How to Optimize Ad Spend in Real Time During Black Friday

[00:00:00] Richard Gaffin: Hey folks. Welcome to the E-Commerce Playbook podcast. I'm your host, Richard Gaffin, director of Digital Product Strategy here at Common Thread Collective, and I'm joined today by our VP of e-commerce strategy and sometime war reporter. He's out there on the front lines in the e-com trenches reporting for us.

[00:00:15] Richard Gaffin: It's Mr. Luke Austin, also my co-host on the DDC hotline. Check it out. But Luke, what? What's going on today, man?

[00:00:22] Luke Austin: It is, we're, we're in November, it's

[00:00:24] Richard Gaffin: Just, yeah, exactly.

[00:00:25] Luke Austin: and with that, I think the, the early bird sales, um, have some of them been launched, yeah's sort of in the, in the midst of doing that. And then Black Friday, cyber Monday is right around the corner. So there's this there's this anxious anticipation that hangs in the air for

[00:00:41] Richard Gaffin: That's right.

[00:00:42] Luke Austin: D two C.

[00:00:43] Luke Austin: Operators, marketers and teams that we are very much in, in the in, in that state with, with you all as well. But excitement. Excitement to push forward the next few weeks and see what it has in store.

[00:00:55] Richard Gaffin: That's right, exactly. It is that time of year and that's what we're talking about today. Of course. What else [00:01:00] is on anyone's mind? And, and the thing that we're gonna focus on today is around the concept of hourly tracking over Black Friday Cyber Monday weekend. And what we're gonna have Luke do is kind of make the case for why hourly tracking is so crucial.

[00:01:14] Richard Gaffin: In this obviously crucial time. Um, but I should preface this by saying you know, and after you Luke talks through this it's gonna make a little bit more sense here, but by the time you're hearing this, this, this is gonna be the 4th of November, and for the following week until the 10th, we are offering a free.

[00:01:33] Richard Gaffin: Black Friday, cyber Monday hourly tracking report that comes with the purchase of a profit system, which again, to remind those of you who have forgotten, it's our spend and forecasting model will model out. 12 months of revenue for you, give you a sense of what daily targets should be across all of your different key platforms.

[00:01:50] Richard Gaffin: But not only that, in in this upcoming week, we're also gonna be offering this hourly BFCM tracking report. So with that out of the way, let's actually jump into what this is and, and I'll throw it over to [00:02:00] you, Luke, to make the kind of pitch to us about why hourly tracking is so important.

[00:02:05] Luke Austin: Yeah, so I, I thought we'd do this through the lens of a couple examples of brands last year, black Friday and Cyber Monday weekend that we worked with to be able to execute on hitting, hitting forecast and using the hourly targets to do that. So we're, I'm gonna, I'm gonna pull up two specific brands that we'll walk through, um, and we'll have the conversation through that lens of. Why it is crucial to not just have hourly targets in place for these big days, but then how do you actually operationalize against hourly targets? What, what is it once you have the hourly targets, what does it enable you to do? Right? What is the behavior that it, um, that that it helps to instill and, um, create within your, within your team? Um, and so, I think we'll start with, we'll start with the first brand here and each of these brands. Um, the lens that we're looking at this with was each of these brands was had a high risk of missing either their Black Friday or Cyber Monday. Target brand one [00:03:00] was pacing on Black Friday, well, behind what their revenue forecast was. And then Brand two, cyber Monday was was at risk based on the pacing, um, for that day. And both of them, because of the transparency of the hourly targets and what it allowed us to see, we're able to course correct really meaningfully to help bridge that gap. Um, and so I think this will sort of come, come to light and hopefully be helpful to folks in terms of how we think about operationalizing hourly targets in their workflow.

[00:03:26] Luke Austin: Um, and to the, the offer we're running right now, we wanna be able to create the, the forecast out through our profit system and hourly targets for as many brands as possible. Um, so it could be something that we partner with you and to be able to, to be able to help make, make reality.

