Unlocking Profit: The Hardest (But Most Important) Part of Scaling

[00:00:00] Richard Gaffin: Hey everyone. Welcome to another episode of The Ecommerce Playbook Podcast. I'm your host, Richard Gaffin, Director of Digital Product Strategy here at Common Thread Collective joined again as I usually am, of course, by Mr. Taylor Holiday, our CEO here at Common Thread.

Taylor, what's going on, man?

[00:00:15] Taylor Holiday: Nothing. Nothing, man, just I mean, nothing and everything all at once.

[00:00:19] Richard Gaffin: Yeah, right. That's how it goes. So generally, so what we want to talk about today is a little bit of what we talked about on the last episode of the pod. Which is just continuing to expand upon why we think the prophit system is so great and why it's so critical to put an operating system in place in order to achieve your specific business goals.

And so what we wanted, the original way we were thinking of framing, this is the most important part of the prophit system. And I think the way you can think about this is like, what our topic for today is, this is there's a certain aspect of what the prophit system does. That's absolutely critical.

To streamlining the way that your business functions. And what that is, is it forces everybody to define things the same way. And so what we want to get into today is specifically, what are those things? What gets defined? What needs to get defined? Why is it such an issue for businesses when we come to them?

So, Taylor, I'll kick it over to you. Why don't you kind of unpack this a little bit more?

[00:01:09] Taylor Holiday: Yeah, this is, this is going to be not the most exciting part of the system. Maybe

there's an episode coming where we talk about the most exciting part of the prophit system, the most fun part of the prophit system, but this is the most important and what the prophit system does is it creates, it's a forcing function for clarity and shared organizational clarity across some really critical metrics of understanding that without clarity, you can't make good decisions on As we begin to drive towards bottom line outcomes as an industry, there's a level of complexity in that information that goes up exponentially from looking at something as simple as top line revenue. Top line revenue is beautiful in its simplicity. It's really easy. It's one number, but when you begin to pursue bottom line outcomes, you introduced not just one additional number, but like 10 additional numbers to consider along the way, you now have to begin to understand shipping and fulfillment costs, product costs.

Payment processor fees efficiency on marketing dollars. Like there's so many additional layers of expenses that you have to get accurate views into in order to really understand marginal outcomes. And what happens is, is that in many cases, this information is not clear in the organization. It is not clear for a million different reasons, because it's systemically spread across multiple places. It's defined differently in different systems. And so the unification of that information into a singular shared set of definitions. Is an incredibly powerful, but incredibly difficult process.

[00:02:49] Richard Gaffin: Okay. So let's talk about the different, let's say, informational nodes in the business that tend to not be unified. So who was owning different data points? How are they being 

[00:02:58] Taylor Holiday: yeah

let's sort of work top to bottom through like the unit economics of a sale and where the information exists in different form factors. So I said at the end, my last sentence I said was that revenue is simple, but it's actually not, it's actually wildly complex because if I use the word revenue or I use the word sales, that actually means nothing. It has no shared meaning amongst different people. So we have to speak in more accounting specific terms, and then we have to understand which it, which term or definition each system is reporting. So as an example of that Shopify causes the problem in this to me primarily, because they provide everybody this default view inside of their Shopify dashboard called total sales.

Now, total sales is not an accounting term at all. It's a made up revenue definition that is basically order revenue minus returns. Okay. Order revenue is defined as gross sales minus discounts, plus taxes, plus shipping. So these all have different sort of form factor composition of different elements. Of revenue, but total sales takes out returns on a daily actualized basis, which I think is a horrific practice for, for many, many reasons.

