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Get a Demo“We used to try to win every day. Now we’re willing to sacrifice entire months.” What does it mean to optimize ad spend when there’s no universal answer? Michael Ting (GM of DTC, JAXXON) joins Connor Rolain (Head of Growth, HexClad) and Connor MacDonald (CMO, Ridge) to challenge one of the most common assumptions in ecommerce email marketing and paid media: that there is a single optimized ad spend. There isn’t. The better question is what you’re trying to achieve and over what time horizon. Michael walks through how JAXXON uses cost per email signup to make confident budget decisions, spending aggressively into slow seasons and monetizing that list when it matters most. The conversation also covers decision quality frameworks, doubling AB testing velocity, and how daily performance reporting builds operator instinct over time. Powered By Motion Creative Benchmarks 2026 https://motionapp.com/thumbstop-pulse/creative-benchmarks-2026?utm_campaign=marketing-operators&utm_medium=sponsor&utm_content=creative-benchmarks-2026&utm_source=marketing-operators-podcast Richpanel https://9ops.co/richpanel Aftersell https://9ops.co/aftersell-mops Haus https://www.haus.io/operators Operators Newsletter https://9operators.com/ 00:00:00 David Protein Ice Cream 00:02:52 Michael Discovers Huel 00:07:59 Michael’s Origin Story 00:15:30 The Nine Figure Gap 00:21:45 No Such Thing as Optimized 00:28:10 Forecasting as a Tool 00:33:05 Cost Per Email North Star 00:40:42 The Chicken Gut Check 00:49:15 Test Velocity Culture 00:58:40 Good Decisions Bad Outcomes 01:04:38 Decision vs Execution 01:09:07 Lower Confidence Move Faster
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Welcome to episode 117 of Marketing Operators. We've got a very special episode for you with Michael Ting. Michael is the GM of DTC at JAXXON, which is a men's jewelry brand pushing 9 figures in revenue. Today we get into a lot. We get into why he believes there's no such thing as optimized ad spend, how he thinks about balancing today's contribution margin with long-term growth, how he uses cost per email as a leading indicator of today and future revenue. So much more what he's focused on going into 2026. This is a very tactical, very actionable episode. Michael's super in the weeds, very data-driven, and is just a really sharp DTC operator. So really hope you enjoy the episode today. Thank you to the sponsors, Motion Creative Benchmarks 2026, Richpanel, Aftersell, and Haus. Let's get into it. All right, we're back with another episode of the Marketing Operators Podcast. We have Michael Ting on the show today. Michael, welcome to the Marketing Operators.
Yeah, I was super excited. I'll mention it later, but really had to learn everything, not exaggerating, everything from this show. So it's crazy to be on it finally.
Nice. Excited to, excited to dig in and kind of figure out what was the most and the least valuable. Where are you calling in from today?
I'm in Los Angeles, Westwood, right next to UCLA. I commute down to Newport. So if you're from Los Angeles, you know what that's like.
Yeah, I don't, I don't envy the HexClad, like half remote, half in-person, and I do not envy some of the trips that some of those folks are making every single day from like Orange County and like Malibu to the office in downtown LA and back every day. It's crazy.
Yeah, but like that's when I listen to The Operator podcast, right? I got 90 minutes a day to kill. Just put it on 2x speed.
So it's baked into the morning and the evening commute. All right, that's great. So Michael, I mean, you, you probably know this. We like to start every episode by talking about our, our new favorite protein-infused products. And, and David Protein is one that comes up a lot. They just launched their, their David Protein ice cream sold out in 28 minutes. I was, I was told that's what I saw from their founder, Peter. And I wanted to know, Connor, did you get your hands on any?
No, dude. And it's so funny because I'm like, not much of a consumer. I'm like not buying the trendy stuff. I'm not, you know, I'm never buying anything. And this is the first time I was like, damn it, I really want this ice cream. And I was like, I looked on Amazon, I looked on the site. I'm like, are all the flavors sold out? I'm like, this doesn't seem right. I was like, should I text Connor? Connor, you met the like VP of performance marketing at David. I'm like, should I text Connor to ask his friend if I could get the ice cream? I was like really going through it, but no. I haven't tracked it down. Hopefully soon. I don't know. I was like, maybe it'll be in grocery stores soon. I don't know. But I'm scheming.
Yeah, I texted, I Slacked Keegan. Or well, hold on, Michael, did you get your hands on any?
I haven't, but have you guys ever tried Huel? I think most underrated D2C brand out there. H-U-E-L. They literally exited the week after Groons and, you know, everyone's social media was all Groons. I think all of ours, Twitter, everything. They exited for 1.2, 1.6 billion, pretty much the same size the week after. But no one in this space has heard of them. They're UK-based. They do like plant-based proteins, but they also make mac and cheese with like a ton of protein. Literally used to live off of it when I was like really grinding at JAXXON and just didn't really have time to take care of myself. Like breakfast, lunch, and dinner, I would just like get this mac and cheese and honestly, it was the best shape I was ever in my life.
Did you, did you ever go through a Soylent phase?
Never a Soylent phase, honestly. Um, I'm not sure how I feel about the plant-based approach, but I love the mac and cheese approach personally.
Yeah, yeah, yeah. No, it's interesting. We're pivoting from David a little bit, but Huel is cool because they like, they were like a fast follow to Soylent, like 2015, or maybe it was like 2017 or something, like way back when Soylent was like a hot D2C brand. I basically got into D2C because of Soylent. I thought it was like the coolest brand. It was like this weird, like dystopian branding. Um, I'd met the founder at a conference back in 2016.
It was really cool.
And then Huel fast followed, and then Soylent obviously like didn't pan out. They raised too much money. They got sold like for parts basically. And then Huel has a billion-dollar exit. So it just shows they pivoted away from like the dystopian all-you-need-in-a-drink form to like normal things like mac and cheese that people actually want to consume.
That's crazy. I never knew the history. I was just a huge fan of the brand, probably top 1% customer. And was always just shocked that like no one's talking about it because I think they executed really well with a interesting product.
They're going, it, it's, it's interesting. Huel's going more of the, uh, like a lot of these RTD protein drinks are like, I like Raw, that's, that's my go-to. And they're, a lot of them are in that like 170 calorie, 20 grams of protein range. Huel's going, they're like, no, this is like a full-on meal replacement, like 400 calories. Like you're gonna, you're gonna be full after this. David, to bring it back to David, I have never seen, I mean, this is obviously like, this is their trend. They're all about like the maximal maximum calorie to protein ratio you can get. Their vanilla bean full pint of ice cream is 210 calories and 30 grams of protein.
It's just sick.
It's, it's, it's, it's, it's insanity. It's insanity. You know, it's been, I've been following along on, on on Twitter and a lot of people are coming outta the woodwork on the ingredients. And not that, I mean, David's never shy that David's not trying to be like a whole food natural ingredient based brand, but like people, people are really coming outta the woodworks on this one cuz their flavor system just says natural and artificial flavor. Like that, that's all it says for the flavor system. And it was funny to see that, dude.
Well, you know what's funny? So actually going back to the, the Soylent point is like their billboards back in 2017 were like pro GMO. That's what I like. That's why I like them so much. They like, they completely own the artificial nature of it. Um, and at the time they were like, natural food is obviously good. And like, there's, there's no like qualms with that. But at the same time, when you think about like feeding tons and tons of people, like, I don't think over the, over, you know, the next 100 years we're going to, you know, exclusively produce natural food. I think we're going to have better and better natural ingredients. And that's how we end up feeding billions and billions of people over time.