[00:03:39] Richard Gaffin: Cool. All right, well let's jump into it. So you'd mentioned that the, this first brand that we're gonna look at here is a brand that would've missed their Black Friday target, the pacing behind early in the day, presumably. And be, but because we had an hourly hourly pacing bill for them, or rather an hourly tracking report, we were able to correct that before it was too late.

[00:03:56] Richard Gaffin: So let's jump into that right now.

[00:03:58] Luke Austin: Yeah. Yeah, that's, that's [00:04:00] exactly right. So this, this first brand I'll, um, and for those watching we'll have screen share pulled up for those listening along will describe what we're talking through here. But for this first brand. What we have up here is on Black Friday we landed minus 1% to discounted sales target for the day. Um, an ad spin landed plus 16% to target. So from first glance this could be read as, okay, we hit the revenue target slightly under 1%, whatever, but we had to spend 16% more to get there. Um, from an efficiency standpoint, from a profit standpoint. that's not a great outcome. It might might be the first read of, of this, which this day in isolation, could be read as that.

[00:04:47] Luke Austin: But we're gonna, I'm gonna zoom out to what was happening the days leading up to this. 'cause it's really important to frame the execution over Black Friday and where it landed. Before we talk through the specific example, so I wanna talk through, um, hourly [00:05:00] tracking and how we approach this in terms of the methodology and, and the specifics of, of these numbers.

[00:05:05] Luke Austin: So we're not gonna talk through much of the forecasting process that goes into our profit system. We have many conversations and episodes around that of how we use the spending power model, the retention model, the event effect model, the marketing calendar. Ad spend, incrementality and measurement to build out a p and l level forecast for every single month of the year, and then break that down into daily targets, um, that then have day of week effect overlaid with the marketing calendar to help get us to really high level of granularity in our daily targets, which allows us to be really close to landing forecast across our dataset.

[00:05:36] Luke Austin: Were less than 2% on, in, in, on either side of, of missing forecast. So 98% are better to goal, um, over the course of this year. Um, as it relates to hourly tar targets, though, we take that same process, each of those steps, and once we have the daily targets, the main way that we're looking at hourly pacing is we are overlaying the target for this specific day [00:06:00] the hourly pacing of the year prior for that same day, right?

[00:06:05] Luke Austin: So we have a forecast set for this year. And that is based on all the factors and the process that I mentioned, which is gonna be a target that's different than the, the target for that same day of last year. It's gonna be higher or lower than last year for that specific day. then in terms of the hourly pacing, we look at every single hour of the day, the composition of that hour of revenue spend, um, efficiency for each of the hours by day.

[00:06:29] Luke Austin: And then we take the target for this year and we look at the overlay of that against the same pacing as last year because what it allows us to do. Is be able to see the, the number we're after this year against a similar behavioral pacing of of last year. That's helpful for a number of reasons, which we'll talk through here.

[00:06:46] Luke Austin: But in terms of the visual we have up and what we'll be talking through, that's what we're looking at is we have the forecast set for this day of this year, and then we're decomposing that on an hourly basis based on the last year's hourly pacing for that specific [00:07:00] day to then see. pacing ahead or behind in which specific metric and be able to make the adjustments accordingly.

[00:07:07] Luke Austin: So that's what we have an hourly tracker here. We call it compare target to last year by hour. So target of this year by the hourly pacing of last year is a lens at which we're looking through this was, which helps us to get a better understanding of our pacing throughout the day.

[00:07:23] Richard Gaffin: Yeah. And just to clarify, for those who are listening, the, the kind of top chart that we're seeing here, if I'm understanding this correctly, is, so what you have is basically Black Friday is Black Friday of 2024. Here is pulled up and, and it's broken down by performance by hour. And then there's a chart above it that shows the sort of hourly performance overlaid against 2020 three's Black Friday performance I would presume.

[00:07:46] Richard Gaffin: Right. And what you can see is that there's a very strong correlation between those two lines. I mean, there's, there's some discrepancies here and there, um, but overall, the sort of these two lines. What actually happened on 2024 [00:08:00] and then what happened in 2023 in terms of the buying behavior over the course of the day follows roughly the same curve.

[00:08:05] Richard Gaffin: Um, which just goes to sort of reinforce that idea that looking at last year's behavior is super beneficial for thinking about what hourly pacing should look like.

