How We Forecast Millions in Revenue for DTC Brands

[00:00:00] Richard Gaffin: Hey everyone today on the pod, we're running a piece from last year called boost your profits created by our VP of e commerce strategy. Luke Austin in it. He gives a step by step walkthrough of CTCs profit system and gives you insight on the nitty gritty of constructing a holistic operating system for your brand.

Part of the reason we're running this now, it's the beginning of the year. And now is the time to set clear expectations for every important metric across the board. Every one of the next 365 days, or however many are left by the time you hear this. But the second reason is that we only have a few more profit system slots left.

So if you're interested, please let us know ASAP at common thread, co. com. One other note, if you're listening to the audio feed, we'd strongly suggest hopping over to our YouTube channel and watching the video version as well. Since there's a ton of visuals that help illustrate what he's talking about.

So without further ado, I'll turn it over to Luke.

If you haven't seen Taylor's video on the overview of the profit system, then start there. But if you have, then you're in the right place. I'm going to walk you through how a growth strategist interacts with the profit system on a day to day basis.

And it starts here. The output of the system, it's a daily email delivered to your inbox that shows pacing against each of the 35 plus critical business metrics, all the way from contribution margin to total revenue and ad spend. Down to the returning customer and new customer cohort performance and the metrics associated with each When you're greater than 10 percent off the expectation on any given day Not only you receive an email but you receive an update from your growth strategist on the account that highlights where Specifically or where off pace and what we're doing to course correct.

It's in a what so what now what format? It's an example of what one of those emails can look like where we can see So The what being our pacing month to date, the week over week trends being highlighted, and then the so what being the actions that we're taking on behalf of the brand based on the pacing that we're seeing in this particular instance, what we're seeing is underpacing against the efficiency expectations specifically for Google ROAS and coming out of a major marketing moment.

The team is making adjustments to increase efficiency expectations on paid search as well as efficiency expectations on Facebook in order to get the ACoS back in line with the target that we need to see to hit the contribution margin target. So it's very clear at this point where we're off pace and why and then what actions the team is taking as a result.

But the question is, how do we get here? How do we get to this level of output and expectation for each of these metrics? And where it really starts is step one, the integration of the data. So when we partner with brands, the first thing that the growth strategist works on is getting all the data sources integrated into statless so that we can see Shopify, Facebook, Google ads, Google analytics, Klaviyo, TikTok, Mighty Scout.

We have a lot of other integrations as well, but any integration on Marketing channels specifically related to that store will bring into StatList as step one. Step two is getting clear on the cost data. So we have cost data by SKU, not just the product cost, but then the cost of delivery and any variable costs associated with getting that product to someone's door.

And that's integrated into StatList as well. With the goal of building out a historical. PNL level view of the brand's e commerce PNL. Ultimately what we're doing here is integrating the data and stat list and bringing in the cost data to build out a real real time PNL that matches the brand's internal PNL for how much profit Uh, that they can expect in any given month.

So we build the P& L out to match what the brand can expect. Every single line item from the revenue definitions to cost of delivery line items, ad spend, any fixed costs in terms of operating expenses down to EBITDA. Once we get to that point. Now, we have the data that matches all in a PNL level for the brand and all the sources connected in statless.

And we can build the custom models to generate the expectation for the business performance. The first of those are the spend and the AMER model. And so, taking historical data, we'll develop this regression model that takes into account all of that. Seasonality, discount percentage, marketing calendar moments, and more factors to give us an expectation for any given month that we're forecasting against what is the optimal spend and AMR level based on the optimization that we're going after, whether it be maximizing first order contribution margin.

Or maximizing first order revenue volume at break even contribution margin. So the spend AMER is developed from the data integration and that predicts new customer revenue. And then we forecast out returning customer revenue through the retention regression model. This is an example of what the methodology and notebook for a one particular brand's retention regression model looks like.

That is a linear regression model that takes into account seasonality. And time, time decay for returning customer cohorts, ultimately with the goal of getting us to a really high confidence level of output in terms of the predictability of our returning customer cohorts. What these models then allow us to do is to start projecting out cohort specific inputs for each specific month.

In the future that we're forecasting against so we use a cohort level forecasting model that takes into account the spend AMER and retention models to get us to an expectation of the revenue contribution margin outcomes. For this specific month we're projecting for. So the example of that, where we have the returning customer model feeding into our cohort input buckets, we have the spend and AMR model informing the spend level and allocations per channel, and then what the total new customer revenue and total returning customer revenue expectations are for those given time periods.

And backing into our channel specific efficiency and spend targets as well. So now we have an expectation for at the monthly level. What is the ad spend? What are the efficiency targets per channel that are going to ladder up to the total new customer total returning customer revenue expectations? For that time period.

We then take that monthly level expectation and within the context of the marketing calendar and the marketing moments plan for that coming month, start to break it down into daily expectations for how we're going to get to that monthly total. So the marketing calendar is integrated within the growth map as well.

And then we start to look at day of week modeling for each specific brand to understand. How different days of the week perform on average from an ad spin level from a revenue pacing level and take that into account to ultimately back into daily targets for each of those 35 critical metrics that live within the statless dashboard.

And we can see every day of every single month for every single metric. What is the expectation? Are we ahead? Are we behind? Are we green? Are we red? And in a really rapid way, be able to understand why we're ahead, why we have to be behind, and what we can do to course correct. And that's how we get to the email being delivered in your inbox every day showing pacing versus target and then highlighting the actions that our team is taking to course correct and get back on track.

That's our growth strategist interacts with the profit system to drive contribution margin growth for your brand.