But so right there, most people, when they think of revenue, that to me is from what I've seen, the most embedded number that most people get attached to. But the bigger the organization becomes, the more you move into sort of standardized gap practices at that causes all sorts of complexity. So, in those cases, you'll see things much more often as reported in net sales. So net sales would be gross minus discounts, minus taxes, minus shipping Minus return. So like the nettest realization of all revenue or sorry, plus shipping, minus, minus taxes, minus return. So, anyways, you get the point is that that will be more often the number that the organization builds the PNL around, because at the end of the day, a PNL is going to flow down to net sales at some point. And so they'll have revenue definitions in maybe their their BI tool or whatever the, the organization is looking at that would be reported. Net sales. So right there, if you're, you have an organization that has a dashboard in total sales and tracking in BI or accounting, that's net sales, there's going to be a reconciliation to those things all the time. Then brands do things like they'll have an accrued return rate in their systems. In other words, they'll use a returns estimation that they normalize at the end of the month. So they, the accrued revenue definition might be 10%, but the Shopify total sales is actualizing returns every day. So there's a discrepancy of those numbers. Some businesses will create their own revenue definitions where they'll want to take out taxes. So it'll be order revenue minus taxes is one that we see a lot. Cause they just think of taxes as a pass through. Every time you deduct a cost from the primary revenue view, I think you create a problem. To me, the primary revenue definition, everybody should look at should be order revenue, every dollar processed from the customer. And then you just flow out costs from there. Taxes are an expense. They're not, they shouldn't be removed from the revenue definition. They should be considered an expense in the process. But the point is I see. Organizations using inside of the same organization, four or five different definitions of sales or revenue. And as you begin to think about that, how can multiple people every day, there's this like ambiguity of reconciliation and it's exhausting because there has not been somebody who went through all the primary organizational tools and created unification around those ideas or led to one primary measure that gets you there.

And that's just on the revenue side. That's before we even talk about any of the costs in the system.

[00:06:33] Richard Gaffin: right. So, I mean, it strikes me that like part of, because the way we kind of framed it is the most important part of the prophit system, but perhaps a way we can think about it as the most difficult part of the prophit system. Because what this requires is at least some level of accounting understanding across a number of people who are just not, are not expecting to be accountants in their work including let's say, maybe talk a little bit about how you bring the the execution.

Wing. So let's say the media buyers or whatever, into this conversation about like accounting practice, basically.

[00:07:04] Taylor Holiday: well, so, so right away, what's going to happen is that you also have the, the Facebook platform reports, a revenue number that uses, it uses to calculate ROAS and it uses to optimize against that revenue definition is order revenue. It takes every dollar that gets passed through by the pixel, unless you've modified what's getting passed through and cappy, it's going to report back the total order value.

Which is order revenue to the, to the platform. So media buyers are trained in order revenue. They, they think in terms of that number, cause it's what shows up in the dashboard most often. So executionally trying to unify around that premise, I think becomes and set your ROAS goals relative to that number just removes one layer. Of modification to definitions that often will happen is that because at the end of the day, you have to operationalize this, the metric that's, that's where I think accountants, when they set the organizational rules, they get lost. And they're the wrong people to set the organizational rules for what revenue we look at, because they don't have to operationalize it.

They don't have to go and execute against it. Another example is like when you, when you net out returns or you do actual returns, like total sales, that's horrific for marketers because people will batch return a bunch of stuff randomly on a Tuesday that makes it all of a sudden look like your efficiency is off.

If you're using that number to calculate MER or AMER or anything else. It wreaks havoc operationally to use anything other than the order value for definitions of tracking and realizing each day's revenue. So that it's like, really hard. The other, the other thing that's often true that will require a bunch of effort to operationalize this thing is that over many years, there'll be all these little edge cases. That have been written as a script on top of a database. And this is usually true inside of larger organizations for exceptions to revenue that they want to exclude. Okay. So, what do I mean by that? Maybe there was a period where the business did a bunch of gift cards for some reason. And they like sent out a thousand gift cards to these influencers.

And so they've written into the system, some sort of manual exclusion for this really small subset of a specific kind of revenue includes these specific batch of gift cards and that that's, that, that little query was written years ago. And it's sort of just like this compiled legacy set of how the revenue definition gets like.