[00:08:14] Luke Austin: Yes. Um, that, that's exactly right. And there's, so there's two, there's two important things for us to chat about as it relates to this, this approach, which is. Um, I think it's clear to most of us that if your marketing calendar, your promo schedule, your email, send cadence. If that's similar to the year prior, then this approach should yield very similar correlation in terms of your hourly, um, tracking, right?

[00:08:43] Luke Austin: Like you should expect we were running the same business, very similar consumer customer subset or potential customer subset and the behavior associated with them. And we have a similar offer going out and email sync cadence, et cetera. Our hourly pacing should look really similar. So this approach makes a lot of sense, [00:09:00] right?

[00:09:00] Luke Austin: Like, and for many brands that, that, um, that will be the case. And then the, the second response to this might be well, how does this help us if we're doing things different this year? Right? So we're looking at the hourly pacing relative to last year's hourly pacing. makes sense if all things are held consistent.

[00:09:17] Luke Austin: But how, how does this help us if we are doing something different than we did last year? And the answer to that question is it helps you to see how you're doing compared to last year. It it helps you to know if the things that you have changed in your marketing calendar, your email cadence, et cetera, if those are in improving or not improving relative to what you did the year prior and not just. And prove yes worse yes, but like what is the, what is the magnitude to what the impact of these things, right? So helps us understand how are we doing relative to the activities we did the year prior, and then what's this, what, how do, how are we quantifying that as well? To what, what extent are those, are those changing?

[00:09:56] Luke Austin: And so I think it's important that we all [00:10:00] understand that having a frame, a frame of reference in order to assess. The performance and the impact of these decisions is, is, is really important. Um, and, and this, this brand that we're looking at, I think is a good example of that. So, as I mentioned on Black Friday, we landed minus 1% to, um, discounted sales target.

[00:10:19] Luke Austin: And, and we landed 16% ahead of ad spend, right? So it'd spend more to get to right at our forecast goal for that day. Um, but what's important is we're gonna zoom out here to the day prior to Thursday, and on Thursday this brand does a VIP early access launch, like many of us do to its existing customer set through email and SMS channels.

[00:10:45] Luke Austin: It's really high percentage returning revenue day. Um, and, and then the sale goes live early access couple, couple hours later. But Thursday is the first day of the sale tour, the back half of the day. And what we have pulled up here on the hourly tracker is that [00:11:00] Thursday landed 65% ahead of sales target. Um, and we landed 20% ahead of, of, of spend. So Thursday meaningfully outperformed what the expectation was by 65%. In this case, that's a little over a hundred thousand dollars relative to what the expectation was. And then we were able to spend more volume than we expected as well based on that pacing. So 20, 20% ahead. Um, and the reason for this is you can, what we can see in the hourly pacing here is last year the sale launched at at 4:00 PM Pacific. And so that's the first sort of hourly spike we see in terms of their revenue pacing is 4:00 PM per Pacific spike. And then an SMS was sent out two hours later. So it was early access email, then wait two hours.

[00:11:47] Luke Austin: Then the SMS that went out, um. local time for the remaining, you know, subsequent few hours of the day. And so you sort of see like two spikes happening through the course of that day as those communications were sent at different times. And the cadence was, [00:12:00] and the cadence was different this year, or sorry, 2024, what we did, right?

[00:12:04] Luke Austin: So last year, relative to the year prior, um, we went out at an earlier time. We actually did an early access VIP through SMS prior and did that 2:00 PM so two hours earlier in the day. Um, and then we did the full sort of maximum list available on email, email blast two hours later. And then that did local send time throughout the tapered off throughout the rest of the day. and so you can see in the hourly pacing is we started ramping up revenue earlier than we did the year prior, right, because we did the, the SMS send earlier and then we did the email send, um, and it started taper off through the day. And that led us to beating the revenue target substantially. Um, what this allowed us to do, again, I think through the lens of the, the, this year prior, um, was very different in terms of the marketing calendar cadence than the, than the year before.