Yeah, I just love how David's leaning into it. It's, and I also love the, like, I don't, I think most people know this, but maybe not that, like, David, like Peter's previous company was the opposite end of the spectrum as where, where David is. You know, RXBAR is all about whole food ingredients, 3 egg whites, you know, 10 cashews, whatever, whatever's on the, and then, and then he has this, which is all about maximizing calorie to protein ratio. And in, in order to do that, he's using artificial ingredients. But it's just, I just think it's so badass that he's been able to like take two, like, you know, same market, two completely different ends of the spectrum on how he is positioning these products. And like the ice cream is just such an exclamation point on that compared to what's out there right now. So I loved it. I think it's amazing. And I'm gonna try to get my hands on some. I slacked Keegan right when I saw it was sold out and, and asked, he's like, hey, I'll send you someone.
Give me on the list, dude. Give me on the list.
I'm like, no, no, I feel bad cuz I'm like, I'm not asking you just send me some, I'll totally buy it.
I literally just want to know when are you restocking so I can be ready to buy it, but I'll get you on the list for the restock. Michael, I want to dig into your background a little bit. You have a pretty impressive involvement with JAXXON's growth trajectory, but I want to just, before we get too deep into that, can you just gimme your background? Like, who are you in the context of digital marketing? How'd you get into this world and like kind of what ultimately got you into your role, which is GM of DTC at JAXXON?
Yeah, it's been honestly a pretty wild, unpredictable journey. Like I think most of us, but I would say what defined my career was essentially at any given point, I'm trying to figure out who was making the decision and become that person and just rinse and repeat. So I actually graduated chemical engineering, so I, my first job was R&D. I was trying to make memory foams and mattresses more comfortable, completely different from what I do today. Um, I felt like the project manager was always telling me what to do. So I was like, okay, how do I become a project manager? Went through the ringer, kind of went into product development and a lot of lean manufacturing stuff as a project manager, program manager. Then I was like, well, now just the category or product manager keeps telling me what to do. So how do I become product manager? So then jumped to Ruggable back in 2021 and I was kind of category management there. Felt like same thing where analytics was the one actually finding the opportunity and I just felt like I was executing it. So then I was like, okay, now I got to go to analytics. So for a little while as manager of analytics, I then felt like, well, I'm finding opportunity, but strategy gets to define where I'm actually looking for opportunities. So jumped to strategy at Ruggable. And then finally I was like, well, I'm doing strategy at Ruggable, I'm just making PowerPoints for the, you know, execs. So how do I figure out how to become an operator? So kind of made the move to JAXXON, really just saw like we had a crazy brand presence, shout out Bear, who just absolutely crushes it with partnerships and building like these authentic relationships. But then I saw the sales numbers and I was like, oh, there's a massive gap between the name recognition among like, I'm probably target demographic. So the name recognition between like my cohort and the actual sales dollars. So I was a little a little cocky and thought I could jump over and try to make the company what I thought it could be. So it's really just been a story of trying to take it from low, or not low, but like, let's say 8 figures and just a journey towards hitting like 9 figures, which I would say we're mostly successful on. I think the crazy part though is that like, you know, my background, analytics, project management, a little bit of data science, a little bit of strategy. Never hands-on performance marketing. And about 2 and a half years ago right now, um, just organizational gap resulted in me and my buddy Ricky, who leads our marketing effort, really having to make all the decisions. And I had no marketing experience. I had no idea. I didn't even know what CAC was. I didn't know what an MER was. And I was given a mid-eight-figure budget to try to grow the company. So, Like I mentioned earlier, I just really had to rely on The Operators podcast to try to crash course myself in being able to execute things like advertising budgeting was the main focus of mine, merchandising, just pricing strategy, all those aspects. Yeah, and honestly, it felt like learned a lot, learned from the mistakes, learned from the good parts, but it's been a wild ride. Love D2C.
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That's interesting. I think that's a cool way to That's a very interesting— I don't know if I've heard someone position it that way before, but I think it's very, it makes a lot of sense. You've let like, you want to be in the seat as the decision maker and you've let that guide your career. And that's, it sounds like that's kind of led you to this point and every step along the way was, how do I become the decision maker? And that's what, is that true? Would you say that's kind of been like the lens you've been viewing your career and like each step through?
Yeah, definitely. And I feel like you guys can empathize where right now I feel like I can make the decisions, I can make the big calls, but I'm spread over such a large surface area of just different areas that I don't actually feel like I'm making the big calls anymore, where it's really just trying to build up the team to be able to make the decisions that I would inside their shoes. So now that I finally feel like I've made it and I get to like look at myself as an operator, I'm like, Wow. The good old days, right?
Yeah. Well, and that's what I think, that's what a good leader does is they empower their team to make decisions as well. So not everything, you know, ends up with them being the bottleneck. And I also love your background. I think that's the cool part about D2C performance marketing. If you put 10 performance marketers in a room, heads of growth, CMOs, what have you, they'll all come from different backgrounds. There's no, there's not like a clear path to getting into this e-com performance marketing world, which I think is really fun because it just, it means there's such a diverse group of people that are in these like, you know, performance marketing roles now, which is exciting. And it just creates a lot of different opinions and types of people. And I think it's good for our industry as a whole that there's so much diversity and background of the people that get into these roles.
Of course. Head to head, if I can hire one person, I'm going to hire one with experience, but inside performance marketing. But it's just been interesting where some things that are abundantly obvious to me, like testing methodologies, how to run a robust testing program, how to do things with like a lot of statistical rigor, how to get creative with numbers to achieve set goals. They come naturally to me, but then some things that are just basic to others, like having an eye for creative. Or figuring out what a great partnership is, just completely foreign to me. So I think that a lot of the value I added where Jackson was a lot of people who are amazing at brand, amazing at creative, just really able to go out there and go out there with an entrepreneurial spirit, essentially just adding some level of quantitative rigor to it. And then it's just been kind of the best of both worlds since I joined.
What I want to get into Jackson and what you're focused on there in a second. But before we do that, You started in product, you started in like product as an engineer. How do you feel like that's helped you as you've moved into marketing roles? Like your origins, your base in product development and just understanding what goes into that, like how has that helped you or not in your current role?
Yeah, it's been interesting. I think that one big area product teaches you how to do is a lot of times you're launching something just without a lot of data and trying to make a decision when you just don't have, you don't have clear numbers to look at a lot of the times. You just have to make a call and you have to kind of both think through, you really have to think through the problem versus trying to calculate an answer. I think that's an interesting part about product. I think the other part was just an appreciation for the challenges of supply chain, for project management, how everything is always later than you expect. For a while at Jackson, was owning the demand planning aspect, and talk about an absolute mess when I was running it. But you really just learn how to appreciate the other side and the challenges they're going through, which I feel like helps you integrate the team slightly better.
I sometimes wish I had that background because I don't understand it, and I find myself at times in the past have found myself thinking, What do you mean we don't have, we don't have more knives? Just order more. It's like, it's not that easy, of course. So yeah, that's, that's cool that you have that perspective and you're able to like bridge the gap between different parts of the org because of that. So I have written down here in our notes that you have a core thesis that there is no such thing as optimized ad spend. It's all about risk tolerance and time horizon. Optimizing for all to— optimizing for results right now is a completely different exercise than optimizing for a 10-year timeline. So I, I definitely resonate with this, this note. You know, we're a highly considered product at HexClad and the ad spend that we're deploying today often doesn't really fully materialize until months or years down the road. So we're always trying to strike that balance of like, you know, capturing demand and generating demand at the same time. So a few questions on this. Can you walk me through how you actually think about ad spend as, as a risk management exercise? Like what does that framework look like in your day-to-day?