So there's cases where we walk into an organization and there's actually no human that can tell you the total query that's generating the output of revenue. Like they just, it's too many years of little adjustments of things. We exclude 0 orders and we exclude dispatcher revenue. And we have this, that it's like, and so there's this compilation of changes over time, that's sort of like legacy technology debt that you have to work through.

[00:09:41] Richard Gaffin: Yeah.

[00:09:41] Taylor Holiday: Another big problem is the way that Shopify handles channels. So, channels are often A phrase that's substituted for like apps or other points of distribution that you create in Shopify's ecosystem. So as an example, if you use a point of sale at events, or you have a retail store, or you use something like leap retail to actualize a wholesale purchases or fair, or, you know, you have tap joy, you have an app, you have all these different points at which transactions are occurring. And what will happen is that the Shopify default view will include all the channels. And then you'll have to figure out, okay, in the marketing view for the. com revenue, which of these channels do we want to include return software has changed over time. And so there's just a lot of effort that goes into getting this right. And somebody has to get into the mud and figure it out in order to then get to a place where we can operationalize the metric.

[00:10:35] Richard Gaffin: Yeah, no, that makes sense. So I was going to say like, let's, let's dig into a little bit about why this particular transition is so difficult, but I mean, it sounds like that's pretty straightforward in that there's, well, there's probably like a human aspect to people being sort of attached to certain revenue numbers and not wanting to give them up maybe, but then it also sounds like there's clearly like, there's It's sort of a logistical nightmare to get everybody on the same page here.

So let's, let's talk about like what, what the process is when we come into a business, let's say, and we're here, we're here to clean your data up. What does it look like when we come in and start that problem?

[00:11:07] Taylor Holiday: Yeah, so the first thing we want to know is what is your current source of truth? Where do you guys look every day? Like what's show me your dashboard, you know, and that number there, that revenue number, what, what is it? How would you define it? Can you write it mathematically as a formula for us? And then can we recreate it? So can we begin to understand the inputs that generate that output because getting to what is presently is step one. What is the present revenue definition? Can we recreate it with the data sources that we have? Okay, cool. Now we have present data integrity. Now the question of do we want to change the definition or formula for that calculation at all?

That becomes a philosophical discussion Some organizations are open to some or not and so we'll work through getting to. Okay. Now we have a shared philosophical point of view and we have data integrity around that number. And a lot of times, sometimes that's very simple. Sometimes that's like, like I said, combing through a lot of history of sources. Sometimes there's. For businesses, in particular, those that are not on Shopify and the data is flowing like into a CDP or a custom data lake into a BI tool. And there's like, there's multiple sources for the gathering of the revenue that becomes really complicated, but those are definitely the edge cases. If you're on Shopify, it usually is about matching the channels that we're pulling into the revenue, agreeing on the definition of term. And then recreating the number within some degree of like, for us, we're not trying to be your accounting system. So if we get to like a tiny percentage error off on a day to day basis, cool.

We're very close directionally in a way that we can be confident in the decisions that we're making.

[00:12:30] Richard Gaffin: So where's that maybe break down for me how the, how that recreation happens. So they have a certain source of truth. What are we, are we using stat? We're pulling and 

[00:12:38] Taylor Holiday: Yeah, exactly. So our step one. That's a great. Yeah.

So, so we're going to integrate all your data sources into statless and present back a view we have in our setting section. We can select from a bunch of different revenue definitions. So you're going to tell us which one you believe that this is.

We're going to set our system to report on that view, and we're going to literally pull up day to day revenue comparison and go. Okay. What do you have? What do I have? What do you have? What do I have? Okay. Kevin on our team leads data integration. He makes sure that we get all the clients set up and accurate. And off we go.