[00:12:56] Luke Austin: And so, um, what was helpful in this case is to [00:13:00] understand what the impact of that different set of activities was. Right? We had a hypothesis around sending the SMS first, sending out earlier, doing the email, sending this cadence. But we need to quantify what the actual impact was. And in this case, it led to a revenue day that was 65% higher than the year before.

[00:13:16] Luke Austin: Um, as it allowed us to, to drive that volume through the day. And then the subsequent action that that led us to be able to. Engage in is increasing the ad spend investment over the course of that day. So we've toggled in the chart here from looking at the sales by hour to the spend by hour. We're able to ramp up ad spend earlier in the day because the, the offer went out earlier, which allowed us to drive more volume as well.

[00:13:38] Luke Austin: And then we kept sort of the same hourly pacing for the remainder of the day. So we got like an extra two hours of spend ramp up, which for a shorter day like this, when we're doing an afternoon VIP early access launch was, was really valuable as well. So. Thursday, the day prior meaningfully beat the revenue expectation that allowed us to spend 20% more than we had [00:14:00] planned for that day.

[00:14:01] Luke Austin: But the end of all of this, we actually underspent the opportunity on Thursday. We left volume on the table relative to what was available to us by only spending 20% more against 65%. Higher revenue target. Our new revenue landed 26% ahead of goal. So we had more room to be able to push ad spend volume over the course of this day, um, because it was even stronger than we anticipated it would be.

[00:14:25] Richard Gaffin: Yeah. Okay, so then let's talk about how that affected, like what happened on Black Friday of 2024.

[00:14:31] Luke Austin: exactly. So this was the outcome of Thursday. And so we go into Friday looking at. We, we beat expectation meaningfully. This was a, the, the adjustments to the VIP launch on Thursday were even more impactful than we thought. We were able to push more volume, but we left volume on the table. So when you look at Friday in isolation, again, we landed minus 1% to revenue goal for the day, but spend landed 16% ahead, so. What we have on the chart here is we can see our revenue [00:15:00] pacing, um, over the course of of the day was, was pretty strong relative to the year prior. But when we toggle over to ad spend. What we ended up doing on Friday is spending much more aggressively the first half of the day and into the early afternoon relative to what the pacing was the year prior.

[00:15:19] Luke Austin: And so we have the hourly spend target breakdown chart here up, and what you can see is our hourly pacing up up to about the 4:00 AM hour was, was really commiserate with the year prior, but then the subsequent like six hours, seven hours. We outspent each of the hour pacing. We kept a higher ad spend over the course of those hours, and in the evening it sort of tapered off and kept more consistent with, um, with the remainder of the day.

[00:15:47] Luke Austin: Now, why did we do this? The main reason was because we left volume on the table on Thursday. Right. We understand. We actually realized too much of the profit expectation relative to. the [00:16:00] revenue on that day, which allowed us to have the dollars available on Friday to be able to spend more to make sure we arrived at the revenue target and didn't leave that on the table relative to Thursday.

[00:16:10] Luke Austin: And so you can see the sort of like net impact of this is if we look at Thursday and Friday in aggregate, which we have pulled up here, we landed 14% ahead of revenue target for those days on aggregate. And 16% ahead of spend target. So really dang close to the expectation of total ad spend, ad spend volume against the revenue expectation for those two days in aggregate.

[00:16:34] Luke Austin: But that came after Thursday being a much more meaningful, meaningful beat and us leaving volume on the table and then us taking those additional dollars and deploying them on Friday, overspending against the expectation to make sure the aggregate landed at the expected goal. If we didn't have the transparency into how. Much of an impact. Those different actions on Thursday made and then what the hourly pacing was relative to the [00:17:00] target. And then Friday we had just allowed to spend two goal, right? Like allowed it to spend 0% to forecast, like write a goal. Instead of spending 16% to forecast, our revenue would've landed 10, 12% behind for a Black Friday, right?

[00:17:12] Luke Austin: We would've just left the, continued to leave the volume on the table, and the total for those days would've been below expect expectation relative to the revenue outcome.