Yeah, so I think a lot of kind of what's helped me find success at Jackson is just these 90-minute commutes to OC where I'm just stuck in my head and just running these like thought experiments pretty much combined with listening to the experience of other people. So essentially the thought experiment that had helped me with this one was the guy named Brad Jacobs, a fascinating guy, founded $8 billion companies, not 8 billion, but 8 separate billion-dollar companies. Crazy entrepreneur. And he does this, he does this exercise where he tries to like accordion his mind back and forth to the really small picture and the really big picture and back to the really small picture. So then when we're talking about trying to optimize your advertising spend, like what is the optimal advertising spend? Well, I think if you accordion to the really small picture and if you're trying to optimize for contribution margin the next hour, what would probably all of us do, especially high LTV, long consideration, optimization process, we'd probably turn off all of our ad spend, hit SMS, and drop our prices 50% and do a massive discount. And that's probably what would optimize contribution margin for the next hour. If you go accordion back to the really, really big macro big picture and you're like, okay, how would I optimize contribution margin over the next 50 years? Well, assuming that we compound growth for the next, let's say, 50 years, this year is going to contribute a fraction of a fraction of a percent where probably what I would do is I would focus on learning. I would focus on getting as many insights, figuring out whether we had the right product and aggressively testing. And I wouldn't care at all about contribution margin, but this year it would just be a learning exercise where I would spend as much as my cash flow could allow. So then if you go back and forth, that just means that essentially on one hand, one extreme, you essentially have optimal ad spend is zero, and the other extreme is optimal ad spend is as much as my cash flow allows, which just makes the problem that we always discuss about trying to— how much incrementality, what are we trying to achieve? It's less so like an optimization problem and just a problem definition where it sounds kind of hooty-tooty, but it really turns into— it brings an aspect, an internal aspect of personal risk management. Where there are probably two ways I can improve contribution margin and the expected value. And the number one way is that I just get better at growth hacking. I just improve as an operator. Number two, a lot of times is just taking on more risk. And I think that like an intuitive example that we can all understand is that like, you know, when we're building a personal finance portfolio, you're buying some stocks and bonds. So you keep, you know, you keep some money in the savings account. You're not fully optimized to maximize your expected value. You're not trying to fully optimize to maximize your return. There's an aspect of risk management where I don't think the money in my savings account is compounding at the rate. I don't think it's getting the maximal value, but what I think is that given my risk tolerance, that's how much money I want to have on hand just in case everything hits the wall. There are like lotteries out there where, for example, if I want to maximize my expected value, I put all of my net worth into the lottery when it gets to a certain point and my expected value is positive, but we don't do that. It's just risk management. So then it just brings in the perspective of like, essentially, I feel like we don't ask ourselves as operators often enough, like, what are we trying to achieve? Are we really honestly trying to maximize our contribution margin for, let's say, a 3-year horizon? Even if that would require us essentially sacking a quarter, or is there a better balance where we are actually optimizing for, let's say, 80% for next quarter and maybe 20% for next year? Um, hopefully that resonates slightly, but I definitely feel like being high AOV brands, you guys can definitely understand that, like, feeling where I could just cut spend and make a little extra contribution margin today, but I know long term that would really bite me.
When you think about balancing short-term contribution margin, cash flow, and then where you want to invest that long-term, it feels to me like if I think about how that gets captured at Ridge, it's like in our forecast, right? Like our, our, our growth team is not allocating our ad dollars versus some form of CapEx or, you know, uh, hiring or, or, you know, retail expansion, right? Like the, the growth, the, the marketing team is not making that decision. That's coming from a much higher level. And where that should get captured is like next month we want to do $1 million at a 4x MER. It's like, that's all that, that's what the marketing team knows and is optimizing towards. And then it is like the group of executives, whoever that may be, to then allocate, hey, based on these results, based on this forecast, we will generate this cash and we're going to invest in these ways. Is that how you would describe it? And then how do you decide, like, well, like, what are some of the thought processes that you go through to then decide where are you allocating that capital so that you're maximizing the value over time?
Yeah, so I think that it kind of gets into the nitty-gritty methodology. I think on one thing where probably contrast is I hate budgets, I hate forecasts. I think that sometimes there are times where forecasting budgets have been useful, but more often than not, what they cause me to do is they cause me to anchor to a result. So one of the stories was back when last year Q2 tariffs hit, Obviously they tariffed, there was a period of time where they tariffed China, Europe, everything got tariffed, we all went through it. And a lot of our COGS started increasing and then we knew we were going to probably miss a gross margin on a budget on a forecast. But what we saw was that everyone was pulling back spend so far that we were seeing record low CACs. So essentially the equation was that like, oh, well, we have a really low CAC that's way below budget or really high or our lower gross margin, which is missing budget. I feel like that if we really fixated on the forecast, we wouldn't have done what we ultimately did, which is spend really aggressively into the low CAC and allow us to kind of like take a lot of market share, which it was probably the best period we've ever had. I think that it just causes us to anchor in that every day, what we do at Jackson is pretty much telling the team, Right now, given our risk tolerance, you need to be optimizing for contribution margin, or right now you need to be optimizing for some of the leading indicators and can talk about the leading indicators. I think we have a really interesting one, which is like a cost per email signup. Can go through the whole thought process of that, but that's really been the major unlock is just kind of disconnecting spend from your prior assumption from 2 months ago, from your reforecast, or just from anchoring to some number that you're trying to hit at all costs, whether or not there's a better way around that number or a way—
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I think, Connor, I agree with your point though. I think, I think like the, the, I think the forecast can, if it's set up the right way, be a guiding force on which moments in time you're doing like you're leaning more or less into the, the ends of the spectrum that Michael just talked about. Like this is, this is something that we had to figure out at Hexclad is, is for, it was hard for us. It's like, all right, our, our MRR goals, let's just say it's a 5X for the year, but we might run the business at a 3X in some of the slower months in the interest of hitting our revenue target. And then, and then coming in at a 5 or a 5.5X. And like, that's that balance of, all right, in the dog days of summer, we're less efficient. We're obviously not optimizing for contribution margin as much 'cause our MER is lower, but we have to keep spending. We have to keep We have to keep generating demand if we're going to hit those, those bigger revenue targets, those higher efficiency, those larger contribution margin moments. But that, that is guided by our forecast. Whereas, whereas before we like had that learning on, on like the seasonality of our business, it felt like every month we were having these conversations on like whether or not we should pull back or spend more or, or, or what have you. And now we, we have that baked in. But I think I agree with your note on like letting the forecast drive that, because if you can set expectations on a monthly more like month-to-month level, then your marketing team just has a really, they're really unlocked to make good decisions and not, you're not having these conversations with finance every week on like whether or not we should pull back or spend more. And yeah, I think that's like a very tough thing to figure out for your business. But Michael, what's, I want to ask you a question about like some tactics on this. Do you, do you have any examples? Because in reality, all brands are doing a little bit of both at, at any given time, right? Like we're running a summer sale right now that is helping us optimize for contribution margin right now, but we're also not pulling. It's like, we're not spinning all the way to that end of the spectrum. If we were, we would put all of our budget into Meta in search right now. We're still investing into YouTube, in linear TV, in podcasts. So. We are running an offer that's optimizing for contribution margin today, but we're also still investing in these upper funnel channels. Like we're—
can I say one thing quickly? Just because like I want to hit this point. Um, because I, I, I like the point about forecast. I made that one. I'm super down to reforecast all the time, which is maybe closer to the spectrum Michael's on where it's like, hey, let's not like, let's not be like too, too, um, you know, anchored to any one like specific number because I think that's extremely problematic too. But the forecast, there's all sorts of just like light constraints that a brand has to give themselves. And the forecast is one, 'cause Connor, you're saying it now, you guys are running a summer sale. If you were optimizing for contribution margin, frankly, like you would probably turn off ad spend or like a lot of it. And Michael made that point earlier. If you were purely like in the month of June, we need to maximize contribution margin. Uh, we're just going to reduce a lot of ad spend because you, I'm sure there are people that you're prospecting today who are not gonna convert until October or whatever, right? Like, you know, there's a ton of like latent, it might be the consideration might be period might be as short as it's gonna be during the summer sale, but there's still a consideration, uh, period and there's still people that are gonna convert over like a very long tail. Um, so one of the reasons you have to spend today is that your forecast says that you need to do like $400 million in December or whatever that's gonna do. Uh, So like, so all of those things are just, I would even say, um, yeah, so anyway, I'll leave it at that. But like, I feel like that kind of, it's not even a marketing thing at that point. That's why I say it's like an org-wide perspective of like, what does the business want to do from a contribution margin perspective, from a cash flow perspective, and like a future growth perspective. And all of those are, I think the forecast is a great way for it to get captured and then can provide constraints on like a day-to-day, week-to-week basis so that nobody's running around being like, I'm going to run at a 10x MER right now, blast the SMS list like Michael mentioned, and just generate a bunch of cash right now. And then, and then I'm going to worry about the September forecast when I get there. Because like ultimately that month will be smaller if you do that.