[00:13:06] Richard Gaffin: Gotcha. Okay. So at, at that point when we've successfully, let's say, recreated the, the current present data. So what, what, what's the next step then? Cause it, that doesn't clearly doesn't resolve everything right away. Like all of a sudden, Hey, we're on the same page. Let's go forward. What are the next steps like organizationally to kind of spread 

[00:13:23] Taylor Holiday: so we're, we're just, we're just talking on revenue right now. We have to get to the

cost side of the business, which is actually the more complicated part of this. So in our system, we were able to absorb unit level costs. Cause that's what we want. Skew level costs. We want to be able to actually create cogs based on what people actually order. So we're going to then absorb, hopefully you're doing a good job with your data integrity and Shopify in terms of every time you create a. Product, you would sign an existing cost to that SKU so that we have both the price that was sold out and the actual unit cost of the product. And I'd say brands are decent at that.

They're getting better. They're beginning to understand the importance of that for systems integration more broadly beyond just statless into lots of different systems that want that number to be right. Or we're going to upload a spreadsheet that you have of every unit cost. We're going to match it.

We're going to reconcile at the unit level. Then from there, there's going to be some adjustment related to shipping, fulfillment, payment, processor fees, et cetera, to get to the contribution margin outcome. And again, we're going to go through that same process of like, okay, do you have this sourced somewhere?

We're going to comp it against what we have. Do we get to that level of data accuracy to where we feel confident that every day we're able to produce a contribution margin number that aligns within a very small margin of error to what you're seeing as an organization, like that's really important because now, once we know that, now we can begin to think about the efficiency expectations of the media. Which is really the point, right, is that if we are going to help you grow contribution, we actually have to be able to see the number, trust the number and action against it. Because the difference, and this is really important because the difference of like 5 percent of being wrong or right about your marginal outcome has a huge effect on whether or not we're able to drive profit.

If we're, let's imagine we have a Ross that's two to one, and we think the marginal breakeven is 50%. And so we're like, all right, That's that's neutral. 50 percent is neutral. And then you find out that you're off by 5 percent on the margin side. Now you're creating negative contribution at every order, right?

You're deteriorating profit like a very aggressively. And that, that can be the, the Delta that's small margin of error is the Delta between the company hitting its financial objectives and not. And so it's really important to get clear here. Really important.

So, so yeah. So what we're, we're getting at though, right. Is that, okay. All of that work to get the information, right. Is the starting point. Think about that as the starting point to build a model, right? So

now, now we're going to go model out, spend an AMER, and we're going to be able to show you these different points on the curve at which you maximize. The year, the contribution margin, max time, maximize lifetime contribution, margin, maximize new customer revenue at breakeven. That model is only useful only if the information is right. If we're wrong about the margin, then we're going to build a curve. That's going to set you off in the wrong direction. And this is sort of like that classic, like. You know, like, sort of, if you're bearing, you know, 90 degrees West, and then all of a sudden you end up at 87 degrees, you can end up wildly off course for small marginal deltas. That's this business is that small inaccuracies in margin lead to really bad outcomes because in most cases, businesses drive their media down to the absolute marginal threshold. Like, that's just like the behavioral norm is that whatever that maximum expectation is, they're plowing dollars all the way up into that marginal threshold. If you're wrong about that threshold and you're out beyond it, you're in real trouble. And I see

that all the time.

[00:16:31] Richard Gaffin: yeah, that makes sense. So actually, so part of this feels like the definition of. So not only are everybody's definitions different at the beginning of this process, they're not on the same page. They don't fully understand what the goal is. Actually, part of what it sounds like you're saying is that not only that all of their core definitions or the core goals are actually wrong because what they don't have is a clear view on contribution margin, which is fundamentally the thing that you're shooting for.

Would you say that's true? Like there's not really a clear sense of marginal efficiency. They're just looking at that total revenue number.

[00:17:02] Taylor Holiday: Yeah, exactly. I think that that, that because it's like sort of the simplest thing to get to, it's the only thing that they feel like they can trust. But the problem is it's wholly insufficient for media management. Right? Because media management is all about return on investment, which is fundamentally a value expectation.