[00:17:21] Richard Gaffin: Yeah. Okay. So to clarify then the, our ability to track hour by hour, at least on Thursday. Revealed to us the fact that there was an opportunity here that we were potentially missing if we had just sort of continued business as usual. So because we had that insight, we were able to on Friday, then make some pretty make some pretty major adjustments to the way that we were approaching Black Friday, um, in order to realize, you know, the potential revenue.

[00:17:49] Richard Gaffin: Right. So talk to me about like how, um. How the actual like hour, like understanding of hour by hour performance on Friday impact [00:18:00] decision making.

[00:18:01] Luke Austin: Yeah, so on on Friday, we'll pull back up the hourly, hourly pacing here. Um. What we can see is that starting about the 4:00 AM hour, the four five and 6:00 AM hour, our revenue outpaced the expectation relative to what the hourly targets were. So we, we were doing, we were doing better the four, five, and 6:00 AM hours. relative to where we thought we would be. But then what we started to see is the seven, 8:00 AM hours tapered off and started to hit the same level as the year prior. And then you can see the nine and 10 hours then started to be below the hourly pacing prior, right? So like the morning hours, like, oh, we're, we're, we're actually potentially pacing ahead here.

[00:18:43] Luke Austin: Then it starts to get back, plateaus back to the pacing last year. Okay, this is commensurate, and then it starts to dip down. And the indication there would be, okay, we might, we may have pulled some of the, more, more of the revenue forward, right? By doing the earlier launch and the email SMS communication. so these [00:19:00] hours are gonna be softer. And so we started to see the softness of the revenue during those sort of mid-morning hours there in terms of the revenue. And so you have a decision to make at that point, right? Okay. We lean in the morning, we saw a strong outcome. We're starting to see slower revenue pacing, um, over the course of these hours.

[00:19:16] Luke Austin: What should we do as a result? Should we pull back our ad, spend pacing, commiserate with that, right? Like the 10:00 AM hour. Landed 22% behind the revenue goal based on the hourly targets for the 10:00 AM hour on Black Friday. So do we start to pull back our ad spends that the, the day in aggregate or the remaining hours of the day, we pace, you know, 10, 15% lower than the, than the hourly expectations. Um, do we hold things consistent hoping that our revenue's gonna pick up? Um, or do we actually double down and, and try to make up some of the revenue, some of the revenue gap? So that's what the hourly, the hourly targets in this case. Surface that that specifically in terms of here's, here's what, um, here's the decision that we have to make relative to the ad span that we're gonna be allocating [00:20:00] over the re the remainder of the day. the context of Thursday, um, if we had on Thursday, let's say, landed 20% ahead of revenue target and 20% ahead of spend, right? So we had, we had sort of matched up to the revenue expectation. Then the deci, the decision on Friday at that 10:00 AM hour, again, likely would've been let's keep our spend consistent with the daily targets.

[00:20:26] Luke Austin: It's not over index, it's not pull back too soon. Right? 'cause we know the sun sour Monday evening revenue peak, but let's keep things consistent. Um, because Thursday we sort of like spent right up to the goal of where the revenue landed Friday. We're like right, right in the mix there. So we don't wanna overextend, um.

[00:20:42] Luke Austin: Um, we don't wanna pull back too aggressively. That wasn't the case though. 'cause Thursday was 68% ahead of the, the revenue target. So we had, we had underspent the, um, what we, what we could have over the course of that day. And so then the conversation on Friday becomes, okay, this is where we're at.

[00:20:59] Luke Austin: We're seeing softness in [00:21:00] some of these revenue. have a lot of additional revenue banked from Thursday relative to the expectation. And we know, um, later this day we're gonna see that increase. And so what we're going to do is actually, um, double down on the ad spend over the, over the remaining And so what I switched to here, um, for those listening along, is the hourly pacing of ad spend over those remaining hours. And you can see at 10, at those 9:10 AM hours when their revenue started to soften. Our ads spend actually started to pick up. We started to push more ads, spend volume over the course of those hours to help sort of push against that curve, um, over the remainder of the day so that we made sure to stay at the revenue expectation. that's what we continue to track over the remainder of the day, is how our revenue is Pac. Um, against Target, and it was slower for a few of those hours. And then as a result of us leaning more into the ad spend, right over these hours, the evening hours picked up and, and ended up pacing in line with what the rev revenue expectation was because we, we didn't let off the gas in a [00:22:00] similar way.