Oh, I was just going to say, I actually really like that because that is something we sometimes struggle to do is, um, the times forecasts have been useful is when we'd say that, hey, we're going to do something that's not going to feel great in the moment, whether it's like surge spend in October ahead of BFCM. That's when it's really been a cool— that's when it's been a useful tool. On one hand though, I feel like we've been talking about this but haven't shared our methodology, which I feel like is kind of the missing piece of the puzzle because we're not just really running around choosing random numbers. I think the missing piece of the puzzle is we have a main KPI, which is a cost per email signup, and just describing like why we see it as super valuable, shockingly valuable, and I don't think talk about it enough. Is essentially you have a spectrum of metrics. You got impressions, clicks, add to carts, checkout, order, LTV. And I would argue that the nuance of the spectrum is that one side, it's really easy to attribute value. So if you tell me how you got an order, I could tell you exactly how much revenue you got, but it would be really hard for me. It would be really hard for me to tell you how much it costs to get that order accurately, especially for a brand like ours. On the other side, if you tell me you got an impression, I could tell you easily how much it costed because it's pretty much immediate. Yet, but the challenge is that it's really hard to attribute, like, what value did you get out of that one impression, one incremental impression? Probably really hard. And it essentially goes so on and so forth. So you got impressions, then like I said, you got click, add to cart, checkout, order, LTV. The one interesting metric though is if you run on an email signup, it kind of breaks the trend where it's easier to attribute revenue to an email signup than a checkout or add to cart order, even though it's earlier in the funnel. And the reason is I can just take my Shopify list of emails. I can take my email list and I can tell you exactly how much value we got out of every single email. And the other advantage is that it's not immediate that I pay $1, get an impression and get an email, but it's much faster than the purchase cycle for a, it's much faster than a purchase cycle for a, let's say like, um, high AOV brand where it might pull forward the sales or the value, like the point where you, the marketing spend have an impact or by, let's say, like months. So then if you essentially, you view your business as these durable businesses, you view it as like a freemium model where my goal is to get an email subscriber. And based on a lot of analysis, I can roughly estimate the lifetime value of the email subscriber. Unlike CPG, the timing of that value is very different where if I get an email subscriber in October, I'm probably going to realize most of the value late November. Whereas if I'm getting an email subscriber on BFCM, I'm probably realizing most of the value on that day. You can do those kind of calculations and then almost run these durable brands as an LTV over CAC, where you just focus on that email subscribe as the main moment instead of an order. Um, it sounds— there are a lot of like pitfalls that you can fall in and we've had to trial and error essentially to figure out how to do this. But the really nice part about it is that it lets you find these pockets of moments. We always talk about it. Like, I feel like every brand feels like they underspend in the lead up to BFCM. And I feel like it's because they don't have this leading indicator that they can attribute future sales to at an earlier moment. But it's allowed us to find these random pockets where the buying intent might not be there, but the marketing spend and impressions is definitely there. And you're getting high-value leads is essentially the goal. So it's just, so essentially how our business runs is in a moment of like promotion, we're really hyper-focused on contribution margin, but let's say in March, nothing's really going on for the business. It's a slow season. Then we can hyper-focus on getting efficient email leads that we can roughly estimate the value for at an efficient cost. Does that make sense?
I love that. So that's, so that's an example of you're optimizing more on the the broader picture end of the spectrum versus the today's value. So you're, I just want to repeat this back to you and correct me if I'm miswording how you guys have this set up. So you're basically saying you are calculating like an average, on average revenue per email signup, correct? And not every email turns into that. Some are more, some are less, but on average, you know that every email you capture is going to lead to $75 of value or $150 $50 of value, whatever it is. So you're basically using that, you're basically, you have a cost per email target that is related to that average revenue per email. And that's what you're using to like optimize your ad spend. And, and as long as you're like in line with what you know, will, as long as the cost per email is in line with what the average email will back out to revenue-wise, you, you're kind of like keep continue spending or pulling back spend. Is that how you operationalize around that?
Yeah, and it's really just two aspects. The first, um, that helped me like kind of find this metric. The first one is looking over our shoulders at our friends in CPG and just how nice their LTV over CAC model felt watching it, where they can spend unprofitably short-term, but they can have a high confidence in the long-term benefit. And for a high decision cycle brand, like most of ours, um, that just didn't feel possible until we had this metric and we kind of shifted the focus to Finding when, finding a moment other than the moment of order that we could attribute spend to. The second one was actually listening to Ridge and you guys. So when I took over our marketing budgeting, we, I would listen to the podcast and our goal at the time, which is to win on contribution margin every day, probably led to significant underspend. And I was listening to Connor just talk about like how low of an MER they would run in the lead up to a sale. And how they would essentially rake in the contribution margin during their quarterly seasonal promotions. And I was like, maybe there's something there, but that feels kind of ridiculous. Why not win contribution margin every day? When we started mapping out the lifetime value though of these like email signups, we did see very, very clearly that if you're able to get a lot of email signups prior to a promotion, you're able to monetize them during the promotion. So even though it might really, really dent your contribution margin in, let's say, October, or for us, another one is like Father's Day, so it might dent April, you can act with some confidence that you will eventually make back the value. I think the challenge for us versus like a CPG brand is the timing of the value is really, is just completely unpredictable for us. So I can fairly confidently tell you how much an email subscriber will get us unless we kind of do something weird like dilute it. But I can tell you how much an email subscriber will get us over, let's say, a 1-year period. I cannot tell you how much it would be over a 30, 60, or 90-day period because it's pretty unpredictable when exactly they purchase. But the stability of the metric over a longer term just enables you to make these decisions and have confidence that like, well, I'm not getting as much value within 30 days as I expected, but I also understand that that's probably just noise and that over Overall, big picture, the stability, I'll make it probably back in 60 days and 70 days or so on and so forth.