It's an expectation of the relationship between the cost of the revenue. And so you cannot do media by you cannot do it on revenue. Not in this era, not in this moment, right? And even, even in reality, even in an area where you're trying to grow top line revenue, there still has to be a consideration for the relationship between at least the cost of the media and the return on revenue that it's creating. So, and there still has to be some, when people are trying to grow top line, there's still generally some expectation of the payback period, right? It might be pushing it out further, it might be saying we're willing to take a one year payback on this media. But that idea is still a value calculation. It's still an expectation of margin. And in fact, it's even a more complicated calculation because now you have to predict margin in the future, which if you can't even get margin right today, I would really think it is extremely unlikely that you're going to accurately project it for 12 months.

[00:18:09] Richard Gaffin: Yeah, that makes sense. Okay. So let's, let's then kind of on a practical level, then beyond, of course signing up for one of our limited prophit system spots right now, commonthreatcode. com hit that high risk button. Let us know if you're a brand between eight and nine figures we'd love to talk to you, but let's say you're not in that boat.

What would you say is like the practical first step to getting on, on the same 

[00:18:30] Taylor Holiday: here?

here's, here's what I would do. This is, this is this step one. Right now, create, go to survey monkey, go to whatever survey tool you want. And I want you to think about the 10 most important leaders in your company. Okay. And you're going to send them this survey unsolicited. Don't prime them. Okay. Do this question.

One, what is your primary source of truth for revenue in this company? See what they all say to write a mathematical definition for how you believe that's calculated. Three, what do you believe the gross margin is on our revenue? Okay. Four, what is the contribution margin of our revenue? Five. What is the operating profit percentage of our company? See how varied your answers are amongst the 10 key leaders in your organization. Okay. And if they all come back with varying definitions that will illustrate to you just how big this problem is. And I would say the more varied the responses are, the bigger issue you've got, because those are the people making the decisions every day inside of your organization about how to drive towards the financial outcome you desire. And until all 10 of them. Say the same thing. And this is a very hard thing to do. You have to beat them over the head repeatedly. You have to cover it in meetings. You have to say it over and over and over and over and over again. And you're not allowed to change it because when you change it, then all of a sudden you create confusion.

So you have to stick with it for a long time. So this is like, that, that's how I would go out and first to assess for yourself. Do I have a problem? Do I have clarity around this key idea or not? And I think that, that to me, I would encourage everybody to do that. And maybe send me back the results.

If you have created 10 clarity here, like let's get you on the pod to tell

us, tell us how to do it. But if you haven't, I think we can help.

[00:20:18] Richard Gaffin: yeah, that's right. So if you want somebody, and here's, here's the reason to go to an agency like us, if you want somebody to do the beating over the head for you, Which a lot of the time, an outsider is a better person to do that. Just give us a call hit us up, comethreatcode. com, hire us and and we'll see what we can do for you.

Anything else you want to hit on this subject, Taylor?

[00:20:36] Taylor Holiday: This is really boring, tedious work,

it's not creative strategy, it's, but, but I, I wish I could sometimes words feel insufficient to me to communicate the imperative nature of these ideas to me that like, if you don't get this right, the whole thing exists in chaos, the whole thing. You can't even begin to talk about attribution before you solve this idea.

And yet I see the exact opposite, which is organizations spend 80 percent of the time talking about attribution. And meanwhile, nobody actually has any idea what the underlying gross margin of the businesses. Or it changes so frequently, or it's being reconciled backwards constantly. And so nobody's actually capable of making a good decision because the underlying architecture of the information is broken. This is the foundation in e commerce. This is the foundation, the unit economics and clarity of the unit economics of your business is the foundation of good decision making. Build that foundation first.

[00:21:31] Richard Gaffin: That's right. All right, folks. Appreciate listening. We'll talk to you again next week. Take care.