[00:22:00] Luke Austin: So the context of where we landed Thursday, beating the expectation actually under underspending by some extent. Um, that, and then on Friday, being able to see how the revenue pacing by hour where that was landing then allowed us to make, make the decision to let's actually increase our ad spend volume against it to make sure that Friday lands really close to the goal.

[00:22:23] Richard Gaffin: Gotcha. Okay, so let's, um, let's then jump into our next example unless there's something else you wanna hit here. And so for context, this is a. This is on Cyber Monday,

[00:22:32] Luke Austin: Yep.

[00:22:32] Richard Gaffin: a similar kind of situation where there was a sense in which like if we had continued business as usual, they would've missed a crucial target on Cyber Monday.

[00:22:40] Richard Gaffin: But because of our hourly pacing, we were able to understand how we would need to adjust to get to where we needed to go. So let's let's jump into this second brand here.

[00:22:48] Luke Austin: Yeah, so, this is a sort of example, but on a very different day. Of Cyber Monday where the hourly pacing on Cyber Monday your evening hours, is gonna be the strongest on Black Friday. Typically, it's gonna start [00:23:00] tailing off those evening hours after the first half of the day into the early, early afternoon. Um, so each of the day, each of these days, the shape of revenue is really, really important to, to understand. Um, but what we can see here is starting pretty early on in the day, um, the 7:00 AM hour. Um, on forward, really through noon, really through midday was under pacing the expectation meaningfully.

[00:23:27] Luke Austin: Some of some of these hours some of these hours were missing expectation by. By 40%, 30%, um, to, to goal for, for a number of subsequent hours there, which you play that out. And Cyber Monday lands 30 40% behind forecast, right? Well, where Cyber Monday actually landed for this business was minus 15% behind forecast in terms of the, the revenue outcome. Um, we won't have time to sort of like put that in the context of where the other days were leading up to it. Um, but it's safe to say this day was pacing to [00:24:00] being really far off the target. And although it landed lower than forecast, it was it, it is a much much better outcome than it was on pace to be over the course of those, um, initial few hours that were, that were pacing meaningfully off target.

[00:24:13] Luke Austin: And so when we. See the, the revenue pacing by hour for those early hours of the day. Um, we can look at the ad spend target by hour, and what we see is a similar story, which is we're underspending the expectation meaningfully, right? And the, the year prior, what we can see in the hourly targets is the ad spend, um, continue to climb up really through like the 9:00 AM hour, like 6, 7, 8, 9:00 AM Just continued to stack higher ad spend. Um, and then it sort of hit a plateau and then increased into the evening hours for the rest of the day. Well, for this day we were underspending those morning hours pretty substantially. Um, and we weren't really building up, um, building them up the same way the year prior had. And [00:25:00] so, um, that it's a pretty clear story to say here, our revenues pacing meaningfully behind target, our ad spends pacing meaningfully behind target.

[00:25:06] Luke Austin: We, we need to make some substantial changes, or we're gonna land 30, 40% behind the expectation for, for this day out of the weekend. And so that's what you can see is starting around the noon, the noon hour, um, span, starts to increase more and then builds up 1, 2, 3, 4:00 PM PST. And each of those hours outpace the span of. The prior year really substantially. Um, and if we sort of cross that with what the revenue looked like is starting around the noon. One hour is when we started to comp to, um, the revenue by hour of the year prior and the the 2:00 PM PST actually beat, um, the year prior, after again, those morning hours were, were, um, pacing, meaning meaningfully off.

[00:25:49] Luke Austin: And then the evening hours paced a lot, a lot closer to the expectation for. The day prior. Um, and in this case, I think it's the, the important [00:26:00] thing to illustrate is how aggressive these early afternoon hours were in terms of the ad spend. They were substantially higher than the original target for those days.