I have two questions. One is if you are, um, like within Meta, are you optimizing for email signups or are you optimizing for purchases and then monitoring cost per email? And then two, I'm curious how that's changed or if it's changed, if you guys have diversified channels, because I would assume that like an email captured from a Snapchat is just going to be worth less than an email captured from like a super high-intent meta campaign, something like that. So as, as like the, the email, the quality of the email changes by channel or by person or whatever else, how do you guys factor those things in?
Yeah, good question. I feel like that's kind of throwing people off where definitely would never optimize for anything but conversion unless you're doing some like reach campaigns, which is a completely separate topic. So this is more just a canary in the coal mine where we're not trying to get the canary to squawk. We're using it as leading indicator of how our spend is doing, even though we're not necessarily optimizing for it. We do sometimes optimize creative for it though, which is interesting. The other part about channel is also something I've talked about a lot and then tried to really dig into data-wise. The nice part is that email subscribe is kind of an equalizer of intent. So if I get a lot of really low quality traffic, they're probably not subscribing to the email as much. It kind of filters through the really bad traffic. We can roughly estimate, let's say we get the number of emails in, in Northbeam from a certain channel compared to the revenue they generate, and then do a gut check to see the attributed revenue over the number of emails kind of makes sense and checks out. And we do see some fluctuation, but by and large, I would say that it does help equalize essentially the buying intent. Whether someone signs up because they saw a Facebook ad or whether someone signs up because they saw a TikTok ad kind of evens out.
So you're just blending it. You're, you're really looking at a blended cost per email signup. You're not getting too granular with like, here's our, here's all of our email signups with a meta UTM. Here's Snapchat, TikTok, and like you, you have different models. You're basically kind of going one level up and saying, all right, blended cost per email signup, blended average lifetime value of an email. And that's, that's kind of like what you're using for the model.
Yeah, and I think that it's not that like we wouldn't be better off by getting very granular, but we're both a small team and I think that like when we get really into the day trading aspect of trying to like make every channel perfect, sometimes we, sometimes we as a company definitely lose sight of the bigger picture and really focus that focusing on the blended metric really just keeps us honest, keeps us simple where the additional granularity in theory would help, but I feel like in practice ends up being kind of a distraction from the main goal.
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Can you speak to how you're, how this is integrated into like a weekly workflow? So on a weekly basis, are you like meeting with your paid team and like, do you guys have a cost per email signup goal? And then on a weekly basis, are you like monitoring where you're at relative to that goal? And then let's say your goal is $2 cost per email signup. Are you then saying, hey, we're at $1.75, let's spend more because we're not at, we're below target and we have some, some like volume upside here, or can you like maybe that's just an example, but could you just walk us through how you're actually like in a day-to-day, week-to-week basis, you're actually like making decisions and interacting with the team and like just overall like operationalizing around this KPI?
Yeah, I'm about to go totally off the rails and then bring it back, but I just love telling the story and I think it's applicable. So in Japan they have these chicken genderers. Oh, perfect. Yeah, it's really out there, but I love telling this on any excuse to tell. They have these chicken genderers And what's fascinating about them is that in order to farm chickens, farm eggs, you need to be able to tell the gender of the little chick hatchling. And they can do it with about 95 to 99% accuracy within 1 to 3 seconds. The crazy part about it though is if you ask them how they're able to tell, there's no physical sign. They just say like, I know. And even crazier is even with all this crazy AI, all the technology, Machine learning still can't do it as well as these people. And the reason they're able to do it so well is because they see probably millions of chickens a year, and just by trial and error, they pick up on the trends, and it's essentially building a feedback loop for themselves that they just hyper-optimize for to a point that not even this fancy AI tool could do. I feel a lot about— that's kind of how I feel about performance, where not even a weekly basis, but I think my team's going to kill me, but I used to do it myself every single day. I would go through the numbers and write a couple bullet points about, you know, the trends I was seeing, what our contribution margin was, what cost per lead was, cost per add to cart, trying to put together and then trying to form it into a cohesive narrative where if let's say we see great cost per add to cart, great cost per lead, contribution margin is lagging, but we're ahead of a major seasonal moment, the narrative would essentially be that like, hey, we're building a funnel for this large moment. We're being efficient with our spend, even though we're not realizing the value. Whereas let's say, um, it's our shipping cutoff and we see a huge spike in conversion rate, uh, cost per lead is super competitive. It's more like, okay, let's lay low with spend. We're realizing value. So I think that's kind of the nuance where I talk about it a lot with Austin from Northbeam, but being able to look at the number every day, but not react to it, I feel like is one of the hardest parts of the job because it's terrifying sometimes that like when you look at the big picture for the year, it's very clear to say like, oh, I kind of overspent here, I underspent here, but in the moment it is kind of nerve-wracking and it's really hard to see when you see your cost per lead jump down. It's like, do I spend into it? Well, maybe the second day you see it, well, you do spend to it. So I think that's kind of not even a weekly basis, but I think, uh, us personally, we like doing it on a daily basis and then just to kind of step back from the day-to-day noise. Probably once a week I have to put together a deck kind of on the performance and that kind of consolidates my thinking, lets me take a step back.
So it's daily monitoring and then would you say weekly making of moves? Hey, we're— it's if like, hey, cost per lead's down a bunch today, but it's only a single day. We're not going to go and action this because it might shoot back up tomorrow and like there's so many daily fluctuations as we all know. But if you zoom out over the course of the week and it's still looking good, then that's when you're going to go and say, all right, we can go and spend more because over the course of a week, our cost per lead's below, it's trending down or something like that. Is that accurate?
Yeah, I would say a very slight twist where kind of at the point in the organization, thanks to just hiring some killer people inside our paid marketing team, No longer really giving them numbers on how much to spend, more so just giving them guidance of like, this week we need to focus on cost per lead, or this week we need to focus on contribution margin, and just trying to build that feedback loop where they're making the decision. We have not really an ad spend budget, to be honest. We have targets we're trying to hit, and their goal is to allocate the ad spend to hit the target. We actually didn't have a finance team for a very long time, which probably is why we had the permission to be able to do these kind of of weird stuff, but their goal is essentially to hit some kind of target. And then kind of what I see my goal in the organization is, is to tell them like which target to focus on. And when they make these decisions, I like separating the person who makes the decision versus the person who is like giving the feedback, because I feel like too often when you're making the decisions yourself, you can tunnel vision into seeing the numbers one way, because you really want something to work and it's just human. They've done the double-blind testing to show that like it's just a statistical thing that if you're making the decision, you're going to be biased towards it. I think that is why like they're making the ad spend decisions independently of kind of like what I'm doing, which is kind of giving feedback and reporting and posting every day on like what we're seeing, what the narrative is, whether what leading indicators are clean, which ones are messy. Sometimes everything just works out where you have a great cost per lead, great cost per checkout, great cost per add to cart, bad contribution margin, but you're ahead of a seasonal moment. And those are the moments where we've done a really good job of, as an organization, of taking advantage of and really raking it in in those moments. More often than not though, it's nuanced where 2 out of 3 metrics might be great or 1 out of 3 of the metrics might be great, but that one is amazing. And then just trying to, just trying to be able to iteratively figure out whether you're correct. The other part about like just posting every day is that it gives you a lot of times up at bat. Like I talked about the chicken gendering, a lot of it is just trying to build some kind of instinct. And I think the best way to build the instinct is to write down what you actually think and then be able to see if you were correct, like to make these kind of predictions. We have a public one where my team does it, and I think it's been a great tool to see like, if we made a mistake, what were we saying in the lead-up? That's been a really good retroactive exercise for us. There have been times where we underspent, there have been times where we overspent, and it's kind of funny reading these old posts because you can just somewhat feel the delusion where I'm like, ah, like it's going to happen. We just really need to believe like, We just need to run aggressively for one more week and we'll be fine. And then the week after it's like, well, that, that didn't work out, so let's learn from that. But I think that it helps it.
