very much. Thank you for having me.
Brian Thomas: Bye for now.
Austin Gardner-Smith Podcast Transcript. Listen to the audio on the guest’s podcast page.