[00:26:12] Luke Austin: And that was a result of one, having transparency to how much the morning hours were pacing behind. So knowing how much we had to the specific amount of ad spend, we had to go and make up. Plus understanding the evening hours are the biggest, um, on Cyber Monday. And so we really need to get, ramp up that volume prior to those evening hours to give us a chance at comping, um, at those levels.

[00:26:34] Luke Austin: Um, which is what, um, we ended up, we ended up doing to bridge the gap from, um, earlier in the day.

[00:26:41] Richard Gaffin: So again, like the clear lesson here being that, let's say that we didn't have sort of hourly transparency into what was happening on Cyber Monday. Like you're saying, that line that where you can see it being 40% below the previous year's performance, that kind of gap would've just persisted throughout the day and they would've ended up meeting fully behind.

[00:26:58] Richard Gaffin: So, um, [00:27:00] so essentially like what happened then is, is like you saw, um, that in order to replicate the. Success or replicate the performance of the previous year or even beat it, there would have to be some sort of meaningful injection of ad spend over and against what you were originally planning to do.

[00:27:17] Richard Gaffin: Is that correct?

[00:27:18] Luke Austin: Yes.

[00:27:18] Richard Gaffin: you had originally been planning to spend a lot less during that hour, but because we had some understanding of the way that the forecast or the plan would need to be broken in order to get the result that we wanted, we were able to meaningfully overspend where we had thought we were gonna be at that hour or something along those lines.

[00:27:35] Richard Gaffin: Yeah.

[00:27:36] Luke Austin: Yeah. Yeah, that, that's exactly right. And I think it sort of goes back to the framing of like, okay, what's, what's the impact of having the hourly targets? in relation to the breakdown by hour compared, compared to the year prior is like one we can see where we're ahead or behind pace. Um, that's, that's the easier thing to get at, get at.

[00:27:55] Luke Austin: But quantifying the extent to which we're at, or we're above or [00:28:00] behind pace for each of these metrics. Sales spend, new customer revenue. Cac, Facebook spend, Facebook, whereas Google spend Google whereas, so quantifying the exact amount that we're behind, um. And then knowing what that means for the rest of the day to be able to stay on on track, um, is, is the most important thing.

[00:28:17] Luke Austin: Okay, we're pacing behind the hour. Are we pacing? Are we pacing behind, you know, $2,000 in spend for each of these hours or $20,000 in spend? What does that mean in terms of how aggressive our actions need to be in the afternoon to give us a shot at being able to comp, um, in, in this specific example. So yeah, highlighting for every single metric, every single hour. Are we ahead or behind? And then specifically by how much that that's what helps to inform the decisions around how we're going to adjust to make sure that trend doesn't persist or that it improves.

[00:28:49] Richard Gaffin: All right. All right. Um, anything else that you wanna hit, hit on these, Luke?

[00:28:55] Luke Austin: I don't think so. Yeah, I don't think so. Dunno what we'd do with [00:29:00] without hourly trackers. Um, and we wanna build some for you all. Um, so. us know

[00:29:06] Richard Gaffin: That's right. Yeah. Yeah. So if, yeah, if you do want us to build a free Black Friday, cyber Monday hourly tracking report with the purchase of a profit system that's going on right now. That offer's going on right now for the next week or so. Um. And this is o by the way, open to, unlike some of our other things this is open to seven figure brands as well.

[00:29:24] Richard Gaffin: So if you're anywhere from seven to nine figures, this is absolutely going to change the way that you approach Black Friday, cyber Monday. So obviously I highly recommend it. I think one thing that worth pointing out too is that like the, the hourly shape of Black Friday and Cyber Monday is very specific in a way that like the hourly sort of.

[00:29:42] Richard Gaffin: Customer behavior is not on other days. So there's like, because there's such like clear hourly changes, it's really, really important to understand how you're performing relative to those on an hourly basis as opposed to a daily basis. Um, so anyway, if that's something that you want and I think you do, you know where to [00:30:00] find us, comment thread code.com, hit that high us button, let us know that you're interested.

[00:30:03] Richard Gaffin: We would love to chat. Alright, I think that's gonna do it for us. For Luke, the weatherman, Austin, which is of course his name on D two C hotline or other podcast. Great talking to y'all and we will see you all next time. Bye.