Sorry to interrupt. You, you've mentioned this post a few times and I just wanna like clear up what that is. Are you saying like every week or every month or every two weeks, are you doing some sort of, like, are you writing a memo like on, on the same day every week or every two weeks or something? And that's like the post and that like is looking at the data and like you talked about deep diving in the data, creating a narrative on like what's happening in the business. Is that what you're talking about? This post that you've mentioned a few times, like what is that?
Oh, it's a Slack post. So we have a Slack channel with all the leaders in the company and posting it every single day actually. So we'll post the numbers on a daily basis, week over week, year over year, and describe what we think is happening. Describe like what, how, whether it aligns with our plans, The thing is that it's not about trying to micromanage the spend budgets every single day. It's more about just trying to— it's more about just making sure you're really rigorous and looking at the numbers every single day. So definitely should not be reacting. And I think that's really the hard part though, is just sometimes posting the numbers saying like, hey, there are some signs that this might be happening, but let's sit back and wait and see. Make, see, get a little more evidence. That's really just been the challenge, but also I think it's been what's developed us as performance marketers and also me, myself, I had no experience in this before. I think that being able to have that retrospective, retroactive look on when I succeeded, when I failed, what I was saying when I succeeded, what I was saying when I failed has really just developed me and just my personal skillset in that respect.
Can I ask a very, to jump back quickly to a very tactical question around optimizing for, cost per email, what is the variance in the window of time in which you realize value from those emails? You kind of alluded to it earlier, but I'd love just like a ballpark of like, is it really, is it super short in November and is it really long in September? Uh, 'cause I'm just curious, like over what timeframe?
Yeah.
'Cause that ultimately comes down to like that risk tolerance and budget allocation. It's like, how much are you willing to invest today and how long are you willing to wait? To capture revenue from that email? So just like the, the, the, the band of oscillation would be interesting to hear about.
Yeah, so you definitely get a spike on the first day. There are some impulse purchasers out there, and I would say for the first 7 days, it's a pretty steep curve. Unlike kind of a lifetime value for like maybe a CPG brand though, it doesn't really level out where you can continue to realize value from emails from a year ago or 2 years ago. It's kind of crazy how, how activated a lot of those cohorts are where I'm like, who signed up for our email list and not interacting with the brand for 2 years and suddenly decides to purchase? But I feel like as marketers, it's hard for us to believe, but as consumers, like, I got a HexClad, I love it, but my purchase journey for that product was essentially, I heard about it on the podcast actually. I saw a ton of ads and then after seeing all the ads, I saw it everywhere organically where every single influencer chef was using a HexClad pan and that's kind of really what sold it on me. But then I waited probably 2 months and then I happened to get a— more than 2 months, probably 9 months. And then someone gave me a Visa gift card and I didn't know what to buy. It was like $300. I was like, oh, this is perfect. I'll get a HexClad pen. The problem though is that like inside your Northbeam model, inside your, um, inside your post-purchase attribution, no data point is really telling that story of like how I bought where you wouldn't be able to say what was the CAC to acquire me. It's just such a vague number. But then it's like one of those sayings that like every model is wrong, but some are useful. So then it's just trying to find something that is a representation of reality that can take this whole story and distill it into something actionable, knowing that like the metric isn't correct, but it has value because it tells you something like the law of averages, where eventually you'll get the value from that.
In this case, I think we use a first-touch attribution model. I think it's a podcast purchase, Connor.
Yeah, I'm trying to remember what I put on it, but I don't think that you had podcasts as an option. Well, I think it was podcast generic, but not podcast operators.
We need marketing operators specifically in the post-purchase survey.
We have a write-in, so I'm going to go check out the write-ins after this and see how many are attributing to marketing operators. That's really, that's, do you, what's like the average? Do you know the average? Like, If you were to blend it out over the course of a year and you were to say, hey, I want to know what's the average time from email signup to order? Like, do you know what that number is? Or is it so variable that that's not even that? And is it so seasonal and variable that that's not even that helpful?
I would also bet, you know, like Michael, you know, just from a cash flow perspective, there has to be some breaking point for the time in which you're willing to wait. Right? Like even if, even if you found out like every email you, you acquire today, 20% of revenue comes 2.5 years in the future. It's like you just can't invest if that can't be your break-even point. Just 2.5 years in the future if you're gonna spend a dollar today. So kind of to Connor's point, is there an average? And then, and then how have you thought about the, the tolerance of time?
To the question of like, is there an average? It's hard to say because like I said, the lifetime value, the lifetime value of an email subscriber continues to appreciate over multiple years. So then what is the lifetime value? Well, it's probably how long the person lives, right? So I think that makes it a little bit hard to find exact timing. I know that we realize a disproportionately large amount of value within, let's say, like 60 to 90 days potentially. And then past there, it's a very slow but actually surprisingly steady gain. In terms of like the risk or in terms of like the cash flow aspect, I think that loops it back to the first point, which is just this is just a risk management exercise, right? Where at some point you need to draw a line in the sand. And it makes sense a lot where things happen that are unexpected. There are known unknowns and unknown unknowns, and unknown unknown might be like tariffs. Unknown unknown might be a quality issue. There's always something that might disrupt your ability to get value from these email subscribers, so you need to hedge against it. I think you just build in a margin of safety where you say you build in a large enough margin of safety where even if everything goes wrong, over a reasonable timeframe, you'd be able to monetize it. And let's say hypothetically you make the call in January and you realize that like, hey, we didn't, we weren't able to monetize this segment as expected. And maybe for a couple days, if not like weeks, we spent unprofitably. Then you just bring in the feedback loop and then you just really try to learn from that. And you better understand next time you're in those shoes, like this is what the reality is versus just the basic model, right? Because I think, yeah.
The other thing that I think is interesting in this conversation is like, as your business grows, your risk tolerance can obviously go up or like the, um, the, the break-even point for which you want to capture value from a lead or a customer, if you're doing like a more traditional LTV to CAC model can go down. Right. And it's like, you know, Hims, I haven't seen this talked about in the timeline in a while, but Hims runs at a 20% AMER for every dollar they generate in new customer revenue. They spend $5 on advertising. It's like the only reason they can do that is because they've been doing it now for 9 years and they have millions of customers. So they have a ton of returning customer revenue, so they can be way far out on the risk curve for acquiring that next, next customer in a way that like no startup today would be able to unless you just wanted to like light money on fire forever. So as Jackson's gotten bigger, I would also just assume if you want to continue growing and prioritize growth, you can get even more aggressive with your cost per email and the payback in which you expect to get a return on that. The payback period.
Yeah. And I think that's like, we all brands are talking about the moats that their brand has. I feel like that experience one is a surprisingly big moat where I honestly, I look at you guys at Ridge a lot for it, where some of the decisions you make, I'm like, I know it's more optimal. I know that we should be doing that. And honestly, I just, I don't have it in me at this point to do it, but it's something that, you know, you I'm sure you guys had the same experience where gradually, gradually through trial and error and seeing the successes, you're able to act more aggressively when you see an opportunity. I think that's something that like every brand kind of has to do within themselves where, um, we could describe all of our playbooks in exact detail, exact numbers, and someone else still wouldn't be able to copy it just because they would probably blink when it got really, really tough. And I think that's the hard part is that like these kind of decisions when you take on risk, it gets tough. And even right now in the organization, I think that's something that like we're internally trying to do is go from a mindset we used to be try to win every day, then every— we were at win every week. I feel like right now we've reached the level of we're trying to win every month. But I think that ultimately what's going to grow the brand is being able to sacrifice entire months, being able to sacrifice October, being able to sacrifice April, being able to sacrifice I don't know, July, in order to drive future value for the quarters and then shifting the mindset of being able to optimize every quarter. Maybe someday we're even thinking about sacking entire quarters to optimize a whole year. Not sure we get to that point, but it's something that like we can all describe, but in the moment it's just hard to execute, right?
I want to— you're talking, you're kind of, you're kind of dancing around a little bit. So I want to ask you some questions on this. I understand you have a decision quality framework, a 2x2 of good and bad decisions. Crossed with good and bad outcomes, and that this is what guides a lot of the decisions that you are making on a day-to-day basis. So can you just like explain what this framework is first, and then I have a few follow-up questions?
Yeah. I was going down a rabbit hole reading a lot about probability and just two really good books, highly recommend. Number one is Thinking in Bets, was written by a professional poker player. Number two is just Michael Malvesan. He writes a lot about, he's a big investor, writes a lot about the impact of luck. As an aside, one of his really interesting points about like, you can tell whether something takes a lot of skill or a lot of luck by asking, can you intentionally fail? Where I could not intentionally fail a coin toss, I cannot, but I could intentionally fail, let's say, like launching a new product, probably. They did a study where they took a bunch of hedge fund analysts, had them choose 10 stocks that they thought would intentionally fail., and 73% of them still beat the S&P. So it's just like a weird little aside about the impact of luck. But essentially what the framework is, and it's just another interesting thought exercise, is that we all know that luck has an impact on our decisions. We can all talk about really easily what are some good decisions we've made that led to good outcomes. We can all talk about what are some bad decisions we made that led to bad outcomes. I think the really hard one that I spent a lot of time thinking about personally is, can you guys list a good decision that led to a really bad outcome, but that if you were given the chance to make that decision again, you would make that kind of decision again? And can you do the opposite, which is what is a bad decision you made that led to a good outcome, but if you could go back in time, you would never do it again? It's something that I kind of puzzled over a surprising amount.
What's an example of that? Like, what's a, what's a What's a bad decision that led to a good outcome and what's a good decision? Because I would, I could see the, the, the, um, the contrarian here would say, well, how could it be a bad decision if it drove a good outcome? Doesn't that by default mean it's a good decision? But I want to hear like, what's an example of, of bad decision that led to a good outcome and good decision that led to a bad outcome per the, the framework you just mentioned?
Yeah, so I think bad decision that led to a good outcome, um, really hiring for me. So. Probably back in 2020, 2023, we were hiring a bunch of media buyers and we found some great candidates, really, really loved one candidate. And as soon as I saw us moving in a different direction, within 24 hours, I wasn't hiring at the time, I just made up a role, got it approved and had him an offer. Had no idea what he would do, hadn't no organizational framework, had no list of responsibilities, just had a salary and an offer letter. And it turned out that worked out really, really great where he's a great culture fit specifically for the organization and within my team. Shout out to Joe. But in hindsight, it was a completely irresponsible decision that could have blown up in my face, like hiring someone without any idea what they would do, without any buy-in from the organization. Probably would not do that again.
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So you're calling that a, you're calling that a bad decision because you weren't really, you weren't really prepared. Like you didn't have the, the roles and responsibilities outlined. You didn't have a 30-60-90 plan. You just in your head were like, I think we need media buyers. I'm just gonna like make this happen and hire this person. But you weren't really ready for it. So you're saying that's a bad decision, but they're saying over time this person like figured out their role alongside you and like they're providing a ton of value now. So like it led to a good outcome. Is that the, Is that what you're saying?
Yeah, even simpler. I think that if I made this decision, if I made this decision 10 times in a row, it would probably work out 2 times. And that I was essentially saved by the bell where it happened the one time I made this decision, it worked out. The flip side is good decision, bad outcome. Like I said, I did a lot of demand planning and we would make investments into like a new category, a new product, and we would make a bet on it where sometimes we're just trying to grow existing products that existing categories that were a small percentage of revenue. We predicted we could 2x the product and we had the appropriate inventory buy for that. In reality, it like 3 to 5x, which sounds great, but it just meant that we sold out within like 30 days. It sounds like, um, it's signing that ridge with all of your suitcases, but I wouldn't— I need to keep expressing to the team where that's a situation where it's a— it was a bad outcome, us not having a key new hero in stock. But on the flip side, to go into demand planning and say we can 5x any category is ultimately just going to lead to like a disaster on your inventory list. So even though that was a bad outcome in that one instance, we shouldn't learn to suddenly aggressive— hyper-aggressively forecast every product. That would just, 2 out of 10 times we'll end up 5x-ing something and those 2 out of 10 times we'll run out of inventory. But by and large, if we have to keep making the decision, we should keep making that same decision.
I don't have a good like example off the top of my head. I, I love both the ones that you just gave. Um, what it does remind me of, and it's ironically, 'cause I also read Thinking in Bets a couple years ago, but like de— I have had the conversation with my team where we need to decouple results from the quality of the work that we're doing and trying to make that distinction a little bit more. There was a period in, of time in 2024 where, you know, the business was doing fine. I felt like we were doing great work and the business was doing fine. And what I felt would have been the wrong decision to make at the time was to like, you know, all of a sudden change our processes, lose confidence in our internal strategy, our internal processes, our team, et cetera, and say, and instead say, hey, I'm going to, I think I'm going to decouple the results from the work here a little bit. I think we were doing extremely quality work. I think we're making extremely high quality decisions. Let's continue down this path. And ultimately it's a, it's a matter of luck to some degree where at some point, whether it's, you know, the Meta algorithm or consumer confidence or whatever else, these things that are out of our control will turn back in our favor and we will begin benefiting again from, uh, the, the quality of the work that we're putting in. So I do, I do like that line of thinking. I think it's an important sort of concept for people to consider.
I think a lot of it's like the decision versus the execution. Like you have to decouple that too, because I think a lot of times you actually, it's not the, it's not the decision that was a bad decision. If the outcome wasn't great, it's often the decision might have been good, but the execution didn't drive. So I think like we've made a few influencer bets that I like, we did this Hailey Bieber activation. I mean, this was years ago. This is probably 3 years ago at this time. And I just don't think like, I don't, now I think in that situation, maybe it wasn't, I think it actually was a good decision. I don't think we activated, executed the right way because I think What we ended up doing was we basically sponsored this, this YouTube series that Hailey was doing. We were integrated in the show and what, so the, I think the decision to activate with her was a good one, but the way we executed in that integration was bad because we ended up just like integrating in a show that had a bunch of like, you know, 17-year-old girls kind of gushing over Hailey Bieber. In reality, what we should have done is led with like a paid deal and had the organic integration be part of the deal. So I think that's a, that's an example. Like, I don't think the decision was necessarily bad. I just think the way that we decided to activate with her was probably not the right way for us to extract value. And, and we see this all the time. It's like, hey, we have a new offer idea on paper, and we even have some data points that suggest it, it worked really well in some aspects, but we also have other data points suggesting that there was parts of the offer that were clunky and didn't work as well. So it's like, good decision, execution wasn't quite there. I think that's important because otherwise you end up like telling yourself you made the wrong decision when in reality it's like you might just need to iterate on the execution of that decision. And then that's the unlock to, because it's like decision, execution, outcome. So like that middle point though, I think can sometimes get glossed over. It's like, oh, you shouldn't have done that partnership. Oh, you shouldn't have done that offer. Oh, you shouldn't have done that ad. Well, it's like, no, not necessarily. Maybe we should have just done that partnership, that offer, that ad a little differently than we did., and that would've given us the outcome. So it's, I think it's interesting to hear this like decoupling of the two from you, Michael, because I think it can very easily get connected to a bad decision when it might not have been.
So was that a variety of company sizes? Was that like $5 billion? Was that $100 million? Was that, I don't know, almost half a billion? Um, just a variety of different company sizes and I think as like leaders in the company, part of the challenge is as you scale, suddenly people start going for the 100% probability of a 15% return projects where they're afraid of being wrong and they're looking essentially at their accuracy, but trying to shift them to focus on impact versus accuracy, where a 50% chance at a 3x return, if you keep making that bet over and over again, will beat out 100% probability at 15%. Percent return long term, but then trying to give your permission, trying to give your team essentially the permission to fail. I try to set the expectation that like, I told our team that like, hey, if you're not failing 20 to 30% of the time, you're not taking big enough risks where you're expected to do things that do not work out and you're not going to be held accountable for it. If in the big picture, these things are having that net benefit. So trying to have that fine line of you still want to hold people accountable to results, but whether you should hold people accountable to every decision, I feel like it's just going to drive that like small incremental bet constantly versus trying to give them the permission to take bigger risks, but also have an asymmetric outcome essentially. That's been something that we've been working on a lot within the brand and just trying to encourage, you know, these senior manager, director level people to make these big calls that might blow up in their face, but also have the comfort to do it and also the expectation that they should be doing that.
Do you have any tips for, um, teams that want to be testing at a higher velocity? Like with this in mind, I, I, and this is a super important point. I've talked about it on the podcast a bunch. Um, I heard it from someone at Elkatterton years ago where they were like, we decided we have no ability to determine what's going to work or what's not. So our main KPI is just volume of testing. And it sounds like you're maybe more in that camp, albeit like thinking a little bit about expected value. So for people who want to be testing at a higher velocity, what are some of the things that you guys have implemented that could be helpful?
Yeah, I love that question because it's one of my favorite topics and I think it comes from background in like R&D where I was running essentially the testing program for these like different memory phones. So I think that let's say like e-com or holdout testing, I'm going to focus a little more on e-com A/B testing. I think the standard is to run things to 95% confidence. And that always struck me as odd because as a performance marketer, how often do you actually make a decision with 95% confidence? Pretty rarely. Like, we're pretty used to making decisions with 60, 70, even 51% confidence. We just make the call. So then ultimately what happens though is that just because you have the ability to do something with 95% confidence on ecom AP tests, the cost is you just slow down your test velocity. Where let's say you go from 95% confidence to 80% confidence, that would literally 2 to 3x your test output. And sure, you would have more leakage, you'd have more false positive, more false negatives, but the resulting net benefit would be 1.9x for your total testing program. Um, there are certain like conditions that you need to meet in order for this to work out. Where essentially the quality of your test has to, the quality of the test in the backlog have to be the same as the current test you're running. So if I think I got 5 winners and 10 losers in my backlog, I should spend more time on the winners to make sure that I realize a full benefit from them. You need to have the development resources, but essentially it's like the cost of the idea of like a cost of quality. I think is eating into a lot of e-com A/B brand— is eating into the testing program for a lot of e-com A/B tests because we're essentially just thinning the velocity just so that we can have 100% confidence in something that like we definitely do not really fully need 100% confidence in. Holdout testing is another one of my favorite ones where I would really love if a SaaS did this. I did the math recently and let's say for like a $30 to $50 million brand, They probably need to be running like a 9+ month holdout test in order for statistical power. That's just how the math works out. Even if you hit statistical significance before then, it's probably just noise. That makes it challenging. And then it's like, well, I'm like a $50 million brand. I probably should be doing some kind of holdout testing to get results. Like, what can I do? Same idea of a spectrum of KPIs where you get essentially clicks, add to cart sessions, checkout sessions, orders, revenue. Just shifting the holdout test, and all of these are in Shopify, instead of looking at your incremental orders where you're never going to hit statsig within a reasonable amount of time, why not look at the incremental checkout sessions or the incremental add-to-cart sessions or the incremental clicks? That's really been an enabler for us for helping test these small channels where like I look at the revenue and just complete noise. I look at the graph and one person bought a solid gold chain and just completely made the test irrelevant. But for example, if you could tell me that Google is getting me a lot of incremental traffic, that's somewhat actionable. And if you could tell me that the add-to-cart rate for that traffic is comparable to the add-to-cart rate for just normal traffic, then that's also actionable. And then essentially the benefit of this is that suddenly you're enabling yourself at, regardless of scale, essentially to get actionable findings out of these holdout tests. Of course, I would rather have the full, like, um, gross profit contribution margin impact with a lot of granularity, but sometimes just the time isn't worth it. The time to actually do the proper test is just ultimately not worth it. And by the time you actually finish it, the insight has probably shifted. Um, so like I said, would really love if someone started making a SaaS platform to do holdout tests on different metrics because right now I'm doing it manually.
That's a great example of like the art of triangulation, right? It's, it's not perfect, but it's, it's data-driven and it's, it's going to allow you to make most of the right decisions most of the time, as long as, as long as you're confident in what those like soft metrics are that, that you're, um, comping to, right? But if you know Meta's like your number one revenue driver and you're confident in that and you have a solid cost per add to cart, well, yeah, if then you go launch Snapchat and you're getting a dollar better cost per add to cart, like I'd be confident scaling Snapchat based on that.
Yeah, and I think like even something like YouTube, um, not a great click-through channel, but if you can tell me that we're getting a lot of incremental traffic from YouTube, I can probably do the back math in the back of my head of like, okay, what if the revenue per click is about the same as Meta? Is that like a good channel for us to be on? Um, it just unlocks a lot of different aspects, but then, you know, we don't want to be spending $100,000 per day on YouTube in order to get statistical significance. Within like 30 days. So then you just end up having to kind of find these roundabout ways to kind of like massage the numbers into a way that you can form a narrative where the goal isn't to publish your results in an R&D paper. The goal is like, okay, I've seen enough that I'm willing to make this bet because I think that disproportionately it will pay off.